Internet content providers (e.g., video streaming, web services, file downloads) are increasingly deploying Peer-to-Peer Content Delivery Networks (PCDNs) to cache and distribute content by leveraging spare compute and storage resources at the network edge. This shift is primarily driven by the high costs associated with building and maintaining large-scale, centralized Content Delivery Network (CDN) architectures. A PCDN architecture can use multiple edge devices (e.g., access points, smart home gadgets) to distribute data in parallel to each client to increase the communication bandwidth and robustness to link/server failures. However, existing distributed multipath transport schedulers face significant challenges, such as Head-of-Line (HoL) blocking and Out-Of-Order (OOO) packet delivery, which can degrade multipath throughput to levels even lower than using a single path alone. In this paper, we introduce MPRD (Multipath Parallel Reverse Segment Download), a novel receiver-driven distributed transport protocol designed to overcome these limitations in PCDN architectures. MPRD is built on top of TCP and rethinks traditional multipath transport by using single-path TCP at each edge server. In MPRD, the client optimizes the starting point and direction of file segments to be downloaded by each single-path TCP connection in order to eliminate the inefficiencies of existing multipath schedulers. We implemented a prototype of MPRD in the Linux userspace and demonstrate that it significantly improves client throughput and reduces the CDN load (when the CDN is augmented by PCDN edge servers). We also evaluate MPRD’s performance in improving video delivery quality and show that it can substantially reduce the stalls and increase the PSNR visual quality by up to 35 dB.

Emerging mobile applications give rise to new bandwidth-hungry and latency-sensitive traffic classes that challenge existing wireless systems. Addressing them requires innovative approaches such as simultaneous data transmission across multiple Radio Access Technologies (RATs), e.g., WiFi and WiGig. However, existing transport layer multi-RAT traffic aggregation schemes, e.g., multi-path TCP, suffer from Head-of-Line (HoL) blocking and sub-optimal traffic splitting across the RATs that severely penalize their performance. In this paper, we investigate the design of FBDT, a novel multi-path transport layer solution that for the first time can achieve the sum of the throughput rates across the individual RATs network paths, despite their channel conditions' dynamics. We have implemented FBDT in the Linux kernel and show substantial improvement in throughput relative to state-of-the-art schemes, e.g, 2.5x gain in a dual-RAT scenario (WiFi and WiGig) when the client is mobile. Second, we extend FBDT to more than two radios and demonstrate that its throughput performance scales linearly with the number of RATs, in contrast to multi-path TCP, whose performance degrades with an increase in the number of RATs. We evaluate the performance of FBDT on different traffic classes and demonstrate: (i) 2-3 times shorter file download times, (ii) up to 10 times shorter streaming times and 10 dB higher video quality for progressive download video applications, and (iii) up to 9 dB higher viewport quality for interactive mobile VR applications, when our viewport quality maximization framework is employed along with FBDT.

The metaverse encompasses many virtual universes and relies on streaming high-quality 360° videos to VR/AR headsets. This type of video transmission requires very high data rates to meet the desired Quality of Experience (QoE) for all clients. Simultaneous data transmission across multiple Radio Access Technologies (RATs) such as WiFi and WiGig is a key solution to meet this required capacity demand. However, existing transport layer multi-RAT traffic aggregation schemes suffer from Head-of-Line (HoL) blocking and sub-optimal traffic splitting across the RATs, particularly when there is a high fluctuation in their channel conditions. As a result, state-of-the-art multi-path TCP (MPTCP) solutions can achieve aggregate transmission data rates that are lower than that of using only a single WiFi RAT in many practical settings, e.g., when the client is mobile. We make two key contributions to enable high quality mobile 360° video VR streaming using multiple RATs. First, we propose the design of FBDT, a novel multi-path transport layer solution that can achieve the sum of individual transmission rates across the RATs despite their system dynamics. We implemented FBDT in the Linux kernel and showed substantial improvement in transmission throughput relative to state-of-the-art schemes, e.g, 2.5x gain in a dual-RAT scenario (WiFi and WiGig) when the VR client is mobile. Second, we formulate an optimization problem to maximize a mobile VR client's viewport quality by taking into account statistical models of how clients explore the 360° look-around panorama and the transmission data rate of each RAT. We explore an iterative method to solve this problem and evaluate its performance through measurement-driven simulations leveraging our testbed. We show up to 12 dB increase in viewport quality when our optimization framework is employed.

