Distributed massive MIMO networks are envisioned to realize cooperative multi-point transmission in next-generation wireless systems. For efficient cooperative hybrid beamforming, the cluster of access points (APs) needs to obtain precise estimates of the uplink channel to perform reliable downlink precoding. However, due to the radio frequency (RF) impairments between the transceivers at the two en-points of the wireless channel, full channel reciprocity does not hold which results in performance degradation in the cooperative hybrid beamforming (CHBF) unless a suitable reciprocity calibration mechanism is in place. We propose a two-step approach to calibrate any two hybrid nodes in the distributed MIMO system. We then present and utilize the novel concept of reciprocal tandem to propose a low-complexity approach for jointly calibrating the cluster of APs and estimating the downlink channel. Finally, we validate our calibration technique's effectiveness through numerical simulation.

Ensuring high-quality video content for wireless users has become increasingly vital. Nevertheless, maintaining a consistent level of video quality faces challenges due to the fluctuating encoded bitrate, primarily caused by dynamic video content, especially in live streaming scenarios. Video compression is typically employed to eliminate unnecessary redundancies within and between video frames, thereby reducing the required bandwidth for video transmission. The encoded bitrate and the quality of the compressed video depend on encoder parameters, specifically, the quantization parameter (QP). Poor choices of en-coder parameters can result in reduced bandwidth efficiency and high likelihood of non-conformance. Non-conformance refers to the violation of the peak signal-to-noise ratio (PSNR) constraint for an encoded video segment. To address these issues, a real-time deep learning-based H.264 controller is proposed. This controller dynamically estimates the optimal encoder parameters based on the content of a video chunk with minimal delay. The objective is to maintain video quality in terms of PSNR above a specified threshold while minimizing the average bitrate of the compressed video. Experimental results, conducted on both QCIF dataset and a diverse range of random videos from public datasets, validate the effectiveness of this approach. Notably, it achieves improvements of up to 2.5 times in average bandwidth usage compared to the state-of-the-art adaptive bitrate video streaming, with a negligible non-conformance probability below 10−2.

Low-complexity estimation and correction of carrier frequency offset (CFO) are essential in orthogonal frequency division multiplexing (OFDM). In this paper, we propose a low-overhead blind CFO estimation technique based on cyclic prefix (CP), in multi-input multi-output (MIMO)-OFDM systems. We propose to use antenna diversity for CFO estimation. Given that the RF chains for all antenna elements at a communication node share the same clock, the carrier frequency offset (CFO) between two points may be estimated by using the combination of the received signal at all antennas. We improve our method by combining the antenna diversity with time diversity by considering the CP for multiple OFDM symbols. We provide a closed-form expression for CFO estimation and present algorithms that can considerably improve the CFO estimation performance at the expense of a linear increase in computational complexity. We validate the effectiveness of our estimation scheme via extensive numerical analysis.

Time Division Duplex (TDD)-based distributed massive MIMO systems are envisioned as candidate solution for the physical layer of 6G multi-antenna systems supporting cooperative hybrid beamforming that heavily relies on the obtained uplink channel estimates for efficient coherent downlink pre-coding. However, due to the hardware impairment between the transmitter and the receiver, full channel reciprocity does not hold between the downlink and uplink direction. Such reciprocity mismatch deteriorates the performance of mm-Wave hybrid beam-forming and has to be estimated and compensated for, to avoid performance degradation in the co-operative hybrid beamforming.In this paper, we address the channel reciprocity calibration between any two nodes at two levels. We decompose the problem into two sub-problems. In the first sub-problem, we calibrate the digital chain, i.e. obtain the mismatch coefficients of the (DAC/ADC) up to a constant scaling factor. In the second sub-problem, we obtain the (PA/LNA) mismatch coefficients. At each step, we formulate the channel reciprocity calibration as a least-square optimization problem that can efficiently be solved via conventional methods such as alternative optimization with high accuracy. Finally, we verify the performance of our channel reciprocity calibration approach through extensive numerical experiments.

Massive MIMO and hybrid beamforming are among the key physical layer technologies for the next generation wireless systems. In the last stage of the hybrid beamforming, the goal is to generate sharp beam with maximal and preferably uniform gain. We highlight the shortcomings of uniform linear arrays (ULAs) in generating such perfect beams, i.e., beams with maximal uniform gain and sharp edges, and propose a solution based on a novel antenna configuration, namely, twin-ULA (TULA). Consequently, we propose two antenna configurations based on TULA: Delta and Star. We pose the problem of finding the beamforming coefficients as a continuous optimization problem for which we find the analytical closed-form solution by a quantization/aggregation method. Thanks to the derived closed-form solution the beamforming coefficients can be easily obtained with low complexity. Through numerical analysis, we illustrate the effectiveness of the proposed antenna structure and beamforming algorithm to reach close-to-perfect beams.

In pursuance of the unused spectrum in higher frequencies, millimeter wave (mmWave) bands have a pivotal role. However, the high path-loss and poor scattering associated with mmWave communications highlight the necessity of employing effective beamforming techniques. In order to efficiently search for the beam to serve a user and to jointly serve multiple users it is often required to use a composite beam which consists of multiple disjoint lobes. A composite beam covers multiple desired angular coverage intervals (ACIs) and ideally has maximum and uniform gain (smoothness) within each desired ACI, negligible gain (leakage) outside the desired ACIs, and sharp edges. We propose an algorithm for designing such ideal composite codebook by providing an analytical closed-form solution with low computational complexity. There is a fundamental trade-off between the gain, leakage and smoothness of the beams. Our design allows to achieve different values in such trade-off based on changing the design parameters. We highlight the shortcomings of the uniform linear arrays (ULAs) in building arbitrary composite beams. Consequently, we use a recently introduced twin-ULA (TULA) antenna structure to effectively resolve these inefficiencies. Numerical results are used to validate the theoretical findings.

In-band full-duplex (FD) communication has emerged as one of the promising techniques to improve data rates in next generation wireless systems. Typical FD scenarios considered in the literature assume FD base stations (BSs) and half-duplex (HD) users activated either in uplink (UL) or downlink (DL), where inter-user interference (IUI) is treated as noise at the DL user. This paper considers more general FD scenarios where an arbitrary fraction of the users are capable of FD and/or they can perform successive interference cancellation (SIC) to mitigate IUI. Consequently, one user can be activated in either UL or DL (HD-UL and HD-DL modes), or simultaneously in both directions requiring self-interference mitigation (SIM) at that user (FD-SIM mode). Furthermore, two users can be scheduled, one in UL and the other in DL (both operating in HD), where the DL user can treat IUI as noise (FD-IN mode) or perform SIC to mitigate IUI (FD-SIC mode). This paper studies opportunistic mode selection and user scheduling under long-term and short-term temporal fairness in single-carrier and multi-carrier (OFDM) FD systems, with the goal of maximizing system utility (e.g. sum-rate). First, the feasible region of temporal demands is characterized for both long-term and short-term fairness. Subsequently, optimal temporal fair schedulers as well as practical low-complexity online algorithms are devised. Simulation results demonstrate that using SIC to mitigate IUI as well as having FD capability at users can improve FD throughput gains significantly especially, when user distribution is concentrated around a few hotspots.

