# Browsing by Subject "Wireless networks"

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Item Bandwidth and power efficient wireless spectrum sensing networks(2011-05) Kim, Jaeweon; Andrews, Jeffrey G.; Vishwanath, Sriram; Arapostathis, Aristotle; Vikalo, Haris; Barr, Ronald E.Show more Opportunistic spectrum reuse is a promising solution to the two main causes of spectrum scarcity: most of the radio frequency (RF) bands are allocated by static licensing, and many of them are underutilized. Frequency spectrum can be more efficiently utilized by allowing communication systems to find out unoccupied spectrum and to use it harmlessly to the licensed users. Reliable sensing of these spectral opportunities is perhaps the most essential element of this technology. Despite significant work on spectrum sensing, further performance improvement is needed to approach its full potential. In this dissertation, wireless spectrum sensing networks (WSSNs) are investigated for reliable detection of the primary (licensed) users, that enables efficient spectrum utilization and minimal power consumption in communications. Reliable spectrum sensing is studied in depth in two parts: a single sensor algorithm and then cooperative sensing are proposed based on a spectral covariance sensing (SCS). The first novel contribution uses different statistical correlations of the received signal and noise in the frequency domain. This detector is analyzed theoretically and verified through realistic simulations using actual digital television signals captured in the US. The proposed SCS detector achieves significant improvement over the existing solutions in terms of sensitivity and also robustness to noise uncertainty. Second, SCS is extended to a distributed WSSN architecture to allow cooperation between 2 or more sensors. Theoretical limits of cooperative white space sensing under correlated shadowing are investigated. We analyze the probability of a false alarm when each node in the WSSN detects the white space using the SCS detection and the base station combines individual results to make the final decision. The detection performance compared with that of the cooperative energy detector is improved and fewer sensor nodes are needed to achieve the same sensitivity. Third, we propose a low power source coding and modulation scheme for power efficient communication between the sensor nodes in WSSN. Complete analysis shows that the proposed scheme not only minimizes total power consumption in the network but also improves bit error rate (BER).Show more Item Congestion control and routing over challenged networks(2011-12) Ryu, Jung Ho; Shakkottai, Sanjay; de Veciana, Gustavo; Vishwanath, Sriram; Julien, Christine; Hasenbein, JohnShow more This dissertation is a study on the design and analysis of novel, optimal routing and rate control algorithms in wireless, mobile communication networks. Congestion control and routing algorithms upto now have been designed and optimized for wired or wireless mesh networks. In those networks, optimal algorithms (optimal in the sense that either the throughput is maximized or delay is minimized, or the network operation cost is minimized) can be engineered based on the classic time scale decomposition assumption that the dynamics of the network are either fast enough so that these algorithms essentially see the average or slow enough that any changes can be tracked to allow the algorithms to adapt over time. However, as technological advancements enable integration of ever more mobile nodes into communication networks, any rate control or routing algorithms based, for example, on averaging out the capacity of the wireless mobile link or tracking the instantaneous capacity will perform poorly. The common element in our solution to engineering efficient routing and rate control algorithms for mobile wireless networks is to make the wireless mobile links seem as if they are wired or wireless links to all but few nodes that directly see the mobile links (either the mobiles or nodes that can transmit to or receive from the mobiles) through an appropriate use of queuing structures at these selected nodes. This approach allows us to design end-to-end rate control or routing algorithms for wireless mobile networks so that neither averaging nor instantaneous tracking is necessary, as we have done in the following three networks. A network where we can easily demonstrate the poor performance of a rate control algorithm based on either averaging or tracking is a simple wireless downlink network where a mobile node moves but stays within the coverage cell of a single base station. In such a scenario, the time scale of the variations of the quality of the wireless channel between the mobile user and the base station can be such that the TCP-like congestion control algorithm at the source can not track the variation and is therefore unable to adjust the instantaneous coding rate at which the data stream can be encoded, i.e., the channel variation time scale is matched to the TCP round trip time scale. On the other hand, setting the coding rate for the average case will still result in low throughput due to the high sensitivity of the TCP rate control algorithm to packet loss and the fact that below average channel conditions occur frequently. In this dissertation, we will propose modifications to the TCP congestion control algorithm for this simple wireless mobile downlink network that will improve the throughput without the need for any tracking of the wireless channel. Intermittently connected network (ICN) is another network where the classic assumption of time scale decomposition is no longer relevant. An intermittently connected network is composed of multiple clusters of nodes that are geographically separated. Each cluster is connected wirelessly internally, but inter-cluster communication between two nodes in different clusters must rely on mobile carrier nodes to transport data between clusters. For instance, a mobile would make contact with a cluster and pick up data from that cluster, then move to a different cluster and drop off data into the second cluster. On contact, a large amount of data can be transferred between a cluster and a mobile, but the time duration between successive mobile-cluster contacts can be relatively long. In this network, an inter-cluster rate controller based on instantaneously tracking the mobile-cluster contacts can lead to under utilization of the network resources; if it is based on using long term average achievable rate of the mobile-cluster contacts, this can lead to large buffer requirements within the clusters. We will design and analyze throughput optimal routing and rate control algorithm for ICNs with minimum delay based on a back-pressure algorithm that is neither based on averaging out or tracking the contacts. The last type of network we study is networks with stationary nodes that are far apart from each other that rely on mobile nodes to communicate with each other. Each mobile transport node can be on one of several fixed routes, and these mobiles drop off or pick up data to and from the stationaries that are on that route. Each route has an associated cost that much be paid by the mobiles to be on (a longer route would have larger cost since it would require the mobile to expend more fuel) and stationaries pay different costs to have a packet picked up by the mobiles on different routes. The challenge in this