Identifying whether the wireless channel between two devices (e.g., a base station and a client device) is Line-of Sight (LoS) or non-Line-of-Sight (nLoS) has many applications, e.g., it can be used in device localization. Prior works have addressed this problem, but they are primarily limited to sub6 GHz systems, assume sophisticated radios on the devices, incur additional communication overhead, and/or are specific to a single class of devices (e.g., a specific smartphone). In this paper, we address this channel classification problem for wireless devices with mmWave radios. Specifically, we show that existing beamforming training messages that are exchanged periodically between mmWave wireless devices can also be used in a deep learning model to solve the channel classification problem with no additional overhead. We then extend our work by developing a transfer learning model (t-LNCC) that is trained on simulated data, and can successfully solve the channel classification problem on any commercial-off-the-shelf (COTS) mmWave device with/without any real-world labeled data. The accuracy of t-LNCC is more than 95% across three different COTS wireless devices, when there is a small sample of labeled data for each device. We finally show the application of our classification problem in estimating the distance between two wireless devices, which can be used in localization.

Data generated by increasingly pervasive and intelligent devices has led to an explosion in the use of machine learning (ML) and artificial intelligence, with ever more complex models trained to support applications in fields as diverse as healthcare, finance, and robotics. In order to train these models in a reasonable amount of time, the training is often distributed among multiple machines. However, paying for these machines (either by constructing a local cloud infrastructure or renting machines through an external provider such as Amazon AWS) is very costly. We propose to reduce these costs by creating a marketplace of computing resources designed to support distributed machine learning algorithms. Through our marketplace (coined “DeepMarket”), users can lend their spare computing resources (when not needed) or augment their resources with available DeepMarket machines to train their ML models. Such a marketplace directly provides several benefits for two groups of researchers: (i) ML researchers would be able to train their models with much reduced cost, and (ii) network economics researchers would be able to experiment with different compute pricing mechanisms. The focus of this Demo is to introduce the audience to DeepMarket and its user interface (named “PLUTO”). In particular, we will bring a few laptops with pre-installed PLUTO applications so that users can see how they can create an account on DeepMarket servers, lend their resource, borrow available resources, submit ML jobs, and retrieve the results. Our overall goal is to encourage the conference audience to install PLUTO on their own machines and create a user and developer community around DeepMarket.

Millimeter-wave (mmWave) systems use highly directional beams with narrow beamwidths to overcome the high path loss associated with their frequency bands. The use of narrow beams complicates the link establishment process as the transmitter and receiver need to search for appropriate beams before they can communicate with each other. Existing mmWave standards address the beam search process as part of the initial access (IA), and use contention based schemes that let multiple clients train their beams in the same search interval. However, there exists a severe power imbalance among competing clients' beams, as clients naturally have different orientations and are at different distances from the same access point. This beam power imbalance coupled with poor contention protocols results in poor IA fairness in dynamic systems with multiple clients. We propose a joint power control and contention adaptation protocol (coined JPOC) that addresses this unfairness problem. JPOC uses an open-loop and client-side power control mechanism that reduces the beam power imbalance among competing clients. It also uses a model-driven contention adaptation protocol that optimally adjusts the duration of the contention time according to the system dynamics. Comprehensive evaluation through a mixture of experiments and simulations show that compared to existing 802.11 ad/ay standards, JPOC substantially reduces the contention overhead and increases the IA fairness.