Narrow beams are key to wireless communications in millimeter wave frequency bands. Beam alignment (BA) allows the base station (BS) to adjust the direction and width of the beam used for communication. During BA, the BS transmits a number of scanning beams covering different angular regions. The goal is to minimize the expected width of the uncertainty region (UR) that includes the angle of departure of the user. Conventionally, in interactive BA, it is assumed that the feedback corresponding to each scanning packet is received prior to transmission of the next one. However, in practice, the feedback delay could be larger because of propagation or system constraints. This paper investigates BA strategies that operate under arbitrary fixed feedback delays. This problem is analyzed through a source coding perspective where the feedback sequences are viewed as source codewords. It is shown that these codewords form a codebook with a particular characteristic which is used to define a new class of codes called d—unimodal codes. By analyzing the properties of these codes, a lower bound on the minimum achievable expected beamwidth is provided. The results reveal potential performance improvements in terms of the BA duration it takes to achieve a fixed expected width of the UR over the state-of-the-art BA methods which do not consider the effect of delay.

mmWave 5G networks promise to enable a new generation of networked applications requiring a combination of high throughput and ultra-low latency. However, in practice, mmWave performance scales poorly for large numbers of users due to the significant overhead required to manage the highly-directional beams. We find that we can substantially reduce or eliminate this overhead by using out-of-band infrared measurements of the surrounding environment generated by a LiDAR sensor. To accomplish this, we develop a ray-tracing system that is robust to noise and other artifacts from the infrared sensor, create a method to estimate the reflection strength from sensor data, and finally apply this information to the multiuser beam selection process. We demonstrate that this approach reduces beam-selection overhead by over 95% in indoor multi-user scenarios, reducing network latency by over 80% and increasing throughput by over 2× in mobile scenarios.

RFID applications for taking inventory and processing transactions in point-of-sale (POS) systems improve operational efficiency but are not designed to provide insights about customers' interactions with products. We bridge this gap by solving the proximity grouping problem to identify groups of RFID tags that stay in close proximity to each other over time. We design DeepTrack, a framework that uses deep learning to automatically track the group of items carried by a customer during her shopping journey. This unearths hidden purchase behaviors helping retailers make better business decisions and paves the way for innovative shopping experiences such as seamless checkout (`a la Amazon Go). DeepTrack employs a recurrent neural network (RNN) with the attention mechanism, to solve the proximity grouping problem in noisy settings without explicitly localizing tags. We tailor DeepTrack's design to track not only mobile groups (products carried by customers) but also flexibly identify stationary tag groups (products on shelves). The key attribute of DeepTrack is that it only uses readily available tag data from commercial off-the-shelf RFID equipment. Our experiments demonstrate that, with only two hours training data, DeepTrack achieves a grouping accuracy of 98.18% (99.79%) when tracking eight mobile (stationary) groups.

Directional transmission patterns (a.k.a. narrow beams) are the key to wireless communications in millimeter wave (mmWave) frequency bands which suffer from high path loss and severe shadowing. In addition, the propagation channel in mmWave frequencies incorporates only a few number of spatial clusters requiring a procedure to align the corresponding narrow beams with the angle of departure (AoD) of the channel clusters. The objective of this procedure, called beam alignment (BA) is to increase the beamforming gain for subsequent data communication. Several prior studies consider optimizing BA procedure to achieve various objectives such as reducing the BA overhead, increasing throughput, and reducing power consumption. While these studies mostly provide optimized BA schemes for scenarios with a single active user, there are often multiple active users in practical networks. Consequently, it is more efficient in terms of BA overhead and delay to design multi-user BA schemes which can perform beam management for multiple users collectively. This paper considers a class of multi-user BA schemes where the base station performs a one shot scan of the angular domain to simultaneously localize multiple users. The objective is to minimize the average of expected width of remaining uncertainty regions (UR) on the AoDs after receiving users' feedbacks. Fundamental bounds on the optimal performance are analyzed using information theoretic tools. Furthermore, a BA optimization problem is formulated and a practical BA scheme, which provides significant gains compared to the beam sweeping used in 5G standard, is proposed.

Retailers are aiming to enhance customer experience by automating the checkout process. The key impediment here is the effort to manually align the product barcode with the scanner, requiring sequential handling of items without blocking the line-of-sight of the laser beam. While recent systems such as Amazon Go eliminate human involvement using an extensive array of cameras, we propose a privacy-preserving alternative, RFGo, that identifies products using passive RFID tags. Foregoing continuous monitoring of customers throughout the store, RFGo scans the products in a dedicated checkout area that is large enough for customers to simply walk in and stand until the scan is complete (in two seconds). Achieving such low-latency checkout is not possible with traditional RFID readers, which decode tags using one antenna at a time. To overcome this, RFGo includes a custom-built RFID reader that simultaneously decodes a tag's response from multiple carrier-level synchronized antennas enabling a large set of tag observations in a very short time. RFGo then feeds these observations to a neural network that accurately distinguishes the products within the checkout area from those that are outside. We build a prototype of RFGo and evaluate its performance in challenging scenarios. Our experiments show that RFGo is extremely accurate, fast and well-suited for practical deployment in apparel stores.

Millimeter-wave (mmWave) bands have the potential to enable significantly high data rates in wireless systems. In order to overcome intense path loss and severe shadowing in these bands, it is essential to employ directional beams for data transmission. Furthermore, it is known that the mmWave channel incorporates a few number of spatial clusters necessitating additional time to align the corresponding beams with the channel prior to data transmission. This procedure is known as beam training (BT). While a longer BT leads to more directional beams (equivalently higher beamforming gains), there is less time for data communication. In this paper, this trade-off is investigated for a time slotted system under practical constraints such as finite beamwidth resolution and discrete modulation and coding schemes. At each BT time slot, the access point (AP) scans a region of uncertainty by transmitting a probing packet and refines angle of arrival (AoA) estimate based on user equipment (UE) feedback. Given a total number time slots, the objective is to find the optimum allocation between BT and data transmission and a feasible beamwidth for the estimation of AoA at each BT time slot such that the expected throughput is maximized. It is shown that the problem satisfies the optimal substructure property enabling the use of a backward dynamic programming approach to find the optimal solution with polynomial computational complexity. Simulation results reveal that in practical scenarios, the proposed approach outperforms existing techniques such as exhaustive and bisection search.

Millimeter-wave (mmWave) bands have shown the potential to enable high data rates for next generation mobile networks. In order to cope with high path loss and severe shadowing in mmWave frequencies, it is essential to employ massive antenna arrays and generate narrow transmission patterns (beams). When narrow beams are used, mobile user tracking is indispensable for reliable communication. In this paper, a joint beam tracking and data communication strategy is proposed in which, the base station (BS) increases the beamwidth during data transmission to compensate for location uncertainty caused by user mobility. In order to evade low beamforming gains due to widening the beam pattern, a probing scheme is proposed in which the BS transmits a number of probing packets to refine the estimation of angle of arrival based on the user feedback, which enables reliable data transmission through narrow beams again. In the proposed scheme, time is divided into similar frames each consisting of a probing phase followed by a data communication phase. A steady state analysis is provided based on which, the duration of data transmission and probing phases are optimized. Furthermore, the results are generalized to consider practical constraints such as minimum feasible beamwidth. Simulation results reveal that the proposed method outperforms well-known approaches such as optimized beam sweeping.

