Wireless transmission using non-contiguous chunks of spectrum is becoming increasingly important due to a variety of scenarios such as: secondary users avoiding incumbent users in TV white space; anticipated spectrum sharing between commercial and military systems; and spectrum sharing among uncoordinated interferers in unlicensed bands. Multi-Channel Multi-Radio (MCMR) platforms and Non-Contiguous Orthogonal Frequency Division Multiple Access (NC-OFDMA) technology are the two commercially viable transmission choices to access these non-contiguous spectrum chunks. Fixed MC-MRs do not scale with increasing number of non-contiguous spectrum chunks due to their fixed set of supporting radio front ends. NC-OFDMA allows nodes to access these non-contiguous spectrum chunks and put null sub-carriers in the remaining chunks. However, nulling sub-carriers increases the sampling rate (spectrum span) which, in turn, increases the power consumption of radio front ends. Our work characterizes this trade-off from a cross-layer perspective, specifically by showing how the slope of ADC DAC power consumption versus sampling rate curve influences scheduling decisions in a multi-hop network. Specifically, we provide a branch and bound algorithm based mixed integer linear programming solution that performs joint power control, spectrum span selection, scheduling and routing in order to minimize the system power of multi-hop NC-OFDMA networks. We also provide a low complexity (O(E^2 M^2)) greedy algorithm where M and E denote the number of channels and links respectively. Numerical simulations suggest that our approach reduces system power by 30 over classical transmit power minimization based cross-layer algorithms.
Software defined radio (SDR) is a revolution in radio technology that promises unprecedented flexibility in radio communications and is viewed as an enabling technology for dynamic spectrum access. This paper investigates how to support user communication sessions by jointly considering power control, scheduling, and flow routing for an SDR-based multi-hop wireless network. We develop a formal mathematical model for scheduling feasibility under the influence of power control. This model extends existing protocol interference model for wireless networks and can be used for a broad class of problems where power control (and thus transmission range and interference range) is part of the optimization space. We formulate a cross-layer optimization problem encompassing power control, scheduling, and flow routing. Subsequently, we develop an efficient solution procedure based on branch-and-bound technique and convex hull relaxation. Using simulation results, we demonstrate the efficacy of the solution procedure and offer insights on the impact of power control on scheduling feasibility, bandwidth efficiency, and bandwidth-footprint product (BFP). Next generation fixed wireless broadband networks are being increasingly deployed as mesh networks in order to provide and extend access to the internet. These networks are characterized by the use of multiple orthogonal channels and nodes with the ability to simultaneously communicate with many neighbors using multiple radios (interfaces) over orthogonal channels. Networks based on the IEEE 802.11a b g and 802.16 standards are examples of these systems. However, due to the limited number of available orthogonal channels, interference is still a factor in such networks. In this paper, we propose a network model that captures the key practical aspects of such systems and characterize the constraints binding their behavior. We provide necessary conditions to verify the feasibility of rate vectors in these networks, and use them to derive upper bounds on the capacity in terms of achievable throughput, using a fast primal-dual algorithm. We then develop two link channel assignment schemes, one static and the other dynamic, in order to derive lower bounds on the achievable throughput. We demonstrate through simulations that the dynamic link channel assignment scheme performs close to optimal on the average, while the static link channel assignment algorithm also performs very well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives. Cognitive radio networks (CRNs) have the potential to utilize spectrum efficiently and are positioned to be the core technology for the next-generation multihop wireless networks. An important problem for such networks is its capacity. We study this problem for CRNs in the SINR (signal-to-interference-and-noise-ratio) model, which is considered to be a better characterization of interference (but also more difficult to analyze) than disk graph model. The main difficulties of this problem are two-fold. First, SINR is a nonconvex function of transmission powers; an optimization problem in the SINR model is usually a nonconvex program and NP-hard in general. Second, in the SINR model, scheduling feasibility and the maximum allowed flow rate on each link are determined by SINR at the physical layer. To maximize capacity, it is essential to follow a cross-layer approach, but joint optimization at physical (power control), link (scheduling), and network (flow routing) layers with the SINR function is inherently difficult. In this paper, we give a mathematical characterization of the joint relationship among these layers. We devise a solution procedure that provides a (1- ) optimal solution to this complex problem, where is the required accuracy. Our theoretical result offers a performance benchmark for any other algorithms developed for practical implementation. Using numerical results, we demonstrate the efficacy of the solution procedure and offer quantitative understanding on the interaction of power control, scheduling, and flow routing in a CRN. This paper studies how the capacity of a static multi-channel network scales as the number of nodes, n, increases. Gupta and Kumar have determined the capacity of single-channel networks, and those bounds are applicable to multi-channel networks as well, provided each node in the network has a dedicated interface per channel.In this work, we establish the capacity of general multi-channel networks wherein the number of interfaces, m, may be smaller than the number of channels, c. We show that the capacity of multi-channel networks exhibits different bounds that are dependent on the ratio between c and m. When the number of interfaces per node is smaller than the number of channels, there is a degradation in the network capacity in many scenarios. However, one important exception is a random network with up to O(log n) channels, wherein the network capacity remains at the Gupta and Kumar bound of Θ(W√noverlog n) bits sec, independent of the number of interfaces available at each node. Since in many practical networks, number of channels available is small (e.g., IEEE 802.11 networks), this bound is of practical interest. This implies that it may be possible to build capacity-optimal multi-channel networks with as few as one interface per node. We also extend our model to consider the impact of interface switching delay, and show that in a random network with up to O(log n) channels, switching delay may not affect capacity if multiple interfaces are used. Cognitive radio (CR) is a revolution in radio technology and is viewed as an enabling technology for dynamic spectrum access. This paper investigates how to design distributed algorithm for a future multi-hop CR network, with the objective of maximizing data rates for a set of user communication sessions. We study this problem via a cross-layer optimization approach, with joint consideration of power control, scheduling, and routing. The main contribution of this paper is the development of a distributed optimization algorithm that iteratively increases data rates for user communication sessions. During each iteration, there are two separate processes, a Conservative Iterative Process (CIP) and an Aggressive Iterative Process (AIP). For both CIP and AIP, we describe our design of routing, minimalist scheduling, and power control scheduling modules. To evaluate the performance of the distributed optimization algorithm, we compare it to an upper bound of the objective function, since the exact optimal solution to the objective function cannot be obtained via its mixed integer nonlinear programming (MINLP) formulation. Since the achievable performance via our distributed algorithm is close to the upper bound and the optimal solution (unknown) lies between the upper bound and the feasible solution obtained by our distributed algorithm, we conclude that the results obtained by our distributed algorithm are very close to the optimal solution.