Spatial correlation has been recognized by 3GPP as one of the key elements in millimeter-wave (mmWave) channel modeling. Correlated channel behavior is induced by macro objects, such as buildings, as well as by micro objects, including humans around the mmWave receivers. The 3GPP's three-dimensional (3D) spatially consistent channel model designed to capture these phenomena assumes a-priori knowledge of the correlation distance between the receivers. In this paper, we propose a novel spatially-consistent human body blockage state generation procedure, which extends the standardized 3D channel model by 3GPP to capture the correlation between the line-of-sight (LoS) links and the reflected cluster states affected by human body blockage. The proposed model is based on analytical expressions for the conditional link state probability, thus permitting the parametrization of the spatial field of receivers. It also does not require any a-priori information on the correlation distance as the latter is identified explicitly based on the environmental parameters. We compare the results for the proposed model with those obtained with the uncorrelated blockage model and conclude that in many special cases correlation manifests itself in quantitatively different propagation conditions experienced at the nearby receivers.

We present the design and implementation of PAFD, a design methodology that enables full-duplex (FD) in hybrid beamforming systems with constant amplitude phased array antennas. The key novelty in PAFD's design is construction of analog beamformers that maximize the beamforming gains in the desired directions while simultaneously reducing the self-interference (SI). PAFD is implemented on the WARP platform, and its performance is extensively evaluated in both indoor and outdoor environments. Our experimental results reveal that (i) PAFD sacrifices a few dB in beamfomring gain to provide large amounts of reduction in SI power; (ii) the reduction in SI is dependent on the number of phased array antennas and increases as the number of antennas increases; and (iii) finally, PAFD significantly outperforms half-duplex (HD) for small cells even in presence of high interference caused by uplink clients to the downlink clients. The gains increase with a larger array size or less multipath in the propagation environment.

We study the resource allocation problem in RAN-level integrated HetNets. This emerging HetNets paradigm allows for dynamic traffic splitting across radio access technologies for each client, and then for aggregating the traffic inside the network to improve the overall resource utilization. We focus on the max-min fair service rate allocation across the clients, and study the properties of the optimal solution. Based on the analysis, we design a low complexity distributed algorithm that tries to achieve max-min fairness. We also design a hybrid network architecture that leverages opportunistic centralized network supervision to augment the distributed solution. We analyze the performance of our proposed algorithms and prove their convergence. We also derive conditions under which the outcome is optimal. When the conditions are not satisfied, we provide constant upper and lower bounds on the optimality gap. Finally, we study the convergence time of our distributed solution and show that leveraging appropriate policies in its design significantly reduces the convergence time.

We study the dynamics of network selection in heterogeneous wireless networks based on client-side control. Clients in such networks selfishly select the best radio access technology (RAT) that maximizes their own throughputs. We study two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, and 3G) networks. Formulating the problem as a non-cooperative game, we study its existence of equilibria, convergence time, efficiency, and practicality. Our results reveal that: 1) single-class RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes; 2) we provide tight bounds on the convergence time of these games; 3) we analyze the Pareto-efficiency of the Nash equilibria of these games, deriving the conditions under which Nash equilibria are Pareto-optimal, and quantifying the distance of equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; and 4) with extensive measurement-driven simulations, we show that RAT selection games converge to Nash equilibria in a small number of steps, and are amenable to practical implementation. We also investigate the impact of noisy throughput estimates, and propose solutions to handle them.

The use of extremely high frequency (EHF) or millimeter-wave (mmWave) band has attracted significant attention for the next generation wireless access networks. As demonstrated by recent measurements, mmWave frequencies render themselves quite sensitive to “blocking” caused by obstacles like foliage, humans, vehicles, etc. However, there is a dearth of analytical models for characterizing such blocking and the consequent effect on the signal reliability. In this paper, we propose a novel, general, and tractable model for characterizing the blocking caused by humans (assuming them to be randomly located in the environment) to mmWave propagation as a function of system parameters like transmitter-receiver locations and dimensions, as well as density and dimensions of humans. Moreover, the proposed model is validated using a ray-launcher tool. Utilizing the proposed model, the blockage probability is shown to increase with human density and separation between the transmitter-receiver pair. Furthermore, the developed analysis is shown to demonstrate the existence of a transmitter antenna height that maximizes the received signal strength, which in turn is a function of the transmitter-receiver distance and their dimensions.