We envision a flexible, dynamic airborne LTE infrastructure built upon Unmanned Autonomous Vehicles (UAVs) that will provide on-demand, on-time, network access, anywhere. In this paper, we design, implement and evaluate SkyRAN, a self-organizing UAV-based LTE RAN (Radio Access Network) that is a key component of this UAV LTE infrastructure network. SkyRAN determines the UAV's operating position in 3D airspace so as to optimize connectivity to all the UEs on the ground. It realizes this by overcoming various challenges in constructing and maintaining radio environment maps to UEs that guide the UAV's position in real-time. SkyRAN is designed to be scalable in that it can be quickly deployed to provide efficient connectivity even over a larger area. It is adaptive in that it reacts to changes in the terrain and UE mobility, to maximize LTE coverage performance while minimizing operating overhead. We implement SkyRAN on a DJI Matrice 600 Pro drone and evaluate it over a 90 000 m2 operating area. Our testbed results indicate that SkyRAN can place the UAV in the optimal location with about 30 secs of a measurement flight. On an average, SkyRAN achieves a throughput of 0.9 - 0.95X of optimal, which is about 1.5 - 2X over other popular baseline schemes.

Deploying LTE networks in unlicensed spectrum requires us to move beyond coexistence mechanisms and understand the suitability of LTE's synchronous operation in a spectrum that is governed by asynchronous access principles. Our study reveals a fundamental conflict in LTE uplink access that arises between the scheduled nature of LTE's multi-user transmissions -- critical for leveraging the diversity (OFDMA) and multiplexing (multi-user MIMO) gains -- and the asynchronous nature of interference on the clients. The result is a significant loss in spectrum utilization and throughput that scales with the number of interfering terminals.

In this paper we formulate a general link packing problem for mmWave networks. Each link is a 4-tuple determined by the choice of receiving user, transmitting access point, transmit beamforming vector and receive beamforming vector. The problem seeks to optimize the weighted sum over active links, where each link is allowed to have any arbitrarily chosen weight or priority and an active link must satisfy a minimum link quality threshold. Our formulation models a practical scenario in which blockages due to arbitrarily placed obstacles in the propagation environment are allowed to occur, where we note that mmWave transmissions are extremely susceptible to blockages. This is a key departure from the classical link packing problem where only the signal attenuation based on propagation distance is modeled. We exploit the sparsity induced by the directional nature of propagation due to beamforming, limited diffraction and the significant signal attenuation due to high path, penetration losses. We propose a novel technique that exploits this sparsity and considers an alternate formulation which is a column-sparse binary packing problem. This alternate formulation is in general conservative and we derive sufficient conditions under which it is equivalent to the original problem. We construct an efficient iterative algorithm and show that it outperforms other heuristics and guarantees a constant factor approximation for input instances that are likely to occur in mmWave networks.

In this paper, we design and develop RIO, a novel battery-free touch sensing user interface (UI) primitive for future IoT and smart spaces. RIO enables UIs to be constructed using off-the-shelf RFID readers and tags, and provides a unique approach to designing smart IoT spaces. With RIO, any surface can be turned into a touch-aware surface by simply attaching RFID tags to them. RIO also supports custom-designed RFID tags, and thus allows specially customized UIs to be easily deployed into a real-world environment. RIO is built using the technique of impedance tracking: when a human finger touches the surface of an RFID tag, the impedance of the antenna changes. This change manifests as a change in the phase of the RFID backscattered signal, and is used by RIO to track fine-grained touch movement over both off-the shelf and custom built tags. We study this impedance behavior in-depth and show how RIO is a reliable UI primitive that is robust even within a multi-tag environment. We leverage this primitive to build a prototype of RIO that can continuously locate a finger during a swipe movement to within 3 mm of its actual position. We also show how custom-design RFID tags can be built and used with RIO, and provide two example applications that demonstrate its real-world use.

The growing demand for data has driven the service providers (SPs) to provide differential treatment of traffic to generate additional revenue streams from content providers (CPs). While SPs currently only provide best-effort services to their CPs, it is plausible to envision a model in near future, where CPs are willing to sponsor quality of service for their content in exchange of sharing a portion of their profit with SPs. This quality sponsoring becomes invaluable especially when the available resources are scarce, such as in wireless networks, and can be accommodated in a non-neutral network. In this paper, we consider the problem of quality-sponsored data (QSD) in a non-neutral network. In our model, SPs allow CPs to sponsor a portion of their resources, and price it appropriately to maximize their payoff. The payoff of the SP depends on the monetary revenue and the satisfaction of end-users both for the non-sponsored and sponsored content, while CPs generate revenue through advertisement. Note that in this setting, end-users still pay for the data they use. We analyze the market dynamics and equilibria in two different frameworks, i.e., sequential and bargaining game frameworks, and provide strategies for: 1) SPs-to determine if and how to price resources and 2) CPs-to determine if and what quality to sponsor. The frameworks characterize different sets of equilibrium strategies and market outcomes depending on the parameters of the market.

The proliferation of smartphones and tablet devices is changing the landscape of user connectivity and data access from predominantly static users to a mix of static and mobile users. While significant advances have been made in wireless transmission strategies (e.g., beamforming and network MIMO) to meet the increased demand for capacity, such strategies primarily cater to static users. To cope with growing heterogeneity in data access, it is critical to identify and optimize strategies that can cater to users of various profiles to maximize system performance and more importantly, improve users' quality of experience. Toward this goal, we first show that users can be profiled into three distinct categories based on their data access (mobility) and channel coherence characteristics. Then, with real-world experiments, we show that the strategy that best serves users in these categories varies distinctly from one profile to another and belongs to the class of strategies that emphasize either multiplexing (e.g., network MIMO), diversity (e.g., distributed antenna systems) or reuse (e.g., conventional CSMA). Two key challenges remain in translating these inferences to a practical system, namely: 1) how to profile users and 2) how to combine strategies to communicate with users of different profiles simultaneously. In addressing these challenges, we present the design of TRINITY-a practical system that effectively caters to a heterogeneous set of users. We implement and evaluate a prototype of TRINITY on our WARP radio testbed. Our extensive experiments show that TRINITY's intelligent combining of transmission strategies improves the total network rate by 50%-150%, satisfies the QoS requirements of thrice as many users, and improves PSNR for video traffic by 10 dB compared with individual transmission strategies.

In this work, we explore the potential and impact of unlicensed LTE on WiFi in unlicensed spectrum. Our experiments demonstrate that the large asymmetry in the channel access methodologies employed by WiFi and LTE (carrier sensing/notification in WiFi, energy sensing alone in LTE-U), can result LTE-U completely blocking WiFi transmissions, and causing significant degradation to either technologies from collisions.

Small cells form a critical component of next generation cellular networks, where spatial reuse is the key to higher spectral efficiencies. Interference management in the spatial domain through beamforming allows for increased reuse without having to sacrifice resources in the time or frequency domain. Existing beamforming techniques for spatial reuse, being coupled with client scheduling, face a key limitation in practical realization, especially with OFDMA small cells. In this context, we argue that for a practical spatial reuse system with beamforming, it is important to decouple beamforming from client scheduling. Further, we show that jointly addressing client association with beamforming is critical to maximizing the reuse potential of beamforming. Towards our goal, we propose ProBeam - a system for multi-cell beamforming and client association in OFDMA small cell networks. ProBeam incorporates two key components - a low complexity, highly accurate SINR estimation module that helps determine interference dependencies for beamforming between small cells; and an efficient, low complexity joint client association and beam selection algorithm for the small cells that accounts for scheduling at the small cells without being coupled with it. We have prototyped ProBeam on a WiMAX-based network of four small cells. Our evaluations reveal the accuracy of our SINR estimation module to be within 1 dB, and the reuse gains from joint client association and beamforming to be as high as 115 percent over baseline approaches.