type of network is to design a distributed route selection algorithm for the mobiles and for the stationaries to stabilize the network and minimize the total network operation cost. The sum cost minimization algorithm based on average source rates and mobility movement pattern would require global knowledge of the rates and movement pattern available at all stationaries and mobiles, rendering such algorithm centralized and weak in the presence of network disruptions. Algorithms based on instantaneous contact, on the contrary, would make them impractical as the mobile-stationary contacts are extremely short and infrequent.Show more Item Design and performance of resource allocation mechanisms for network slicing(2018-09-14) Caballero Garces, Pablo; De Veciana, Gustavo; Banchs Roca, Albert; Andrews, Jeffrey G; Baccelli, Francois; Shakkottai, Sanjay; Hasenbein, John JShow more Next generation wireless networks are expected to handle an exponential increase in demand for capacity generated by a collection of tenants and/or services with heterogeneous requirements. Multi-tenant network sharing, enabled through virtualization and network slicing, offers the opportunity to reduce operational and deployment costs, and the challenge of managing resource allocations among multiple tenants serving possibly mobile diverse customers. When designing shared radio resource allocation mechanisms, it is as important to provide tenants with customization and isolation guarantees, as it is to achieve high resource utilization and to do so via low complexity and easy to implement algorithms. This thesis is devoted to the design and analysis of resource allocation mechanisms that meet these objectives. We propose a sharing model in which tenants are assigned a share/budget of a pool of network resources. This share is then redistributed in the form of weights amongst users, which in turn drive dynamic resource allocations which are partially able to adapt to the traffic demands on, and requirements of, different slices customer populations. We propose and analyze two approaches for redistributing slices’ share among customers which we classify into their associated (i) cooperative, and (ii) competitive resource allocations. In the cooperative resource allocation setting, a pre-established policy is proposed, in which resources are eventually assigned in proportion to the slice’s share and the relative number of active users in currently has at a resource. This is shown to be socially optimal in a particular setting and simple to implement, with statistical multiplexing gains that increase with the number of tenants and the size of the resource pool. These gains stem from the ability of the scheme to adapt to dynamic loads leading to an up to 50% network capacity savings with respect to static allocations. We further improve these gains by presenting a framework that combines resource allocation and wireless user association which uses limited computational, information, and handoff overheads. However, using our cooperative scheme over a large pool of resources restricts the degree to which a slice can differentiate its customers’ performance at a per resource level. Thus, we study how this trade-off affects the network utility and propose a mechanism to determine an optimal partition the resources into a collection of self-managed pools under cooperative resource allocations. Our competitive resource allocation approach enables tenants to reap the performance benefits of sharing while retaining the ability to customize their own users’ allocations. This setting results in a network slicing game in which each tenant reacts to the user allocations of the others so as to maximize its own customers’ utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a ten- ant always achieves the same, or better, utility than it could achieve under a static partitioning of resources, hence providing the same level of inter-slice protection as static resource partitioning. The network utility of the equilibrium allocations is shown to be, under mild conditions, close to the socially optimal ones. The competitive resource allocation framework is complemented with a study on admission control policies that enable tenants to ensure minimum rate guarantees to their users. Our analysis and extensive simulation results confirm that our framework provides a comprehensive practical solution towards multi-tenant network slicing. We also discuss how our theoretical results fill a gap in the general resource allocation literature for strategic players.Show more Item Designing MIMO interference alignment networks(2012-08) Nosrat Makouei, Behrang; Heath, Robert W., Jr, 1973-; Andrews, Jeffrey G.; Evans, Brian L.; Hasenbein, John; Nettles, Scott; Vishwanath, SriramShow more Wireless networks are increasingly interference-limited, which motivates the development of sophisticated interference management techniques. One recently discovered approach is interference alignment, which attains the maximum sum rate scaling (with signal-to-noise ratio) in many network configurations. Interference alignment is not yet well understood from an engineering perspective. Such design considerations include (i) partial rather than complete knowledge of channel state information, (ii) correlated channels, (iii) bursty packet-based network traffic that requires the frequent setup and tear down of sessions, and (iv) the spatial distribution and interaction of transmit/receive pairs. This dissertation aims to establish the benefits and limitations of interference alignment under these four considerations. The first contribution of this dissertation considers an isolated group of transmit/receiver pairs (a cluster) cooperating through interference alignment and derives the signal-to-interference-plus-noise ratio distribution at each receiver for each stream. This distribution is used to compare interference alignment to beamforming and spatial multiplexing (as examples of common transmission techniques) in terms of sum rate to identify potential switching points between them. This dissertation identifies such switching points and provides design recommendations based on severity of the correlation or the channel state information uncertainty. The second contribution considers transmitters that are not associated with any interference alignment cooperating group but want to use the channel. The goal is to retain the benefits of interference alignment amid interference from the out-of-cluster transmitters. This dissertation shows that when the out-of-cluster transmitters have enough antennas, they can access the channel without changing the performance of the interference alignment receivers. Furthermore, optimum transmit filters maximizing the sum rate of the out-of-cluster transmit/receive pairs are derived. When insufficient antennas exist at the out-of-cluster transmitters, several transmit filters that trade off complexity and sum rate performance are presented. The last contribution, in contrast to the first two, takes into account the impact of large scale fading and the spatial distribution of the transmit/receive pairs on interference alignment by deriving the transmission capacity in a decentralized clustered interference alignment network. Channel state information uncertainty and feedback overhead are considered and the optimum training period is derived. Transmission capacity of interference alignment is compared to spatial