We present the design and implementation of FD2, a directional full-duplex (FD) communication system for indoor wireless networks. An FD2AP uses directional transmit and receive antennas to reduce self-interference, and to combat AP-AP and client-client interferences that arise due to FD operation in multi-cell networks. FD2addresses the joint problem of scheduling and beam selection by proposing efficient practical algorithms. FD2is implemented on the WARP platform, and its performance is compared against CSMA/CA and other FD and directional communication systems. Our experimental results reveal that: (i) Simple application of FD to multi-cell networks can result in significant loss of capacity due to high FD induced interference, while FD2can effectively overcome the problem and provide an average gain of ninefold; (ii) FD2's performance depends on the hardware capture properties and the corresponding rate table, and increases when packets can be captured at lower SINR margins, or when dynamic range of the rate table is high; and (iii) FD2's uplink and downlink performances are susceptible to channel dynamics, and are impacted differently due to mobility. However, we show that training FD2's rates according to traffic direction, mobility, and feedback rate, increases its robustness to channel dynamics.

We study the convergence properties of distributed network selection in HetNets with priority-based service. Clients in such networks have different priority weights (e.g., QoS requirements, scheduling policies, etc.) for different access networks and act selfishly to maximize their own throughput. We formulate the problem as a non-cooperative game, and study its convergence for two models: (i) A purely client-centric model where each client uses its own preference to select a network, and (ii) a hybrid client-network model that uses a combination of client and network preferences to arrive at pairings. Our results reveal that: (a) Pure client-centric network selection with generic weights can result in infinite oscillations for any improvement path (i.e., shows strongly cyclic behavior). However, we show that under several classes of practical priority weights (e.g., weights that achieve different notions of fairness) or under additional client-side policies, convergence can be guaranteed; (b) We study convergence time under client-centric model and provide tight polynomial and linear bounds; (c) We show that applying a minimal amount of network control in the hybrid model, guarantees convergence for clients with generic weights. We also introduce a controllable knob that network controller can employ to balance between convergence time and its network-wide objective with predictable tradeoff.

In anticipation of the increasing demand of wireless traffic, WiFi standardization efforts have recently focused on two key technologies for capacity improvement: multi-user MIMO and wider bandwidth. However, users experience heterogeneous channel orthogonality characteristics across sub-carriers in the same channel bandwidth, which prevents ideal multi-user gain. Moreover, frequency selectivity increases as bandwidth scales and correspondingly severely deteriorates multi-user MIMO performance. In this work, we consider the frequency selectivity of current and emerging WiFi channel bandwidths to optimize multi-user MIMO by dividing the occupied channel bandwidth into equally-sized sub-channels according to the level of frequency selectivity. In our selectivity-aware multi-user MIMO design, SAMU, each sub-channel is allocated according to the largest bandwidth that can be considered frequency-flat, and an optimal subset of users is chosen to serve in each sub-channel according to spatial orthogonality, achieving a significant performance improvement for all users in the network. Additionally, we propose a selectivity-aware very high throughput (SA-VHT) mode, which is based on and an extension to the existing IEEE 802.11ac standard. Over emulated and real indoor channels, even with minimal mobility, SAMU achieves as much as 80 percent throughput improvement compared to existing multi-user MIMO schemes, which could serve as a lower bound as bandwidth scales.