Power line communications (PLC) offer an immediate means of providing high bandwidth connectivity in settings where there is no in-built network infrastructure. While there is recent work on understanding physical and MAC layer artifacts of PLC, its applicability and performance in multi-flow settings is not well understood. We first undertake an extensive measurement study that sheds light on the properties of PLC that significantly affect performance in multi-flow settings. Using the understanding gained, we design BOLT, a framework that adopts a learning-based approach to effectively manage and orchestrate flows in a PLC network. BOLT is flexible and is agnostic to standards; it can be used to implement scheduling algorithms that target different performance goals. We implement BOLT on three different testbeds using off-the-shelf PLC adapters and showcase its ability to effectively manage flows, delivering several folds throughput improvement over state-of-the-art solutions.

We investigate the open problem of characterizing the multiplexing gain offered by FD in a network of M cells (compared to the gain of two available on a single link). While self-interference cancellation is fundamental in realizing full duplex (FD) capability, the more challenging problem in a network-wide deployment of FD communication is a new form of uplink-downlink interference, namely UDI, caused by transmission of uplink clients on the downlink reception of other clients operating in the same frequency band during FD. We leverage spatial interference alignment (IA) as an effective approach to address UDI and characterize the scalability of the FD's multiplexing gain (in terms of M) by providing a closed-form expression. To the best of our knowledge, this is the first characterization of FD's multiplexing gain in a multi-cell network. We also provide an IA construction that can achieve the best scaling possible. Further, we extend our results to practical settings with limited number of clients and limited information sharing between access points.

We consider the classical problem of spatial interference alignment (IA) in MIMO channels with constant channel coefficients through design of linear transmit precoders and receiver filters. Some easily (polynomial time) computable necessary conditions for IA have been derived in the literature [1], [2], [3]. Computable sufficient and necessary conditions that completely characterizes the feasibility of an IA problem have also been obtained [4], [2]. However, it has been shown that checking the feasibility of interference alignment when the number of antennas are more than two is NP-complete[3]. This result is inline with full characterization of the feasibility of IA as the sufficiency conditions require multiplication of Schubert cycles that becomes exhaustive as the dimensions grows. Naturally, the following questions may arise: “Is it possible to have a sufficiency condition for a general case of IA based on only the dimensions of the system (number of antennas at each node and degrees of freedom (DoF) per node [4]) that is simple (polynomial time) to compute?” and “How effective such sufficiency conditions would be?”. In this paper, we provide an affirmative answer to the first question and show the proposed sufficient condition is asymptotically optimal. The sufficiency conditions are expressed in terms of simple inequalities based on system dimensions. Unlike necessary conditions that are based on simple argument such as dimension counting [1], we have not yet been able to provide an elementary proof for the derived sufficiency conditions. The provided proof requires familiarity with Schubert calculus over complex Grassmannians.

Large-scale multiuser MIMO (MU-MIMO) systems have the potential for multi-fold scaling of network capacity. The research community has recognized this theoretical potential and developed architectures [1,2] with large numbers of RF chains. Unfortunately, building the hardware with a large number of RF chains is challenging in practice. CSI data transport and computational overhead of MU-MIMO beamforming can also become prohibitive under large network scale. Furthermore, it is difficult to physically append extra RF chains on existing communication equipments to support such large-scale MU-MIMO architectures.

To handle increased capacity demands, sophisticated MIMO-based transmission strategies, based on transmitter cooperation, have emerged. However, different types of users' channels (e.g., static vs mobile, stable vs dynamic channels) that make up today's enterprises, require different MIMO transmission strategies. With the wrong strategy, a user could even see a degradation in performance. Our overarching goal is to design and implement a framework, TRINITY, that can simultaneously cater to a heterogeneous mix of users, by intelligently combining a plurality of MIMO transmission strategies wherein the transmitters at different nodes can cooperate to deliver significant performance gains. Three key challenges that we address in building TRINITY are: (i) how to categorize users into channel profiles such that a single transmission strategy caters to the users of a profile, (ii) how to combine strategies to communicate with users of different profiles simultaneously, and (iii) what is the granularity of transmitter cooperation needed to balance efficiency with complexity. We implement and evaluate TRINITY on our WARP radio testbed. Our extensive experiments show that TRINITY's intelligent combining of transmission strategies improves the total network rate by 50%-150%, satisfies the QoS requirements of thrice as many users, and improves PSNR for video traffic by 10 dB compared to individual transmission strategies.

The growing demand for data has driven the Service Providers (SPs) to provide differential treatment of traffic to generate additional revenue streams from Content Providers (CPs). While SPs currently only provide best-effort services to their CPs, it is plausible to envision a model in near future, where CPs are willing to sponsor quality of service for their content in exchange of sharing a portion of their profit with SPs. In this paper, we introduce the problem of Quality-Sponsored Data (QSD) in cellular networks and study its implications on market entities in various scenarios. The direct coupling between the scarce wireless resources and the market decisions resulting from QSD is taken into account. In our model, SPs make a portion of their resources available for sponsorship by CPs, and price it appropriately to maximize their payoff, which depends on the monetary revenue and the satisfaction of end-users both for the non-sponsored and sponsored content, while CPs generate revenue through advertisement. We analyze the market dynamics and equilibria, and provide strategies for (i) SPs: to determine if and how to price resources, and (ii) CPs: to determine if and what quality to sponsor. We also discuss about the effects of different parameters of the model on market dynamics.

Next generation WLANs (802.11ac) are undergoing a major shift in their communication paradigm with the introduction of multi-user MIMO (MU-MIMO), transitioning from single-user to multi-user communications. We argue that the conventional AP deployment model of co-located antennas as well as their PHY and MAC mechanisms are not designed to realize the complete potential of MU-MIMO. We propose to leverage distributed antenna systems (DAS) to empower next generation 802.11ac networks. We highlight the multitude of benefits that DAS brings to MU-MIMO and 802.11ac in general. However, several challenges arise in the process of realizing these benefits in practice, where avoiding client modifications and making only minimal software modifications to APs is important to enable rapid adoption. Towards addressing these challenges, we present the design and implementation of MIDAS, the Multiple-Input Distributed Antenna System. MIDAS couples a DAS deployment of AP antennas with a suite of novel yet standards-compatible mechanisms at the PHY and MAC layers that best leverage the DAS deployment to maximize 802.11ac performance. Our WARP-based experimental evaluation demonstrates MIDAS's ability to significantly boost the performance of current 802.11ac design, demonstrating throughput gains over 802.11ac MU-MIMO for 100-200%, while remaining amenable to commercial adoption.

We consider the problem of interference alignment in vector subspaces of the available vector space at each communication node. We assume that the communication nodes are divided into two sets, the transmitting nodes and the receiving nodes. A full duplex node may be treated as two nodes split into the transmitting node and a receiving node. We consider only a part of communication network that is defined by the set of links from the transmitting node to receiving nodes that are considered to generate interference at their receiving nodes. The interference network is defined as this subset of the links and their transmitting and receiving nodes. The goal is to maximize the size of subspaces of the available vector space at each node that is interference free, i.e., at the transmitting node this subspace may be freely used for the transmission and at a receiving point this subspace may be freely used for reception. We provide a computable necessary and sufficient condition for the achievablility of such subspaces in an arbitrary network. therefore, given a topology for an interference network and the size of vector spaces available at each node (the number of antennas), we can provide a definite answer if a set of subspaces with a given size (degrees of freedom per node) is achievable or not.

Enabling wireless full-duplex (from an AP) with multiple half-duplex (HD) clients is key to widespread adoption of full-duplex (FD) in commercial networks. However, enabling FD in such networks is fundamentally challenged by a new form of uplink-downlink interference (UDI), arising between HD clients operating simultaneously in the uplink and downlink directions. In this context, we first show that spatial interference alignment (IA) between clients is an effective and scalable technique to address UDI and hence enable FD in these networks, especially in the presence of MIMO.

