Zhangyu Guan

Distributed spectrum management and relay selection in interference-limited cooperative wireless networks

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign (i) portions of the spectrum and (ii) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate. In this article, we make a significant contribution in this direction. First, we mathematically formulate the problem of joint spectrum management and relay selection for a set of sessions concurrently utilizing an interference-limited infrastructure-less wireless network. We then study distributed solutions to this (nonlinear and nonconvex) problem. The overall problem is separated into two subproblems, (i) spectrum management through power allocation with given relay selection strategy, and (ii) relay selection for a given spectral profile. Distributed solutions for each of the two subproblems are proposed, which are then analyzed based on notions from variational inequality (VI) theory. The distributed algorithms can be proven to converge, under certain conditions, to VI solutions, which are also Nash equilibrium (NE) solutions of the equivalent NE problems. A distributed algorithm based on iterative solution of the two subproblems is then designed. Performance and price of anarchy of the distributed algorithm are then studied by comparing it to the globally optimal solution obtained with a centralized algorithm. Simulation results show that the proposed distributed algorithm achieves performance that is within a few percentage points of the optimal solution.

CU-LTE: Spectrally-efficient and fair coexistence between LTE and Wi-Fi in unlicensed bands

To cope with the increasing scarcity of spectrum resources, researchers have been working to extend LTE/LTE-A cellular systems to unlicensed bands, leading to so-called unlicensed LTE (U-LTE). However, this extension is by no means straightforward, primarily because the radio resource management schemes used by LTE and by systems already deployed in unlicensed bands are incompatible. Specifically, it is well known that coexistence with scheduled systems like LTE degrades considerably the throughput of Wi-Fi networks that are based on carrier-sense medium access schemes. To address this challenge, we propose for the first time a cognitive coexistence scheme to enable spectrum sharing between U-LTE and Wi-Fi networks, referred to as CU-LTE. The proposed scheme is designed to jointly determine dynamic channel selection, carrier aggregation and fractional spectrum access for U-LTE networks, while guaranteeing fair spectrum access for Wi-Fi based on a newly designed cross-technology fairness criterion. We first derive a mathematical model of the spectrum sharing problem for the coexisting networks; we then design a solution algorithm to solve the resulting fairness constrained mixed integer nonlinear optimization problem. The algorithm, based on a combination of branch and bound and convex relaxation techniques, maximizes the network utility with guaranteed optimality precision that can be set arbitrarily to 1 at the expense of computational complexity. Performance evaluation indicates that near-optimal spectrum access can be achieved with guaranteed fairness between U-LTE and Wi-Fi. Issues regarding implementation of CU-LTE are also discussed.

Distributed Geometric-Programming-Based Power Control in Cellular Cognitive Radio Networks

Power control is critical for wireless communications that allow spectrum sharing among secondary users and primary users. In this paper, we derive an optimal distributed power control strategy aiming at the total capacity maximization of secondary network with interference constraints to primary users. Due to the nonconvexity of system utility, geometric programming is introduced to transform nonconvex optimization problems into convex optimization problems. Furthermore, system utility is usually coupled which means each utility depends not only on its local variables but also on the variables of other utilities. We introduce auxiliary variables and extra equality constraints to transfer the coupling in utility to coupling in constraints. The solution of the proposed power control strategy is shown to be globally optimal and leads to excellent performance.

On the Effect of Cooperative Relaying on the Performance of Video Streaming Applications in Cognitive Radio Networks

The problem of optimal resource allocation to share high-quality multimedia content in cognitive ad hoc networks with cooperative relays is addressed in this paper. Cooperative transmission is a promising technique to increase the capacity of wireless links by exploiting spatial diversity without multiple antennas at each node. However, mainstream research in this field focuses on optimizing physical layer performance measures, with little consideration for application-specific and network-wide performance measures. In this paper, the problem of joint video encoding rate control, power control, relay selection and channel assignment is formulated as a mixed-integer nonlinear problem(MINLP), and a solution algorithm based on a combination of the branch and bound framework and convex relaxation techniques is then proposed. The proposed solution jointly allocates channel, power, video encoding rate, and relay nodes for secondary users to maximize the video quality under the constraints posed by delay-sensitive video applications. Performance evaluation results show that cognitive networks with cooperative relaying can provide considerably higher video quality (in terms of the average peak signal-to-noise ratio (PSNR)) than solutions that do not rely on cooperation or without dynamic spectrum allocation.