multiplexing to highlight the tradeoff between channel estimation accuracy and the inter-cluster interference; the closer the nodes to each other, the higher the channel estimation accuracy and the inter-cluster interference.Show more Item Exploiting temporal stability and low-rank structure for localization in mobile networks(2010-08) Rallapalli, Swati; Zhang, Yin, doctor of computer science; Qiu, Lili, Ph. D.Show more Localization is a fundamental operation for many wireless networks. While GPS is widely used for location determination, it is unavailable in many environments either due to its high cost or the lack of line of sight to the satellites (e.g., indoors, under the ground, or in a downtown canyon). The limitations of GPS have motivated researchers to develop many localization schemes to infer locations based on measured wireless signals. However, most of these existing schemes focus on localization in static wireless networks. As many wireless networks are mobile (e.g., mobile sensor networks, disaster recovery networks, and vehicular networks), we focus on localization in mobile networks in this thesis. We analyze real mobility traces and find that they exhibit temporal stability and low-rank structure. Motivated by this observation, we develop three novel localization schemes to accurately determine locations in mobile networks: 1. Low Rank based Localization (LRL), which exploits the low-rank structure in mobility. 2. Temporal Stability based Localization (TSL), which leverages the temporal stability. 3. Temporal Stability and Low Rank based Localization (TSLRL), which incorporates both the temporal stability and the low-rank structure. These localization schemes are general and can leverage either mere connectivity (i.e., range-free localization) or distance estimation between neighbors (i.e., range-based localization). Using extensive simulations and testbed experiments, we show that our new schemes significantly outperform state-of-the-art localization schemes under a wide range of scenarios and are robust to measurement errors.Show more Item Exploring tradeoffs in wireless networks under flow-level traffic: energy, capacity and QoS(2009-12) Kim, Hongseok; De Veciana, GustavoShow more Wireless resources are scarce, shared and time-varying making resource allocation mechanisms, e.g., scheduling, a key and challenging element of wireless system design. In designing good schedulers, we consider three types of performance metrics: system capacity, quality of service (QoS) seen by users, and the energy expenditures (battery lifetimes) incurred by mobile terminals. In this dissertation we investigate the impact of scheduling policies on these performance metrics, their interactions, and/or tradeoffs, and we specifically focus on flow-level performance under stochastic traffic loads. In the first part of the dissertation we evaluate interactions among flow-level performance metrics when integrating QoS and best effort flows in a wireless system using opportunistic scheduling. We introduce a simple flow-level model capturing the salient features of bandwidth sharing for an opportunistic scheduler which ensures a mean throughput to each QoS stream on every time slot. We show that the integration of QoS and best effort flows results in a loss of opportunism, which in turn results in a reduction of the stability region, degradation in system capacity, and increased file transfer delay. In the second part of the dissertation we study several ways in which mobile terminals can backoff on their uplink transmit power (thus slow down their transmissions) in order to extend battery lifetimes. This is particularly effective when a wireless system is underloaded, so the degradation in the users' perceived performance can be negligible. The challenge, however, is developing a mechanism that achieves a good tradeoff among transmit power, idling/circuit power, and the performance customers will see. We consider systems with flow-level dynamics supporting either real-time or best effort (e.g., file transfers) sessions. We show that significant energy savings can be achieved by leveraging dynamic spare capacity. We then extend our study to the case where mobile terminals have multiple transmit antennas. In the third part of the dissertation we develop a framework for user association in infrastructure-based wireless networks, specifically focused on adaptively balancing flow loads given spatially inhomogeneous traffic distributions. Our work encompasses several possible user association objective functions resulting in rate-optimal, throughput-optimal, delay-optimal, and load-equalizing policy, which we collectively denote [alpha]-optimal user association. We prove that the optimal load vector that minimizes this function is the fixed point of a certain mapping. Based on this mapping we propose an iterative distributed user association policy and prove that it converges to the globally optimal decision in steady state. In addition we address admission control policies for the case where the system cannot be stabilized.Show more Item Fundamentals of distributed transmission in wireless networks : a transmission-capacity perspective(2011-05) Liu, Chun-Hung; Andrews, Jeffrey G.; Shakkottai, Sanjay; Arapostathis, Ari; Morton, David; Vishwanath, SriramShow more Interference is a defining feature of a wireless network. How to optimally deal with it is one of the most critical and least understood aspects of decentralized multiuser communication. This dissertation focuses on distributed transmission strategies that a transmitter can follow to achieve reliability while reducing the impact of interference. The problem is investigated from three directions : distributed opportunistic scheduling, multicast outage and transmission capacity, and ergodic transmission capacity, which study distributed transmission in different scenarios from a transmission-capacity perspective. Transmission capacity is spatial throughput metric in a large-scale wireless network with outage constraints. To understand the fundamental limits of distributed transmission, these three directions are investigated from the underlying tradeoffs in different transmission scenarios. All analytic results regarding the three directions are rigorously derived and proved under the framework of transmission capacity. For the first direction, three distributed opportunistic scheduling schemes -- distributed channel-aware, interferer-aware and interferer-channel-aware scheduling are proposed. The main idea of the three schemes is to avoid transmitting in a deep fading and/or sever interfering context. Theoretical analysis and simulations show that the three schemes are able to achieve high transmission capacity and reliability. The second direction focuses on the study of the transmission capacity problem in a distributed multicast transmission scenario. Multicast transmission, wherein the same packet must be delivered to multiple receivers, has several distinctive traits as opposed to more commonly studied unicast transmission. The general expression for the scaling law of multicast transmission capacity is found and it can provide some insight on how to do distributed single-hop and multi-hop retransmissions. In the third direction, the transmission capacity problem is investigated for Markovain fading channels with temporal and spatial ergodicity. The scaling law of the ergodic transmission capacity is derived and it