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.

The classical definition of degrees of freedom (DoF) deals with the degrees of a communication channel or multiple communication channels in the limit of high SNR. This can be interpreted as the number of independent streams that can be sent in each communication channel in the high SNR regime. We introduce the concept of DoF per communication node where at a transmitting node the DoF is the number of independent dimensions that can be used for transmission and at each receiver node the DoF is the number of independent dimensions that can be used for receiving data signals. In general the communication channels or links in a network can be divided into two sets: the interfering channels and the intended channels; hence, the network may be considered as an overlay of two networks, respectively: the interfering network and data-intended network. In the classical form, DoF is defined for channels in the data-intended network. We illustrate a new interpretation of DoF that depends only on the interfering network and can be formalized in full generality based on degrees of freedom per node in the network. While the classical DoF has been studied generally in the context of interference and X-channels, the per node DoF concept generalizes the idea to other possible networks. Using this generalized notion of DoF, this paper provides new results on DoF for different networks and also makes a connection to the classical definition of DoF defined in interference and X-channels.

Mobile operators are witnessing a dramatic increase in traffic spurred by a combination of popularity of smartphones, innovative applications and diverse services. As mobile traffic transitions from being voice dominated to video and data dominated, the revenue per byte for the mobile operators is declining at an unhealthy rate. To counter the traffic growth and build cost-effective networks, many operators are now forging alliances for RAN (Radio Access Network) sharing to improve coverage and capacity at reasonable investments and operational costs. This paper presents the design and implementation of NetShare, a network-wide radio resource management framework that provides effective RAN Sharing. NetShare introduces a novel two-level scheduler split between the mobile gateway and the cellular basestations to effectively manage and allocate the wireless resources of the radio access network composed of multiple basestations among multiple different entities (such as operators, content providers, etc.) that share the network. Firstly, NetShare provides performance isolation across entities with a minimum guaranteed resource allocation to each entity across the network. Secondly, NetShare optimally distributes the resources to each entity across the network proportional to the resource demand at each basestation. Through extensive LTE-based system simulations and prototype evaluations on a WiMAX testbed, we show the efficacy of NetShare in (a) providing isolation across entities and (b) efficiently distributing resources for each entity across the network thus achieving high utilization of resources for an entity.

As the growth of mobile video traffic outpaces that of cellular network speed, industry is adopting HTTP-based adaptive video streaming technology which enables dynamic adaptation of video bit-rates to match changing network conditions. However, recent measurement studies have observed problems in fairness, stability, and efficiency of resource utilization when multiple adaptive video flows compete for bandwidth on a shared wired link. Through experiments and simulations, we confirm that such undesirable behavior manifests itself in cellular networks as well. To overcome these problems, we design an in-network resource management framework, AVIS, that schedules HTTP-based adaptive video flows on cellular networks. AVIS effectively manages the resources of a cellular base station across adaptive video flows. AVIS also provides a framework for mobile operators to achieve a desired balance between optimal resource allocation and user quality of experience. AVIS has three key differentiating features: (1) It optimally computes the bit-rate allocation for each user, (2) It includes a scheduler and per-flow shapers to enforce bit-rate stability of each flow and (3) It leverages the resource virtualization technique to separate resource management of adaptive video flows from regular video flows. We implement a prototype system of AVIS and evaluate it on both a WiMAX network testbed and a LTE system simulator to show its efficacy and scalability.

Network MIMO (netMIMO) has potential for significantly enhancing the capacity of wireless networks with tight coordination of access points (APs) to serve multiple users concurrently. Existing schemes realize netMIMO by integrating distributed APs into one ``giant'' MIMO but do not scale well owing to their global synchronization requirement and overhead in sharing data between APs. To remedy this limitation, we propose a novel system, NEMOx, that realizes netMIMO downlink transmission for large-scale wireless networks. NEMOx organizes a network into practical-size clusters, each containing multiple distributed APs (dAPs) that opportunistically synchronize with each other for netMIMO downlink transmission. Inter-cluster interference is managed with a decentralized channel-access algorithm, which is designed to balance between the dAPs' cooperation gain and spatial reuse---a unique tradeoff in netMIMO. Within each cluster, NEMOx optimizes the power budgeting among dAPs and the set of users to serve, ensuring fairness and effective cancellation of cross-talk interference. We have implemented and evaluated a prototype of NEMOx in a software radio testbed, demonstrating its throughput scalability and multiple folds of performance gain over current wireless LAN architecture and alternative netMIMO schemes.

Small cells form a critical component of next generation cellular networks, where spatial reuse is the key to higher spectral efficiencies. Interference management in the spatial domain through beamforming allows for increased reuse without having to sacrifice resources in the time or frequency domain. Existing beamforming techniques for spatial reuse, being coupled with client scheduling, face a key limitation in practical realization, especially with OFDMA small cells. In this context, we argue that for a practical spatial reuse system with beamforming, it is important to decouple beamforming from client scheduling. Further, we show that jointly addressing client association with beamforming is critical to maximizing the reuse potential of beamforming.

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.

The proliferation of smartphones and tablet devices is changing the landscape of user connectivity and data access from predominantly static users to a mix of static and mobile users. While significant advances have been made in wireless transmission strategies (e.g., network MIMO) to meet the increased demand for capacity, such strategies primarily cater to static users. To cope with growing heterogeneity in data access, it is critical to identify and optimize strategies that can cater to users of various profiles to maximize system performance and more importantly, improve users' quality of experience.

Coordinated Multi-Point (CoMP) transmission is emerging as a concept that can substantially suppress interference, thus improving the capacity of multi-cell wireless networks. However, existing CoMP techniques either require sharing of data and channel state information (CSI) for all links in the network, or have limited capability of interference suppression. In this paper, we propose distributed interference alignment and cancellation (DIAC) to overcome these limitations. DIAC builds on a key intuition of interference locality — since each link is interfered with a limited number of neighboring links, it is sufficient to coordinate with those strong interferers and ignore others, in order to bound the overhead in CoMP. DIAC realizes the localized coordination by integrating interference cancellation and distributed interference alignment, and can be applied to both the uplink and downlink of multi-cell wireless networks. We validate DIAC using both model-driven and trace-based simulation where the traces are collected by implementing a MIMO-OFDM channel estimator on a software radio platform. Our experiments show that DIAC can substantially improve the degrees of freedom in multi-cell wireless networks.

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.

We consider the problem of network tomography in probabilistic tree mixture models. We invoke the theory of compressed sensing and prove that the distribution of a random communication network model with n nodes represented by a probabilistic mixture of k trees can be identified using low order routing summaries pertinent to groups of small sizes dlog k), then certain classes of inference algorithms can successfully determine the unknown model, i.e. the topologies of mixing trees and their corresponding probabilities. We show that a variation of ℓ1minimization over the space of all possible trees of n nodes can be used for this purpose. In addition, we propose a novel inference algorithm with a complexity polynomial in nlog k, with the same provable guarantee. The proposed model is applicable to practical situations such as ad-hoc and Peer-to-Peer(P2P) networks, and the presented inference method can lead to distributed protocols for network monitoring and tomography. In particular, we provide preliminary insight and numerical results on how the ideas are amenable to wireless sensor networks.