Jointly optimal rate control and relay selection for cooperative wireless video streaming

Physical-layer cooperation allows leveraging the spatial diversity of wireless channels without requiring multiple antennas on a single device. However, most research in this field focuses on optimizing physical-layer metrics, with little consideration for network-wide and application-specific performance measures. This paper studies cross-layer design techniques for video streaming over cooperative networks. The problem of joint rate control, relay selection, and power allocation is formulated as a mixed-integer nonlinear problem, with the objective of maximizing the sum peak signal-to-noise ratio (PSNR) of a set of concurrent video sessions. A global optimization algorithm based on the branch and bound framework and on convex relaxation of nonconvex constraints is then proposed to solve the problem. The proposed algorithm can provide a theoretical upper bound on the achievable video quality and is shown to provably converge to the optimal solution. In addition, it is shown that cooperative relaying allows nodes to save energy without leading to a perceivable decrease in video quality. Based on this observation, an uncoordinated, distributed, and localized low-complexity algorithm is designed, for which we derive conditions for convergence to a Nash equlibrium (NE) of relay selection. The distributed algorithm is also shown to achieve performance comparable in practice to the optimal solution.

Video Transmission Over Lossy Wireless Networks: A Cross-Layer Perspective

Video content currently makes up nearly half of the “fixed” Internet traffic and more than a third of the mobile traffic in North America, with most other regions showing similar trends. As mobile data rates continue to increase and more people rely on 802.11 wireless for home and commercial Internet access, the amount of video transmitted over at least one wireless hop will likely continue to increase. In addition, as cameras continue to become smaller and cheaper, the demand for video services in sensor and MANET networks will also increase. In this paper, we examine the state of the art of wireless video communication at each layer of the networking stack. We consider both existing and emerging technologies at each layer of the protocol stack as well as cross-layer designs, and discuss how these solutions can increase the video experience for the end user.

Optimal and fair resource allocation for multiuser wireless multimedia transmissions

This paper presents an optimal and fair strategy for multiuser multimedia radio resource allocation (RRA) based on coopetition, which suggests a judicious mixture of competition and cooperation. We formulate the co-opetition strategy as sum utility maximization at constraints from both Physical (PHY) and Application (APP) layers. We show that the maximization can be solved efficiently employing the well-defined Layering as Optimization Decomposition (LOD) method. Moreover, the coopetition strategy is applied to power allocation among multiple video users, and evaluated through comparing with existing- competition based strategy. Numerical results indicate that, the co-opetition strategy adapts the best to the changes of network conditions, participating users, and so forth. It is also shown that the coopetition can lead to an improved number of satisfied users, and in the meanwhile provide more flexible tradeoff between system efficiency and fairness among users.

Crowdsourcing Access Network Spectrum Allocation Using Smartphones

The hundreds of millions of deployed smartphones provide an unprecedented opportunity to collect data to monitor, debug, and continuously adapt wireless networks to improve performance. In contrast with previous mobile devices, such as laptops, smartphones are always on but mostly idle, making them available to perform measurements that help other nearby active devices make better use of available network resources. We present the design of PocketSniffer, a system delivering wireless measurements from smartphones both to network administrators for monitoring and debugging purposes and to algorithms performing realtime network adaptation. By collecting data from smartphones, PocketSniffer supports novel adaptation algorithms designed around common deployment scenarios involving both cooperative and self-interested clients and networks. We present preliminary results from a prototype and discuss challenges to realizing this vision.

Optimizing cooperative video streaming in wireless networks

Physical-layer cooperation allows leveraging the spatial diversity of the wireless channel without requiring multiple antennas on a single device. However, most research in this field focuses on optimizing physical layer metrics, with little consideration for network-wide and application-specific performance measures. This paper studies cross-layer design techniques for video streaming over cooperative networks. The problem of joint video rate control, relay selection, and power allocation is formulated as a mixed-integer nonlinear problem, with the objective of maximizing the sum peak signal-to-noise ratio (PSNR) of a set of concurrent video sessions. An asynchronous, distributed and localized low-complexity algorithm is designed, based on the iterative solution of convex optimization problems at each individual node. In addition, a global-optimization centralized algorithm based on convex relaxations of non-convex constraints is also proposed as performance benchmark. The distributed algorithm is shown to achieve performance within a few percentage points of the optimal solution. It is also shown that cooperative relaying allows nodes to reduce the overall power consumption without leading to a perceivable decrease in video quality.

Distributed queueing games in interference-limited wireless networks

We study distributed queueing games in interference-limited ad-hoc wireless networks. We formulate system design as a Nash Equilibrium (NE) problem, where the users aim at maximizing their own throughput by choosing the optimal transmission threshold. We first derive conditions for the existence and uniqueness of the NE; then we propose a distributed best-response algorithm solving the game along with its convergence properties. A second contribution of the paper is to develop a Branch and Bound-based (centralized) algorithm solving the associated (nonconvex) social problem, which one can use as benchmark to evaluate the performance of the proposed game theoretical formulation. Interestingly, our numerical results show that the sum-throughput achievable at the NE of the proposed game are very close to that of the social problem, which validates our game theoretical formulation. The performance loss is not negligible only in high interference scenarios. For such cases, we proposed a pricing-based algorithm yielding sum-throughput solutions very close to the globally optimal ones, at the cost of very limited signaling among the users.