Frequently, client-side wireless devices have a view of multiple WiFi access points, whether from open residential and commercial networks, corporate networks, or mesh networks. Given the increasing number of radios and antennas in today's wireless devices, residual capacity from these multiple APs could be leveraged if client devices communicate with multiple APs simultaneously. In this paper, we exploit multi-user multi-input multi-output (MU-MIMO) technology to improve throughput and reliability in both directions of a wireless connection. For uplink, we use multi-user beamforming to enable the client devices to send multiple data streams to multiple APs simultaneously. For downlink, we leverage interference nulling technology to allow the client devices to decode parallel packets from multiple APs. This iBeam system requires no changes to existing APs or backhaul networks and is compatible with the IEEE 802.11 standards. We experimentally evaluate iBeam and show significant throughput improvements over both single-AP connections and multi-AP connections in a time division mode. The client's reliability and stability are also significantly improved due to the multi-AP diversity gain.

With the growing demand for increased spectral efficiencies, there has been renewed interest in enabling full-duplex communications. However, existing approaches to enable full-duplex require a clean-slate approach to address the key challenge in full-duplex, namely self-interference suppression. This serves as a big deterrent to enabling full-duplex in existing cellular networks. Towards our vision of enabling full-duplex in legacy cellular, specifically LTE networks, with no modifications to existing hardware at BS and client as well as technology specific industry standards, we present the design of our experimental system FD-LTE, that incorporates a combination of passive SI cancellation schemes, with legacy LTE half-duplex BS and client devices. We build a prototype of FD-LTE, integrate it with LTE's evolved packet core and conduct over-the-air experiments to explore the feasibility and potential for full-duplex with legacy LTE networks. We report promising experimental results from FD-LTE, which currently applies to scenarios with limited ranges that is typical of small cells.

We study the dynamics of network selection in heterogeneous wireless networks (HetNets). Users in such networks selfishly select the best radio access technology (RAT) with the objective of maximizing their own throughputs. We propose two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, 3G) networks. Next, we formulate the problem as a non-cooperative game, and study its convergence, efficiency, and practicality. Our results reveal that: (i) Single-class RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes. We next introduce a hysteresis mechanism in RAT selection games, and prove that with appropriate hysteresis policies, convergence can still be guaranteed; (ii) We analyze the Pareto-efficiency of the Nash equilibria of these games. We derive the conditions under which Nash equilibria are Pareto-optimal, and we quantify the distance of Nash equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; (iii) Finally, with extensive measurement-driven simulations we show that RAT selection games converge to Nash equilibria in a small number of steps, and hence are amenable to practical implementation. We also investigate the impact of noisy throughput measurements, and propose solutions to handle them.

We present the design and implementation of ADAM, the first adaptive beamforming-based multicast system and experimental framework for indoor wireless environments. ADAM addresses the joint problem of adaptive beamformer design at the PHY layer and client scheduling at the MAC layer by proposing efficient algorithms that are amenable to practical implementation. ADAM is implemented on a field programmable gate array (FPGA) platform, and its performance is compared against that of omnidirectional and switched beamforming based multicast. Our experimental results reveal that: 1) switched multicast beamforming has limited gains in indoor multipath environments, whose deficiencies can be effectively overcome by ADAM to yield an average gain of threefold; 2) the higher the dynamic range of the discrete transmission rates employed by the MAC hardware, the higher the gains in ADAM's performance, yielding up to ninefold improvement over omni with the 802.11 rate table; and 3) finally, ADAM's performance is susceptible to channel variations due to user mobility and infrequent channel information feedback. However, we show that training ADAM's signal-to-noise ratio (SNR)-rate mapping to incorporate feedback rate and coherence time significantly increases its robustness to channel dynamics.