We consider a downlink multi-user multi-input-multi-output (MU-MIMO) fading channel wherein the base station can schedule several user terminals on the same time-frequency resource. A severe practical problem in MU-MIMO is that when computing its feedback report, a user does not have an accurate estimate of the interference it might see (if scheduled) from the signals intended for the other co-scheduled users. This results in a mismatch between the user reported signal-to-interference-plus-noise-ratio (SINR) and the one it actually observes in the aftermath of scheduling. To alleviate this problem we propose to inform each user (in a slow or semi-static manner) about the rank of the precoding matrix that it should report, along with an estimate of the total number of streams that the base station expects to co-schedule on a time-frequency resource. The suggested rank and the expected total number of streams can be user-specific and together convey the expected total number of co-scheduled interfering streams to the intended user. Each user then computes one or more SINRs for all the precoding matrices having the suggested rank and reports its preferred precoding matrix along with the corresponding SINRs. The SINRs are computed after assuming that the co-scheduled interfering streams will be transmitted along vectors isotropically distributed in the orthogonal complement of the range of the precoding matrix being examined. Alternatively, the SINRs can be computed after assuming that the co-scheduled interfering streams will be transmitted along the worst-case choice of mutually orthogonal vectors that lie in the orthogonal complement. We show that the proposed solutions, while requiring negligible additional signalling overhead, mitigate the mismatch problem to a large extent and result in significant improvements in system throughput.

In this paper, we consider the design of a binary network coding scheme for the wireless broadcast, in which the same packet sequence is broadcasted to multiple receivers. In the proposed coding scheme, a short block code is employed for the coding across the broadcasted packets to generate redundant packets to recover the lost packets for all terminals in the service. In particular, we assume the optimal decoder and consider the design of short block codes with a small number of redundancy. We present a residual graph based code design method and propose an iterative design algorithm with stochastic update. The short block code is then extended with an improved pseudo-random code to achieve the flexibility on redundancy. Numerical results show that the proposed coding scheme provides superior performance over other coding schemes.

Recent works have considered the feasibility of full duplex (FD) wireless communications in practice. While the first FD system by Choi et.al. relied on a specific antenna cancellation technique to achieve a significant portion of self-interference cancellation, the various limitations of this technique prompted latter works to move away from antenna cancellation and rely on analog cancellation achieved through channel estimation. However, the latter systems in turn require the use of variable attenuator and delay elements that need to be automatically tuned to compensate for the self-interference channel. This not only adds complexity to the overall system but also makes the performance sensitive to wide-band channels. More importantly, none of the existing FD schemes can be readily scaled to MIMO systems.

This paper studies the robust weighted-sum rate optimization problem in the presence of channel uncertainty over a K-user Gaussian Interference Channel (GIFC), where multiple antennas are present at all transmitters and receivers. Motivated by recent results on interference alignment that show the optimality of linear precoders and simple receivers in achieving the maximum degrees-of-freedom available in the GIFC, we consider linear transmit precoding and two simple decoding schemes: single-stream decoding and single-user decoding. The resulting precoder design problems are then posed as specific optimization problems. Unfortunately, due to the hardness of these problems, optimal solutions cannot be efficiently obtained. Instead of resorting to ad-hoc algorithms, we show that it is possible to design algorithms using a systematic approach. Towards this end, this paper develops new provably convergent iterative algorithms for precoder design through ingenious sub-problem formulations such that each of these sub-problems can be solved optimally. The sub-problems are solved in closed-form for certain cases and formulated as standard convex problems for the rest. To complement these contributions on achievable schemes, we generalize the genie-MAC outer bounding technique to incorporate channel uncertainty using notions of compound-MAC capacity and then obtain computable outer bounds using an alternating optimization approach. Thus, we introduce one of the first approaches to obtain tighter outer bounds on the capacity region of the GIFC in the presence of channel uncertainty.

In this paper we address the problem of multicasting individual and common information from a single transmitter (source) to multiple receivers (destinations). We consider the general case where a separate message could be intended for any subset of destinations. We call this problem multicasting in broadcast channel. We derive an achievable rate region for this setup when the `deterministic channel model' is employed. The achievable region is a function of link transfer functions and is characterized by a set of linear inequalities. We show that the obtained region is computable and convex. Moreover, we investigate the tightness of our achievable rate region for some well-known scenarios with up to three destinations. We verify that the derived multicast achievable regions for these particular cases coincide with the best known results in the literature which in fact characterize the multicast capacity regions for these scenarios.

The increasing popularity of applications involving group communications in wireless networks has led to the need for efficient multicasting solutions. The ability of smart antennas to beamform and hence improve the signal quality at the clients has made them especially attractive for multicasting applications. Unfortunately, the problem of multicast beamforming design is a non-convex optimization problem for which only suboptimal solutions has been proposed in the literature. In this work, we uncover a hidden convexity of the problem under certain channel conditions which happens frequently for practical systems with Rayleigh fading channel model. We propose a solution based on this observation which consists of two steps: (1) we solve the dual problem and check for a uniqueness condition; if satisfied, we show that the duality gap is zero and the solution of the primal problem is obtained based on the solution of the dual problem; (2) if the uniqueness condition is not satisfied, we use a gradient descent based approach. Our evaluations reveal that the proposed algorithm significantly improves the multicast performance over state-of-the-art solutions. Simulation results show that in most scenarios of interest the algorithm converges within a required limit in the first step with high probability. We also obtain the performance bounds for the primal problem based on the dual formulation.

The proliferation of mobile applications and services involving group communications has increased the need for efficient wireless multicasting solutions in next generation broadband access networks. The availability of antenna arrays at the base stations makes adaptive beamforming especially attractive for multicasting; with beamforming, the received signal strength at the clients can be significantly improved. However, the problem of designing optimal transmit beamformers for multicast applications is challenging, mainly due to its non-convex nature. In this work, we design efficient transmit beamformers for wireless link layer multicasting where instantaneous channel state information is available at the transmitter. In addressing the non-convexity of the problem, we design non-iterative, near-optimal beamforming algorithms based on the optimality conditions derived from its Lagrangian formulation. We show that for real channels, e.g, where Pulse Amplitude Modulation (PAM) is used, the optimal solution can be obtained. To address the effectiveness of the proposed solution for the complex channel, we also derive an upper bound on the multicast rate based on the dual formulation of the problem. Evaluations reveal that our algorithms deliver a performance that is close to the upper bound, while significantly improving the multicast rate over state of the art solutions.

In this paper, we consider a downlink (DL) multiuser (MU) multi-input-multi-output (MIMO) channel with linear precoding where the base station simultaneously schedules several user terminals on the same frequency sub-band. We assume imperfect (or quantized) per-user channel state information at the base station and present two types of channel state information (CSI) reports from user terminals, namely, the CSI report that assumes the single-user (SU) MIMO transmissions and the enhanced CSI feedback report that assumes the MU-MIMO transmissions, and in the latter case we consider both uniform and nonuniform power allocations. To improve the MU-MIMO system performance, we propose signal-to-interference-plus-noise ratio (SINR) approximation techniques that utilize the quantized CSI available at the base station and improve the rate matching. We also introduce user pooling techniques which enable a reduction in feedback signaling overhead via per-user feedback mode selection. The proposed techniques also allow unconstrained user pairing at the base station scheduler and hence enable dynamic switching between SU and MU MIMO transmissions. The simulation results demonstrate the efficiency of the proposed MU-MIMO enhancement techniques.

We consider the problem of maximizing the weighted sum rate (WSR) in MIMO broadcast channel where the number of transmitted streams (ranks) is constrained. The problem is treated both with or without interference pre-compensation also known as dirty paper coding (DPC). The rank constrained problem is highly motivated by the practical consideration on the receiver complexity in current wireless systems such as LTE. We propose a unified algorithm based on fixed point iteration. The proposed approach has very fast convergence rate that usually converges to the minimal number of streams for each user and finds the corresponding optimal precoding matrix. Rank minimization is particularly desirable in practice. The order in which the users' streams are encoded is crucial when dirty paper coding is allowed. We prove that the optimal user ordering does not depend on the transmission rank constraints and is given only by the weight vector. Using simulations, we compare the performance of our proposed scheme with the best known algorithms in the literature and demonstrate the effect of rank constraints.