can indicate a long-term distributed transmission and interference management policy for enhancing transmission capacity.Show more Item High-performance scheduling algorithms for wireless networks(2010-12) Bodas, Shreeshankar Ravishankar; Vishwanath, Sriram; Shakkottai, Sanjay; Caramanis, Constantine; de Veciana, Gustavo; Hasenbein, John; Srikant, R.Show more The problem of designing scheduling algorithm for multi-channel (e.g., OFDM-based) wireless downlink networks is considered, where the system has a large bandwidth and proportionally large number of users to serve. For this system, while the classical MaxWeight algorithm is known to be throughput-optimal, its buffer-overflow performance is very poor (formally, it is shown that it has zero rate function in our setting). To address this, a class of algorithms called iHLQF (iterated Heaviest matching with Longest Queues First) is proposed. The algorithms in this class are shown to be throughput-optimal for a general class of arrival/channel processes, and also rate-function optimal (i.e., exponentially small buffer overflow probability) for certain arrival/channel processes, where the channel-rates are 0 or 1 packets per timeslot. iHLQF however has higher computational complexity than MaxWeight (n⁴ vs. n² computations per timeslot respectively). To overcome this issue, a new algorithm called SSG (Server-Side Greedy) is proposed. It is shown that SSG is throughput-optimal, results in a much better per-user buffer overflow performance than the MaxWeight algorithm (positive rate function for certain arrival/channel processes), and has a computational complexity (n²) that is comparable to the MaxWeight algorithm. Thus, it provides a nice trade-off between buffer-overflow performance and computational complexity. For multi-rate channel processes, where the channels can serve multiple packets per timeslot, new Markov chain-based coupling arguments are used to derive rate-function positivity results for the SSG algorithm. Finally, an algorithm called DMEQ is proposed and shown to be rate-function optimal for certain multi-rate channel scenarios, whose definition characterizes the sufficient conditions for rate-function optimality in this regime. These results are validated by both analysis and simulations.Show more Item Improving performance and incentives in disruption-tolerant networks(2010-08) Shevade, Upendra; Zhang, Yin, doctor of computer science; Browne, James; Mok, Aloysius; Qiu, Lili; Vin, HarrickShow more The recent proliferation of personal wireless devices has led to the emergence of disruption-tolerant networks (DTNs), which are characterized by intermittent connectivity among some or all participating nodes and a consequent lack of contemporaneous end-to-end paths between the source and consumer of information. However, the success of DTNs as a communication paradigm is critically dependent on the following challenges being addressed: (1) How to enable popular but demanding applications, such as video-on-demand, to operate in such constrained network settings, and (2) How to incentivize individual devices to cooperate when network operation is only possible under, or greatly benefits from cooperation. In this dissertation, we present a novel set of protocols and develop real systems that effectively meet the above challenges. We make the following contributions: First, we design and implement a novel system for enabling high bandwidth content distribution in vehicular DTNs by leveraging infrastructure access points (APs). We predict which APs will soon be visited by a vehicular node and then proactively push content-of-interest to those APs. Our replication schemes optimize content delivery by exploiting Internet connectivity, local wireless connectivity, node relay connectivity and mesh connectivity among APs. We demonstrate the effectiveness of our system through trace-driven simulation and Emulab emulation using real taxi and bus traces. We further deploy our system in two vehicular networks: a fourteen AP 802.11b network and a four AP 802.11n network with smartphones and laptops as clients. Second, we propose an incentive-aware routing protocol for DTNs. In DTNs, routing takes place in a store-and-forward fashion with the help of relay nodes. If the nodes in a DTN are controlled by rational entities, such as people or organizations, the nodes can be expected to behave selfishly by attempting to maximize their utilities and conserve their resources. Since routing is inherently a cooperative activity, system operation will be critically impaired unless cooperation is incentivized. We propose the use of pair-wise tit-for-tat (TFT) as a simple, robust and practical incentive mechanism for DTNs. We then develop an incentive-aware routing protocol that allows selfish nodes to maximize their own performance while conforming to TFT constraints.Show more Item Improving the performance and efficiency of wireless networks using rate adaptation(2015-12) Khan, Muhammad Owais; Vishwanath, Sriram; Qiu, Lili, Ph. D.; de Veciana, Gustavo; Julien, Christine; Gouda, MohamedShow more Recent years have seen a staggering increase in the deployment and utilization of wireless networks. More and more devices are being equipped with Wireless LAN (WLAN) cards to take advantage of the omnipresence of WLAN networks. Therefore, it has become necessary that the protocols used by WLANs are efficient and provide good performance. Rate Adaptation protocols are an important mechanism employed by WLANs to improve network performance. This dissertation develops three complementary techniques, which use rate adaptation to optimize and improve performance by i) performing rate adaptation to optimize energy consumption, ii) developing a more accurate technique to predict the frame delivery ratio that is used by rate adaptation protocols, and iii) jointly optimizing rate adaptation with data retransmission to maximize throughput. More specifically, in i), we use extensive measurements to develop a simple yet accurate energy consumption model for 802.11n wireless cards. We use the model to drive the design of an energy aware rate adaptation scheme. A major benefit of a model-based rate adaptation is that applying a model allows us to eliminate frequent probes required in many existing rate adaptation schemes. In ii), we find that the accuracy of existing delivery ratio calculation techniques is still limited due to bursty errors inherent to the wireless channel. We develop a new method for computing packet delivery rate that captures the burstiness of errors. Furthermore, we propose a new data interleaving technique, which leverages our framework to reduce the burstiness of errors, thereby improving frame delivery ratio. Finally, in iii), we address the susceptibility of wireless networks to transmission failures due to dynamic channel conditions and unpredictable interference. To efficiently recover from failures, we propose a retransmission scheme where the receiver combines information received from multiple failed transmissions associated with the same frame. The protocol has two distinguishing features. First, it simultaneously supports partial retransmission and combines bits with low confidence. Second, it jointly optimizes the data rate of the retransmission and the information to be retransmitted to maximize throughput.Show more Item Improving the performance of wireless networks using frame aggregation and rate adaptation(2010-12) Kim, Won