A class of carrier sense multiple access (CSMA) protocols used in a broad range of wireless applications uses synchronized contention where nodes periodically contend at intervals of fixed duration. While several models exist for asynchronous CSMA contention used in protocols like IEEE 802.11 MAC, no model exists for synchronized CSMA contention that also incorporates realistic factors like clock drifts. In this paper, we introduce a model that quantifies the interplay of clock drifts with contention window size, control packet size, and carrier sense regulated by usage of guard time. Using a field programmable gate array (FPGA)-based MAC protocol implementation and controlled experiments on a wireless testbed, we evaluate the model predictions on the isolated and combined impact of these key performance factors to per-flow throughput and fairness properties in both single-hop and multihop networks. Our model and experimental evaluation reveal conditions on protocol parameters under which the throughput of certain flows can exponentially decrease; while at the same time, it enables solutions that can offset such problems in a predictable manner.

We present the design and implementation of ADAM, the first adaptive beamforming based multicast system and experimental framework for indoor wireless environments. ADAM addresses the joint problem of adaptive beamformer design at the PHY layer and client scheduling at the MAC layer by proposing efficient algorithms that are amenable to practical implementation. ADAM is implemented on an FPGA platform and its performance is compared against that of omni-directional and switched beamforming based multicast. Our experimental results reveal that (i) switched multicast beamforming has limited gains in indoor multi-path environments, whose deficiencies can be effectively overcome by ADAM to yield an average gain of three-fold; (ii) the higher the dynamic range of the discrete transmission rates employed by the MAC hardware, the higher the gains in ADAM's performance, yielding upto nine-fold improvement over omni with the 802.11 rate table; and (iii) finally, ADAM's performance is susceptible to channel variations due to user mobility and infrequent channel information feedback. However, we show that training ADAM's SNR-rate mapping to incorporate feedback rate and coherence time significantly increases its robustness to channel dynamics.

Given that full duplex (FD) and MIMO both employ multiple antenna resources, an important question that arises is how to make the choice between MIMO and FD? We show that optimal performance requires a combination of both to be used. Hence, we present the design and implementation of MIDU, the first MIMO full duplex system for wireless networks. MIDU employs antenna cancellation with symmetric placement of transmit and receive antennas as its primary RF cancellation technique. We show that MIDU's design provides large amounts of self-interference cancellation with several key advantages: (i) It allows for two stages of additive antenna cancellation in tandem, to yield as high as 45 dB self-interference suppression; (ii) It can potentially eliminate the need for other forms of analog cancellation, thereby avoiding the need for variable attenuator and delays; (iii) It easily scales to MIMO systems, therefore enabling the coexistence of MIMO and full duplex. We implemented MIDU on the WARP FPGA platform, and evaluated its performance against half duplex (HD)-MIMO. Our results reveal that, with the same number of RF chains, MIDU can potentially double the throughput achieved by half duplex MIMO in a single link; and provide median gains of at least 20% even in single cell scenarios, where full duplex encounters inter-client interference. Based on key insights from our results, we also highlight how to efficiently enable scheduling for a MIDU node.

Contending flows in multihop 802.11 wireless networks compete with two fundamental asymmetries: 1) channel asymmetry, in which one flow has a stronger signal, potentially yielding physical layer capture; and 2) topological asymmetry, in which one flow has increased channel state information, potentially yielding an advantage in winning access to the channel. Prior work has considered these asymmetries independently with a highly simplified view of the other. However, in this paper, we perform thousands of measurements on coupled flows in urban environments and build a simple yet accurate model that jointly considers information and channel asymmetries. We show that if these two asymmetries are not considered jointly, throughput predictions of even two coupled flows are vastly distorted from reality when traffic characteristics are only slightly altered (e.g., changes to modulation rate, packet size, or access mechanism). These performance modes are sensitive not only to small changes in system properties, but also small-scale link fluctuations that are common in an urban mesh network. We analyze all possible capture relationships for two-flow subtopologies and show that capture of the reverse traffic can allow a previously starving flow to compete fairly. Finally, we show how to extend and apply the model in domains such as modulation rate adaptation and understanding the interaction of control and data traffic.