We consider the interference relay channel (IRC) where a single relay assists the communications between multiple source-destination links under the half-duplex (HD) constraint. We assume each source has an independent message and its transmitted signal may cause interference at the other destinations. We assume each node only estimates its backward channels and has no knowledge of its forward channels as well as the other links. The role of the relay is to generate signals to cooperate with the intended signal and mitigate the interference at all destinations. Specifically, we propose coding schemes and transmission strategies for 2-user AWGN IRC with two sourcedestination pairs. We compare the performance of the proposed IRC coding schemes with that of a TDMA strategy where each source-destination pair communicates in alternative time slots with the assistance of the relay. Our simulation results based on practical turbo codes as underlying constituent codes show that the proposed transmission strategies result in a significant performance improvement over TDMA strategy in terms of outage probability and throughput.

In this paper we introduce a novel linear network coding scheme, namely “rotate-and-add coding”, that possesses low encoding complexity and operates fundamentally different from the traditional network codes. This scheme can operate on a small field (e.g. F2), thereby, it alleviates the computational complexities due to multiplication and addition operations in large finite fields. The key idea is to function on a vector of symbols instead of working with a single symbol of a large field. Each node encodes its received vectors by simply rotationally shifting the vectors and then adding them, i.e., here the addition is done in vector form and the multiplication is replaced by rotation. We verify that the new scheme requires lower computation and overhead than the existing schemes. However, as the cost of reducing the complexity, it provides slightly smaller throughput.

We present a cross-layer design of transmitting scalable video streams from a base station to multiple clients over a shared fading wireless network by jointly considering the application layer information and the wireless channel conditions. We first design a long-term resource allocation algorithm that determines the optimal wireless scheduling policy in order to maximize the weighted sum of average video quality of all streams. We prove that our algorithm achieves the global optimum even though the problem is not concave in the parameter space. We then devise two on-line scheduling algorithms that utilize the results obtained by the long-term resource allocation algorithm for user and packet scheduling as well as video frame dropping strategy. We compare our schemes with existing video scheduling and buffer management schemes in the literature and simulation results show our proposed schemes significantly outperform existing ones.

A multi-hop relay network with multiple antenna terminals in a quasi-static slow fading environment is considered. The fundamental diversity-multiplexing gain tradeoff (DMT) is analyzed in the case of half-duplex relay terminals. While decode-and-forward (DF) relaying achieves the optimal DMT in the full-duplex relay scenario, it is shown that the dynamic decode-and-forward (DDF) protocol achieves the optimal DMT if the relay is constrained to half-duplex operation. For the latter case, static DF protocols are considered as well, and the corresponding DMT performance is shown to fall short of the optimal performance, which indicates that dynamic channel allocation is required for optimal DMT performance. The optimal DMT is expressed as the solution of a convex optimization problem and explicit DMT expressions are presented for some special cases. In the case of multiple relays, it is shown that the optimal diversity gain, which is achieved by exploiting the available "hop-diversity", is dominated by the neighboring two-hops with the minimum diversity gain.

In this work, a new framework for channel estimation in MIMO OFDM systems is provided. Sparse channel estimation refers to estimating the time domain channel impulse response by exploiting the fact that the channel has a very few nonzero taps. We formalize the problem and drive necessary and sufficient condition on the number of pilots for perfect channel recovery which leads to a L0 norm optimization problem. A practical suboptimal solution is proposed that is a modified orthogonal matching pursuit (OMP) which exploits the sparsity structure of the MIMO channel. The investigations reveal that the training overhead can be drastically reduced while maintaining the same accuracy as the current state of the art techniques.

We consider the transmission of multiple scalable video streams from a server to multiple users over a fading wireless channel. We first present a long-term resource allocation algorithm that determines the scheduling policy and the parameters used by the scheduling policy in order to maximize the weighted sum of PSNR of all video streams. We then present an online scheduling algorithm that utilizes the results obtained by the long-term resource allocation algorithm for user and packet scheduling as well as video frame dropping strategy. Simulation results show that our proposed scheduling scheme significantly improves the video quality compared to the best-known scheduling algorithms in the literature.

We consider a shared relay channel (SRC) where a single relay assists the communications between multiple source-destination links under the half-duplex (HD) constraint. Specifically, we derive lower and upper bounds on the capacity of 2-user AWGN MIMO SRC with two source-destination pairs. Two different coding strategies are presented based on super-position coding and on dirty paper coding, respectively. We compare the performance of the proposed SRC coding schemes with that of a TDMA strategy where each source-destination pair communicates in alternative time slots with the assistance of the relay. The proposed coding schemes for SRC results in significant performance improvement over TDMA strategy in terms of both ergodic capacity and outage probability.

We consider the sum-rate maximization via linear preceding in downlink MIMO-OFDM systems with quantized feedback. We address the preceding codebook design based on the capacity measure by introducing a new distance metric. We propose a codebook structure and its associated design algorithm that allows for significant reduction in the memory requirement and computational complexity in real-time system implementation. We then provide a system design approach comprising of four main ingredients: (i) a multi-rank beamforming (MRBF) scheme, (ii) an efficient CQI-based precoder selection algorithm, (iii) reduced feedback strategies, and (iv) novel channel quality indicator (CQI) combining. Our simulation results show that the proposed MRBF scheme can approach the precoding upper bounds with relatively few feedback bits. Moreover, with the same number of bits, the proposed scheme simultaneously achieves higher throughput and lower computational complexity in comparison to the other existing precoding schemes.

We treat the problem of designing low-density parity-check (LDPC) codes to approach the capacity of relay channels. We consider an efficient analysis framework that decouples the factor graph (FG) of aB-block transmission into successive partial FGs, each of which denotes a two-block transmission. We develop design methods to find the optimum code ensemble for the partial FG. In particular, we formulate the relay operations and the destination operations as equivalent virtual MISO and MIMO systems, and employ a binary symmetric channel (BSC) model for the relay node output. For AWGN channels, we further develop a Gaussian approximation for the detector output at the destination node. Jointly treating the relay and the destination, we analyze the performance of the LDPC-coded relay system using the extrinsic mutual information transfer(EXIT) chart technique. Furthermore, differential evolution is employed to search for the optimum code ensemble. Our results show that the optimized codes always outperform the regular LDPC codes with a significant gain; in the AWGN case, when Protocol-II is employed and the relay is close to the source, the optimized code performs within 0.1dB to the capacity bound.

We treat the problem of outage minimization via power control in MIMO systems with quantized feedback. We formulate the optimal quantized power control design for a general MIMO system and provide the numerical procedure for finding the optimal solution. Our results not only extend but also show the deficiency of the existing quantized power control schemes for MISO systems. We further propose a design based on the pre-evaluation of the packet error rate performance of practical MIMO systems employing short-length LDPC codes and QAM modulations and with possibly unreliable feedback links. It is demonstrated that with only a few bits of feedback, the resulting quantized power control strategy achieves considerable gain over a system without power control.