Soo, 1975-; Nettles, Scott M.; de Veciana, Gustavo; Heath, Robert W.; Julien, Christine; Qiu, LiliShow more As the data rates supported by the physical layer increase, overheads increasingly dominate the throughput of wireless networks. A promising approach for reducing overheads is to group a number of frames together into one transmission. This can reduce the impact of overheads by sharing headers and the time spent waiting to gain access to the transmission floor. Traditional aggregation schemes require that frames that are aggregated all be destined to the same receiver. These approaches neglect the fact that transmissions are broadcast and a single transmission will potentially be received by many receivers. Thus, by taking advantage of the broadcast nature of wireless transmissions, overheads can be amortized over more data and achieve more performance gain. To show this, we design a series of MAC-based aggregation protocols that take advantage of rate adaptation and the broadcast nature of wireless transmissions. We first show the design of a system that can aggregate both unicast and broadcast frames. Further, the system can classify TCP ACK segments so that they can be aggregated with TCP data flowing in the opposite direction. Second, we develop a rate-adaptive frame aggregation scheme that allows us to find the best aggregation size by tracking the size based on received data frames and the data rate chosen by rate adaptation. Third, we develop a multi-destination frame aggregation scheme to aggregate broadcast frames and unicast frames that are destined for different receivers using delayed ACKs. Using a delayed ACK scheme allows multiple receivers to control transmission time of the ACKs. Finally, we extend multi-destination rate-adaptive frame aggregation to allow piggybacking of various types of metadata with user packets. This promises to lower the impact of metadata-based control protocols on data transport. A novel aspect of our work is that we implement and validate the designs not through simulation, but rather using our wireless node prototype, Hydra, which supports a high performance PHY based on 802.11n. To validate our designs, we conduct extensive experiments both on real and emulator-based channels and measure system performance.Show more Item Interference management with limited channel state information in wireless networks(2014-12) Lee, Namyoon; Heath, Robert W., Jr, 1973-; Baccelli, F. (François), 1954-Show more Interference creates a fundamental barrier in attempting to improve throughput in wireless networks, especially when multiple concurrent transmissions share the wireless medium. In recent years, significant progress has been made on characterizing the capacity limits of wireless networks under the premise of global and instantaneous channel state information at transmitter (CSIT). In practice, however, the acquisition of such instantaneous and global CSIT as a means toward cooperation is highly challenging due to the distributed nature of transmitters and dynamic wireless propagation environments. In many limited CSIT scenarios, the promising gains from interference management strategies using instantaneous and global CSIT disappear, often providing the same result as cases where there is no CSIT. Is it possible to obtain substantial performance gains with limited CSIT in wireless networks, given previous evidence that there is marginal or no gain over the case with no CSIT? To shed light on the answer to this question, in this dissertation, I present several achievable sum of degrees of freedom (sum-DoF) characterizations of wireless networks. The sum-DoF is a coarse sum-capacity approximation of the networks, deemphasizing noise effects. These characterizations rely on a set of proposed and existing interference management strategies that exploit limited CSIT. I begin with the classical multi-user multiple-input-single-output (MISO) broadcast channel with delayed CSIT and show how CSI feedback delays change sum-capacity scaling law by proposing an innovative interference alignment technique called space-time interference alignment. Next, I consider interference networks with distributed and delayed CSIT and show how to optimally use distributed and moderately-delayed CSIT to yield the same sum-DoF as instantaneous and global CSIT using the idea of distributed space-time interference alignment. I also consider a two-hop layered multiple-input-multiple-output (MIMO) interference channel, where I show that two cascaded interfering links can be decomposed into two independent parallel relay channels without using CSIT at source nodes through the proposed interference-free relaying technique. Then I go beyond one-way and layered to multi-way and fully-connected wireless networks where I characterize the achievable sum-DoF of networks where no CSIT is available at source nodes using the proposed space-time physical-layer network coding. Lastly, I characterize analytical expressions for the sum spectral efficiency in a large-scale single-input-multiple- output (SIMO) interference network where the spatial locations of nodes are modeled by means of stochastic geometry. I derive analytical expressions for the ergodic sum spectral efficiency and the scaling laws as functions of relevant system parameters depending on different channel knowledge assumptions at receivers.Show more Item Low-overhead cooperation to mitigate interference in wireless networks(2013-05) Peters, Steven Wayne; Heath, Robert W., Jr, 1973-Show more Wireless cellular networks, which serve a large area by geographically partitioning users, suffer from interference from adjacent cells transmitting in the same frequency band. This interference can theoretically be completely mitigated via transceiver cooperation in both the uplink and downlink. Optimally, base stations serving the users can utilize high-capacity backbones. to jointly transmit and receive all the data in the network across all the base stations. In reality, the backbone connecting the base stations is of finite capacity, limiting joint processing to localized clusters. Even with joint processing on a small scale, the overhead involved in sharing data between multiple base stations is large and time-sensitive. Other forms of cooperation have been shown to require less overhead while exhibiting much of the performance benefit from interference mitigation. One particular strategy, called interference alignment (IA), has been shown to exploit all the spatial degrees of freedom in the channel provided data cannot be shared among base stations. Interference alignment was developed for the multi-user interference channel to exploit independent channel observations when all of the links in the network have high signal-to-noise ratio, and assumes all the nodes utilizing the physical resources are participating in the cooperative protocol. When some or all of the links are at moderate signal-to-noise ratio, or when there are non-cooperating users, IA is suboptimal. In this dissertation, I take three approaches to addressing the drawbacks of IA. First, I develop cooperative transmission strategies that outperform IA in various operationg regimes, including at low-to-moderate SNR and in the presence of non-cooperating users. These strategies have the same complexity and overhead as IA. I then develop algorithms for network partitioning by directly considering the overhead of cooperative