Contending flows in multi-hop 802.11 wireless networks compete with two fundamental asymmetries: (i) channel asymmetry, in which one flow has a stronger signal, potentially yielding physical layer capture, and (ii) topological asymmetry, in which one flow has increased channel state information, potentially yielding an advantage in winning access to the channel. Prior work has considered these asymmetries independently with a highly simplified view of the other. However, in this work, we perform thousands of measurements on coupled flows in urban environments and build a simple, yet accurate model that jointly considers information and channel asymmetries. We show that if these two asymmetries are not considered jointly, throughput predictions of even two coupled flows are vastly distorted from reality when traffic characteristics are only slightly altered (e.g., changes to modulation rate, packet size, or access mechanism). These performance modes are sensitive not only to small changes in system properties, but also small-scale link fluctuations that are common in an urban mesh network. We analyze all possible capture relationships for two-flow sub-topologies and show that capture of the reverse traffic can allow a previously starving flow to compete fairly. Finally, we show how to extend and apply the model in domains such as modulation rate adaptation and understanding the interaction of control and data traffic.

Multi-User MIMO promises to increase the spectral efficiency of next generation wireless systems and is currently being incorporated in future industry standards. Although a significant amount of research has focused on theoretical capacity analysis, little is known about the performance of such systems in practice. In this paper, we present the design and implementation of the first multi-user beamforming system and experimental framework for wireless LANs. Using extensive measurements in an indoor environment, we evaluate the impact of receiver separation distance, outdated channel information due to mobility and environmental variation, and the potential for increasing spatial reuse. For the measured indoor environment, our results reveal that two receivers achieve close to maximum performance with a minimum separation distance of a quarter of a wavelength. We also show that the required channel information update rate is dependent on environmental variation and user mobility as well as a per-link SNR requirement. Assuming that a link can tolerate an SNR decrease of 3 dB, the required channel update rate is equal to 100 and 10 ms for non-mobile receivers and mobile receivers with a pedestrian speed of 3 mph respectively. Our results also show that spatial reuse can be increased by efficiently eliminating interference at any desired location; however, this may come at the expense of a significant drop in the quality of the served users.

A class of CSMA protocols used in a broad range of wireless applications uses synchronized contention where nodes periodically contend at intervals of fixed duration. While several models exist for asynchronous CSMA contention used in protocols like IEEE 802.11 MAC, no model exists for synchronized CSMA contention that also incorporates realistic factors like clock drifts. In this paper, we introduce a model that quantifies the interplay of clock drifts with contention window size, control packet size, and carrier sense regulated by usage of guard time. Using an FPGA-based MAC protocol implementation and controlled experiments on a wireless testbed we evaluate the model predictions on the isolated and combined impact of these key performance factors to per-flow throughput and fairness properties in both single-hop and multi-hop networks. Our model and experimental evaluation reveal conditions on protocol parameters under which the throughput of certain flows can exponentially decrease; while at the same time, it enables solutions that can offset such problems in a predictable manner.

This paper addresses channel assignment and random medium access design for single-radio multi-channel mesh networks. Two prior approaches include: (i) designing MAC protocols that dynamically select channels based on local information and (ii) partitioning the mesh into subnetworks with different channels and using 802.11 as the medium access protocol. Both of these approaches suffer from limited throughput improvement; the first approach due to wrong or incomplete channel state information that inherently arises in a multi-hop wireless environment, while the second approach due to high interference within each subnetwork. In this paper, we first introduce D1C-CA, Distance-1 Constrained Channel Assignment. D1C-CA statically assigns channels to a set of links as a function of physical connectivity, contention, and the unique gateway functionality of mesh networks, i.e, all Internet (non-local) traffic has a gateway node as its source or destination. To design D1C-CA, we model the channel assignment problem as a new form of graph edge coloring in which edges at distance one are constrained. We prove that the problem is NP-complete and design an efficient heuristic solution for mesh networks. Second, we design an asynchronous control-channel-based MAC protocol that solves multi-channel coordination problems and employs the proposed channel assignment algorithm. Finally, we investigate the performance of our approach through extensive simulations and show considerable performance improvements compared to alternate schemes.