The authors consider the design of LDPC codes for cooperative relay systems in the half-duplex mode. The capacity of halfduplex relay channels has been studied previously but the design of good channel codes for such channels remains a challenging problem. Employing an efficient relay protocol, we transform the half-duplex relay code design problem into a problem of ratecompatible LDPC code design where different code segments experience different SNRs. The density evolution with conventional Gaussian approximation for single user channels, which assumes invariant SNR within one codeword, is not capable of accurately predicting the code performance for this system. Here we develop a density evolution with a modified Gaussian approximation that takes into account the SNR variation in one received codeword as well as the rate-compatibility constraint. We then optimize the code ensemble using a modified differential evolution procedure. Extensive simulations are carried out to demonstrate that the proposed algorithm offers more accurate prediction of code performance in half-duplex relay channels than the conventional methods, and the optimized codes achieve a significant gain over existing codes.

We treat the problem of quantization codebook design for MIMO precoding schemes. We propose a design criterion based on the capacity measure which is different from the maximum mutual-minimum-distance conventionally used to design the codebook. The latter criterion is derived from SNR maximization which is not necessarily the capacity- optimal quantization strategy. While the capacity expression does not directly define a valid distance metric for the design, a bound on the capacity expression can be used to formulate the quantization codebook design. We introduce a new distance metric on the Grassmanian manifold which is used to design the optimal precoder codebook. While the original formulation can be used for the (space) correlated channel model, we propose a differential codebook design in order to track changes in the channel statistics when the channel is correlated in time (or in frequency, e.g., across different tones in OFDM systems). Simulation results has shown considerable improvement through the proposed codebook design both for single user (SU-) and multiple user (MU-) MIMO systems.

In this study, we consider the design of LDPC codes for cooperative relay systems in half-duplex mode (namely, "cheap" relay) that are of practical interest. We transform the code design problem into the design of rate-compatible LDPC codes where the SNRs in different parts of one codeword are different. Due to the SNR variation, the conventional density evolution (DE) or extrinsic-mutual-information-transfer (EXIT) is not capable of accurately predicting the code performance. We develop a more refined definition of code ensembles and present a modified DE based algorithm related to the new relay code structure. Our results show that the proposed algorithm is more accurate than the conventional DE or EXIT in this case. We further employ the code optimization based on differential evolution. The optimized "cheap" relay code significantly outperforms existing codes.

The problem of throughput maximisation in a wireless multiple-input multiple-output (MIMO) system using a quantised feedback, which is an appropriate model for practical systems with limited feedback capacity, is considered. Unlike the ergodic capacity that can be achieved through power control only, maximising the throughput in the block fading channels is based on appropriate rate control strategy. The optimal quantised rate control design for general MIMO systems is formulated and a gradient descent search algorithm to find the optimal solution is employed. It is seen that the proposed quantised rate control scheme with only a few bits of feedback considerably improves the throughput of a MIMO system. With the same amount of feedback overhead, the proposed quantised rate control with constant power is compared with the optimal quantised power control strategy with an optimised constant rate, and the result demonstrates the importance of rate control in throughput maximisation. The effect of quantised rate control in MIMO systems employing different automatic repeat request schemes is also investigated.

In this paper, we consider practical methods to approach the theoretical performance limits in the fading relay channel under different assumptions of transmitter channel knowledge. Specifically, we consider two degrees of transmitter channel knowledge: 1) perfect feedback is available and power control is employed and 2) no channel state knowledge is available at the transmitters and only spatial power allocation is possible. First, when perfect feedback is available, the optimal power control policy determines the ultimate limits of performance for constant rate transmission in the slow fading environment. However, in practice, perfect channel knowledge is not possible at the transmitters due to the finite capacity of the feedback links. We find practical methods to approach this performance limit through the use of power control with finite rate feedback. The finite-rate feedback results are shown for the low-complexity, full-diversity amplify-and-forward (AF) protocol. Interestingly, we see that only a few feedback bits are needed to achieve most of the gains of the optimal perfect feedback power control algorithm. Second, we consider the performance limit when the transmitters have no channel state knowledge and derive the optimal spatial power allocation between the source and relay for a given sum power constraint for the AF protocol. For most practical cases of interest, equal power allocation between the source and relay is shown to be nearly optimal. Our work suggests that there is minimal power savings from using spatial power allocation at the transmitters. To obtain large performance improvements over constant power transmission, it is imperative to have feedback for each realization of the channel state to allow for temporal power control.

We introduce a generalized definition for "low-pass" filters that covers time-varying and nonlinear systems under the same umbrella. We show that the qualitative concept of signal smoothing can be made precise through the concept of contractions in probabilistic metric spaces. For illustration, we consider classical linear time-invariant low-pass filters, nonlinear median filters, and time-varying guaranteed maximum delay schedulers employed in communication systems.

In this paper, packet scheduling with maximum delay constraints is considered with the objective to minimize average transmit power over Gaussian channels. The main emphasis is on deriving robust schedulers which do not rely on the knowledge of the source arrival process. Towards that end, we first show that all schedulers (robust or otherwise) which guarantee a maximum queuing delay for each packet are equivalent to a time-varying linear filter. Using the connection between filtering and scheduling, we study the design of optimal power minimizing robust schedulers. Two cases, motivated by filtering connection, are studied in detail. First, a time-invariant robust scheduler is presented and its performance is completely characterized. Second, we present the optimal time-varying robust scheduler, and show that it has a very intuitive time water-filling structure. We also present upper and lower bounds on the performance of power-minimizing schedulers as a function of delay constraints. The new results form an important step towards understanding of the packet time-scale interactions between physical layer metric of power and network layer metric of delay

In this paper a new achievable rate region for the user cooperation channel is derived which exceeds the best-known result for this channel. Since the user cooperation channel includes many other known channels as a special case, the new rate region provides improved achievable rates for these cases. The most notable example is that of the Gaussian relay channel, for which we present a new closed form inner bound higher than the only known result for many channel conditions.

In this work, we show that in the wireless relay network, a tremendous savings in energy can be achieved by having side information at the transmitters and by employing power control. We present efficient protocols and the corresponding optimal power control policies that approach the universal lower bound on the outage probability of the block fading relay channel. Each of the proposed protocols have their own utility for specific channel conditions. However, a hybrid protocol between two known coding schemes is the best scheme for all channel conditions and is sufficient to approach the lower bound on outage probability. Unlike the single link channel, we show that exploiting the knowledge of the channel at the transmitters can significantly lower the outage even if the transmit powers at the source and relay have to be kept constant. In this case, it is also demonstrated that the lower bound on outage is closely followed by the outage probability of the hybrid protocol. Our results reveal that exploiting the right network protocol in conjunction with power control result in orders of magnitude savings in power over direct transmission for a target performance level.

We derive the ergodic capacity of discrete-time fading channel with additive Gaussian noise subject to both peak and average power constraint. The average power can be interpreted as the cost that we incur to achieve a certain rate. On the other hand, the motivation of this analysis comes from the fuel that there is also a peak power limitation in practical communication system. It is been shown that the optimal power adaption is no longer water-filling or constant power adaption which is the case where there is no limitation on the peak power. The numerical results show that the importance of peak power constraint becomes negligible for relatively low available average power, while it is limiting the capacity to be finite even as the available average power goes to infinity.

In this paper, we derive the capacity of the Gaussian degraded 'cheap' relay channel, consisting of nodes using 'cheap' radios operating in TDD mode when transmitting and sending in the same frequency band. The TDD model captures a practical limitation of most of the RF radios used in commercial wireless systems. The proof of achievability relies on a combination of superposition encoding and list decoding, while the converse is derived using the min-cut max-flow theorem for networks with 'cheap' nodes (previously derived by the authors). Even with 'cheap' radios, our capacity analysis shows that cooperative coding is beneficial and has a capacity advantage over direct transmission.