strategies. Partitioning balances the capacity gains of cooperation with the overhead required to achieve them. Finally, I develop the shared relaying model, which is equivalent to the interference channel but with a single multi-antenna relay mediating communications between transceivers. The shared relay requires less overhead and cooperation than interference alignment but requires added infrastructure. It is shown to outperform conventional relaying strategies in cellular networks with a fixed number of total relay antennas.Show more Item Modeling and analysis of wireless networks with correlation and motion(2019-06-13) Choi, Chang-sik; Baccelli, F. (François), 1954-; de Veciana, Gustavo; Heath, Robert W; Andrews, Jeffrey G; Sirbu, MihaiShow more The use of stochastic geometry allows the analysis of the typical performance of a wireless network. Specifically, under a stationary framework, the network performance at a typical receiver represents the network performance spatially-averaged over all receivers. This approach has been applied to the Poisson point processes whose points are independently located in space. The Poisson point process expresses a total independence type randomness in network architectures. Its tractability leads to its wide use in modeling various wireless networks, e.g., cellular networks, ad hoc networks, and vehicular networks. However, a network analysis using the Poisson point process might be inaccurate when the network components are geometrically correlated or in motion, as in heterogeneous cellular networks, or vehicular networks. For instance, macro base stations are deployed far from each other. Vehicles are located on roads, i.e., lines, and they move on the lines. As a result, the analysis of these networks can be improved by new spatial models that capture these spatial and dynamic features. In my first contribution, I derive the signal-to-interference ratio (SIR) coverage probability of a typical user in heterogeneous cellular networks where base stations are modeled by the sum of a Poisson point process and a stationary square grid. In my second contribution, I develop a stationary framework based on the sum of a Cox point process and a Poisson point process to model random cellular networks with linear base stations and linear users on straight lines. I derive the SIR coverage probability of the typical user and characterize its association. In the third contribution, I investigate the statistical properties of the Cox point process, exploring the nearest distance distribution and the convergence of the Cox-Voronoi cell. In the above three contributions, I analyze the performance of wireless networks by focusing on their correlated structures, extracting results which cannot be obtained from models based only on Poisson point processes. In my fourth contribution, I propose a new technology for harvesting Internet-of-Things (IoT) data based on mesh relaying with vehicles as sinks. I derive the network capacity and compare it to the traditional approach, which is based on static base stations. In the fifth contribution, I derive the SIR distribution of direct communication from roadside devices to vehicles. By characterizing the evolution of the network snapshots, I derive the behavior of vehicles' service coverage area and the network latency. In my sixth contribution, I propose a data harvesting technology for the ground-based data devices, based on the use of unmanned aerial vehicles (UAVs). I derive the total data transmitted from a typical device by characterizing the evolution of network geometry with respect to time. These last three contributions are built on a combination of network snapshot analysis and network evolution analysis.Show more Item Modeling and analyzing wireless networks using stochastic geometry(2018-05) Lee, Junse; Baccelli, F. (François), 1954-; de Veciana, Gustavo; Andrews, Jeffrey; Heath, Robert; Taillefumier, ThibaudShow more Over the past decade, stochastic geometric models, and most notably the planar Poisson point process (PPP) model, have become popular for the analysis of spectral efficiency in wireless networks, in both the D2D and the cellular contexts [1]. By modeling base station (BS) and user locations as spatial point processes, stochastic geometry has recently been recognized as a tractable and efficient analytical tool to quantify key performance metrics. This tool provides a natural way of defining and computing macroscopic properties of multiuser information theory. These properties are obtained by averaging over all node patterns found in a large random network of the Euclidean plane. For example, some key performance metrics such as signal to interference and noise ratio and data rate depend on the network geometric configurations. This tool has thus been widely adopted for analyzing the network performance and broadening network design. This thesis proposes new models to represent several new scenarios. Three main scenarios are considered: 3-D inbuilding networks, MIMO adhoc networks, and multihop communication under mmWave networks. To do so, mathematical tools such as Poisson point processes, Poisson line processes, Boolean models and Poisson bipolar models are used. Each model is 1) generative in that it has a clear physical interpretation, 2) leads to explicit analytical representations of important wireless performance metrics, and 3) highly parametric, with parameters expressing the geometric characteristic of the elements of networks. Physical interpretations from these models are quite different from previous results. The core of this thesis is focused on the effects of correlated shadowing. Shadowing is the effect that the received signal power fluctuates due to objects obstructing the propagation path. By introducing an independent shadowing term over links, it is possible to model the effect of shadow fading. Most previous papers analyzing urban networks assume that shadowing fields are independent over links. With this assumption, it is possible to derive simple closed-form expressions of important network performance metrics. However, this assumption cannot capture that shadowing fields are spatially correlated. This thesis goes beyond the independent shadowing approximation and analyzes the effects of correlated shadowing on various performance metricsShow more Item Modeling, analysis, and design of collaborative services in vehicular and cloud/edge networks(2022-05-02) Kassir, Saadallah; De Veciana, Gustavo; Andrews, Jeffrey G; Caramanis, Constantine; Leibowicz, Benjamin D; Shakkottai, SanjayShow more The new wireless network technologies introduced in the fifth generation of cellular networks (5G) have enabled the development of various classes of mobile applications. This thesis investigates how these emerging mobile use-cases can make the most of the state-of-the-art wireless and computing technologies through effective collaborative network management and operations strategies. We study two general classes of services: (1) collaborative traffic relaying in vehicular ad-hoc networks (VANETs), aiming at providing highly available, fair and reliable connectivity/throughput to the network users; and (2) collaborative real-time services, aimed at providing devices with low-latency and high availability/reliability connectivity. In the first part of this thesis, we study VANETs and propose a novel vehicle connectivity framework wherein vehicles within communication range of each other form vehicle clusters, allowing them to opportunistically route traffic from/to each other. With the formation of these logical entities, vehicles can be viewed as mobile relay nodes, and have the potential to substantially improve the coverage and per-user throughput of the vehicular network. In this setting, we begin by presenting an analytical framework to study the performance gains enabled by this network architecture on a single road, and we show that vehicle clustering leads to considerable benefits including reduced throughput variability and improved coverage. We then look at larger-scale cellular networks and leverage results from the stochastic geometry literature to show that the proposed opportunistic vehicle clustering and relaying scheme has the potential to improve the throughput for both vehicles and non-vehicle-bound users by more than an order of magnitude through opportunistic relaying and cell load-balancing. Finally, we study wireless resource allocation mechanisms leading to improvements in shared-rate fairness among the network users. In the second part of this thesis, we study the operation of networks supporting real-time services, with a focus on devising efficient and timely information sharing mechanisms among the interconnected entities. We first examine how joint management of wireless communication and cloud/edge-computing resources can improve the timeliness of the information shared over the network, while reducing network resource provisioning costs. We investigate tradeoffs associated with status-update rate adaptation and service placement in the Cloud-to-Thing continuum for devices running real-time applications, and develop associated algorithms aiming at controlling the network congestion and improving the service availability. We argue that sending more information might be detrimental to its quality, and that various application-specific properties influence the service placement decision in the Cloud-to-Thing continuum. We then examine the performance of real-time multi-user services via the specific example of Multiplayer Cloud Gaming (MCG), and exhibit how joint rate adaptation is key to controlling congestion and providing a high quality of service in spite of spatio-temporal variations in the network delays particularly impacting massive multi-user services. Finally, we give particular attention to timely information sharing in collaborative-sensing vehicular networks. We introduce a communication-efficient information-sharing mechanism enabling vehicles to benefit from each other’s sensing capability in real-time via a centralized node (e.g., edge compute node, a cellular base station, or a road side unit). Our proposed mechanism opportunistically improves the vehicles’ situational awareness when assistance is available, allowing them, for instance, to drive at a faster speed without compromising on safety.Show more Item On distributed scheduling for wireless networks with time-varying channels(2013-05) Reddy, Akula Aneesh; Shakkottai, SanjayShow more Wireless scheduling is a fundamental problem in wireless networks that involves scheduling transmissions of multiple users in order to support data flows with as high rates as possible. This problem was first addressed by Tassuilas and Ephremides, resulting in the celebrated Back-Pressure network scheduling algorithm. This algorithm schedules network links to maximize throughput in an opportunistic fashion using instantaneous network state information (NSI), i.e., queue and channel state knowledge across the entire network. However, the Back-Pressure (BP) algorithm suffers from various drawbacks - (a) it requires knowledge of instantaneous NSI from the whole network, i.e. feedback about time-varying channel and queue states from all links of the network, (b) the algorithm requires solving a global optimization problem at each time to determine the schedule, making it highly centralized. Further, Back-pressure algorithm was originally designed for wireless networks where interference is modeled using protocol interference model. As recent break-throughs in full-duplex communications and interference cancelation techniques provide greatly increased capacity and scheduling flexibility, it is not clear how BP algorithm can be modified to improve the data rates and reduce the delay. In this thesis, we address the drawbacks of Back-Pressure algorithm to some extent. In particular, our first work provides a new scheduling algorithm (similar to BP) that allows users to make individual decisions (distributed) based on heterogeneously delayed network state information (NSI). Regarding the complexity issue, in our second work, we analyze the performance of the greedy version of BP algorithm, known as Greedy Maximal Scheduling (GMS) and understand the effect of channel variations on the performance of GMS. In particular, we characterize the efficiency ratio of GMS in wireless networks with fading. In our third and fourth work, we propose and analyze new scheduling algorithms that can benefit from new advancements in interference cancelation techniques.Show more Item On the dynamics and optimization of spatial random systems(2022-08-03) Ramesan, Nithin Seyon; De Veciana, Gustavo; Baccelli, Francois; Shakkottai, Sanjay; Andrews, Jeffrey G; Neeman, JoeShow more In this thesis, we model and study a collection of real-world systems – each of whose evolution or behavior depends on spatial relationships between locations of interacting entities that comprise the system. For each, we use the theory of Poisson point processes (PPPs) to model the random locations of constituent entities. All but one of the systems we study are wireless networks comprised of transmitters and receivers, whose interaction with the system at large is via the signal power they transmit and receive respectively. The other is an epidemic system – here the constituent entities are members of a population among whom a contagion is spreading. Each individual's interaction with the system at large is via their transmission of the contagion to other individuals. We are concerned with two lines of inquiry with respect to these systems - dynamics and optimization. The former line of inquiry is the study of the time-evolution of a time-varying system, the characterization of its steady-state behavior and engineering/design insights derived thereafter. The first two works presented in this thesis fall under this umbrella. In the first work, we consider a wireless queue in a field of moving transmitters. The queue's arrival rate is independent of the rest of the system, but its departure rate depends on the interference it experiences. This interference in turns depends on the locations of the moving transmitters and is therefore time-varying. We characterize stability conditions for the queue as a function of the mobility (showing the non-existence of stability guarantees in the absence of mobility and the existence of a stability guarantee in its presence). We then show that the queue length decreases (according to a stochastic ordering) as mobility increases, which is a consequence of the interference becoming less variable. The overall insight is that mobility in networks improves queuing delay-related performance metrics. The second work also studies the effect of mobility, albeit its effect on the spread and survival of an SIS epidemic. Individuals in a population can spread a contagion to neighbors that are close enough, and recover from the contagion independent of the rest of the system. Individuals move around via large i.i.d. displacements. We find exact expressions that characterize the steady-state fraction of infected individuals using the rate conservation principle, and approximate expressions for the same via moment measure closure methods. We then use them to conjecture a phase diagram (supported by simulations) that describes the values of system parameters for which the epidemic dies out. In doing so, we find that reducing mobility (eq., lockdowns) in the population need not always lead to the contagion dying out faster. We then shift our attention to the latter line of inquiry, optimization, which is concerned with studying an objective function (eq., a performance metric) of a wireless system, and maximizing it with respect to a particular system variable. In the third and fourth works in this thesis, the system variable we optimize over is the intensity measure of the PPP. In our third work, we attempt to analytically solve a specific optimization problem – given transmitters distributed according to a homogeneous PPP, our goal is to show (under general assumptions on the system) that the probability of coverage of an arbitrary fixed location is maximized for a unique value of the intensity of the PPP. We make partial progress by using the theory of Malliavin calculus to show that there must be at least one and at most a finite number of maxima, but fall short of our final goal. Our next work takes a numerical approach to solving a far more general problem – given a wireless performance metric (the mean of a functional of a PPP), we propose an algorithm to find the optimal mean number of transmitters and a corresponding optimized intensity measure that controls placement of transmitters. This algorithm also uses the theory of Malliavin calculus – here, to iteratively move along a steepest ascent direction and hence improve the intensity measure. Numerical results show that the algorithm produces wireless network models that adapt well to underlying system configurations. The proposed framework is hence a powerful tool for the dimensioning and planning of wireless networks, and to our knowledge, the first such tool based on point process theory. Our final work deals with the problem of optimizing a global proportional fairness metric for a (very large) wireless network in a distributed way using local power control. We do so via a heuristic that considers the nearest-neighbor graph of transmitter-receiver pairs and decomposes the graph into its individual connected components. We establish that each connected component is on average small, and hence the corresponding optimization problems are of low average complexity. Under specific assumptions on the system, the optimization problem can be further simplified and has a closed form solution. Simulations show that the resulting power control scheme results in significant performance gains.Show more Item Online learning algorithms for wireless scheduling(2023-12) Song, Jianhan; De Veciana, Gustavo; Shakkottai , Sanjay; Hasenbein, John J; Mokhtari, Aryan; Caramanis, ConstantineShow more Online learning, and more specifically, multi-armed bandit algorithms, has recently garnered significant interest across diverse fields. Within an online learning framework, agents can leverage past interactions with their environment to optimize future decisions, making it an ideal mechanism for use in applications such as recommendation systems. Driven by these advantages, we believe that the online learning approach can be effectively employed to address resource allocation and scheduling challenges in wireless systems, with the potential to enhance the adaptability and robustness of system performance. In this dissertation, we explore the applications of multi-armed bandit algorithms in various wireless settings, showcasing their efficacy through both theoretical analysis and empirical demonstrations. We first studied the multi-user scheduling problem for the wireless downlink with instantaneous channel rate and queue information. We introduced the concept of "meta-scheduling", which formulates the task of selecting an optimal wireless scheduler as a bandit problem, and proposed a UCB-type bandit algorithm designed to adapt to the dynamics of a queueing system. Expanding on the meta-scheduling concept, we then studied a model of hierarchical scheduling in the context of network slicing, in which the base station learns the optimal option among infinitely-many arms. Our approach involves formulating the problem as a blackbox optimization and addressing it using an HOO-type bandit algorithm adaptive to random queueing cycles. Lastly, we transitioned into a multi-agent setting, where decisions of learning agents in close proximity are coupled with each other through interference. Within this context, we identified a low-complexity structure termed the "weakly-coupled system", and developed a decentralized bandit algorithm to facilitate the learning of optimal collective actions. Throughout each of these segments, we presented rigorous theoretical proofs demonstrating that the proposed algorithms exhibit the desired sub-linear regret compared to an idealized genie. Furthermore, we validated the efficacy of the algorithms through a series of experiments using simulation.Show more Item Online learning for scheduling in wireless networks(2022-05-06) Tariq, Isfar; Shakkottai, Sanjay; De Veciana, Gustavo A; Caramanis, Constantine; Baccelli, Francois; Hasenbein, John JShow more Over the last few years, online learning has grown in importance as it allows us to build systems that can interact with the environment while continuously learning from past interactions to improve future decisions to maximize some objective. While online learning is used in several areas like recommendation systems, however, due to the complexity of wireless scheduling it is unclear how to utilize online learning. For instance, MU-MIMO scheduling involves the selection of a user subset and associated rate selection each time-slot for varying channel states (the vector of quantized channels matrices for each of the users) — a complex integer optimization problem that is different for each channel state. We propose that a low-dimensional structure is present in the wireless systems which can be exploited through online learning. For instance, channel-states "near" each other will likely have the same optimal solution. In our first problem, we present a framework through which we formulate the wireless scheduling problem as a multi-armed bandit problem. We then propose an online algorithm that can cluster the channel-states and learn the capacity region of these clusters. We show that our algorithms can significantly reduce the complexity of online learning for wireless settings and provide regret guarantees for our algorithm. In the second problem, we expand on our previous work and present (1) a framework that further exploits the low-dimensional structure present in the system by clustering users and (2) an online algorithm that utilizes the parameters learned by our previous algorithms to optimize the subset of users to be scheduled for given channel-state. We show that our algorithms can not only converge faster but also improve the overall throughput of the system. We also provide regret guarantees for the user clustering algorithm.Show more