Due to the drastic increase in wireless video traffic, the capacity of the existing and future wireless networks will be greatly stressed, while interference will become the dominant capacity-limiting factor. In this paper, we investigate relay-assisted downlink multiuser video streaming in a cognitive radio (CR) cellular network. We incorporate zero-forcing precoding to allow transmitters collaboratively send encoded (mixed) signals to all CR users, such that undesired signals will be canceled and the desired signal can be decoded at each CR user. We present a stochastic programming formulation of the problem, as well as a problem reformulation that greatly reduces computational complexity. In the cases of a single licensed channel and multiple licensed channels with channel bonding, we develop an optimal distributed algorithm with proven convergence and convergence speed. In the case of multiple channels without channel bonding, we develop a greedy algorithm with a proven performance bound. The algorithms are evaluated with simulations and are shown to achieve considerable gains over two heuristic schemes.

Cooperative diversity is considered as an effective means for combating weather turbulence in FSO networks. We investigate the problem of maximizing the FSO network-wide throughput under constraint of a given power budget and a number of FSO transceivers. The problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem. We propose both centralized and distributed algorithms using bipartite matching and convex optimization to obtain highly competitive solutions. The proposed algorithms are shown to outperform the non-cooperative scheme and an existing relay selection protocol with considerable gains through simulations.

Although the technology of femtocells is highly promising, many challenging problems should be addressed before fully harvesting its potential. In this paper, we investigate the problem of cell association and handover management in femtocell networks. Two extreme cases for cell association are first discussed and analyzed. Then we propose our algorithm to maximize network capacity while achieving fairness among users. Based on this algorithm, we further develop a handover algorithm to reduce the number of unnecessary handovers using Bayesian estimation. The proposed handover algorithm is demonstrated to outperform a heuristic scheme with considerable gains in our simulation study.

Compressed sensing (CS) refers to the process of reconstructing a signal that is supposed to be sparse or compressible. CS has wide applications, such as in cognitive radio networks. In this paper, we investigate effective CS schemes for balancing energy efficiency and estimation error. We propose an enhancement to a Bayesian estimation approach and an enhancement to the isotonic regression approach that is based on nearly isotonic regression. We also show how to compute the routing matrix for selecting active sensor nodes. The proposed enhancements are evaluated with trace-driven simulations. Considerable gaps are observed between the original approaches and the proposed enhancements in the simulation results. The near isotonic regression method achieves the best performance among all the CS schemes examined in this paper.

Due to the drastic increase in wireless video traffic, the capacity of existing and future wireless networks will be greatly stressed, while interference will become the dominant capacity limiting factor. In this paper, we investigate cooperative relay in CR networks using video as a reference application. We incorporate interference alignment to allow transmitters collaboratively send encoded signals to all CR users, such that undesired signals will be canceled and the desired signal can be decoded at each CR user. We present a stochastic programming formulation, as well as a reformulation that greatly reduces computational complexity. In the cases of a single licensed channel and multiple licensed channels with channel bonding, we develop an optimal distributed algorithm with proven convergence and convergence speed. In the case of multiple channels without channel bonding, we develop a greedy algorithm with a proven performance bound. The algorithms are evaluated with simulations and are shown to achieve considerable gains over two heuristic schemes that do not consider interference alignment.

Femtocells are shown highly effective on improving network coverage and capacity by bringing base stations closer to mobile users. In this paper, we investigate the problem of streaming scalable videos in femtocell cognitive radio (CR) networks. This is a challenging problem due to the stringent QoS requirements of real-time videos and the new dimensions of network dynamics and uncertainties in CR networks. We develop a framework that captures the key design issues and trade-offs with a stochastic programming problem formulation. In the case of a single FBS, we develop an optimum-achieving distributed algorithm, which is shown also optimal for the case of multiple non-interfering FBS's. In the case of interfering FBS's, we develop a greedy algorithm that can compute near-optimal solutions, and prove a closed-form lower bound on its performance. The proposed algorithms are evaluated with simulations, and are shown to outperform three alternative schemes with considerable margins.

A femtocell is a small cellular base station (BS), typically used for serving approved users within a small coverage. In this paper, we investigate the problem of data multicast in femtocell networks that incorporates superposition coding (SC) and successive interference cancellation (SIC). The problem is to decide the transmission schedule for each BS, as well as the power allocation for the SC layers, to achieve a sufficiently large SNR for each layer to be decodable with SIC. The objective is to minimize the total BS power consumption. We formulate a Mixed Integer Nonlinear Programming (MINLP) problem, which is NP-hard in general. We then reformulate the problem into a simpler form, and derive upper and lower performance bounds. Finally, we consider three typical connection scenarios in the femtocell network, and develop optimal and near-optimal algorithms for the three scenarios. The proposed algorithms have low computational complexity, and outperform a heuristic scheme with considerable gains in our simulation study.

Femtocells are shown highly effective on improving network coverage and capacity by bringing base stations closer to mobile users. In this paper, we investigate the problem of streaming scalable videos in femtocell cognitive radio (CR) networks. This is a challenging problem due to the stringent QoS requirements of real-time videos and the new dimensions of network dynamics and uncertainties in CR networks. We develop a framework that captures the key design issues and trade-offs with a stochastic programming problem formulation. In the case of a single FBS, we develop an optimum-achieving distributed algorithm, which is shown also optimal for the case of multiple non-interfering FBS's. In the case of interfering FBS's, we develop a greedy algorithm that can compute near-optimal solutions, and prove a closed-form lower bound for its performance. The proposed algorithms are evaluated with simulations, and are shown to outperform two alternative schemes with considerable margins.

Cognitive radios (CR) and cooperative communications represent new paradigms that both can effectively improve the spectrum efficiency of future wireless networks. In this paper, we investigate the problem of cooperative relay in CR networks for further improved network performance. The objective is to provide an analysis for the comparison of two representative cooperative relay strategies, decode and forward (DF) and amplify and forward (AF), in the context of CR networks. We consider optimal spectrum sensing and p-Persistent CSMA for spectrum access, and derive closed-form expressions for network-wide throughput achieved by DF and AF. Our analysis is validated by simulations. We find each of the strategies performs better in a certain parameter range; there is no case of dominance for the two strategies. The considerable gaps between the cooperative relay results and the direct link results exemplify the diversity gain achieved by cooperative relays in CR networks.

We investigate the problem of scalable video multicast in emerging cognitive radio (CR) networks. Although considerable advances have been made in CR research, such important problems have not been well studied. Naturally, 'bandwidth hungry' multimedia applications are excellent candidates for fully capitalizing the potential of CRs. We propose a crosslayer optimization approach to multicast video in CR networks. Specifically, we consider an infrastructure-based CR network collocated with N primary networks and model CR video multicast over the N channels as a mixed integer nonlinear programming (MINLP) problem. The objective is three-fold: to optimize the overall received video quality; to achieve proportional fairness among multicast users; and to keep the interference to primary users below a prescribed threshold. We propose a sequential fixing algorithm and a greedy algorithm to solve the MINLP, while the latter has low complexity and proven optimality gap. Our simulations with MPEG-4 fine grained scalability (FGS) video demonstrate the efficacy and superior performance of the proposed algorithms.

We investigate the problem of streaming multiple videos over multi-hop cognitive radio (CR) networks. Fine-Granularity-Scalability (FGS) and Medium-Grain-Scalable (MGS) videos are adopted to accommodate the heterogeneity among channel availabilities and dynamic network conditions. We obtain a mixed integer nonlinear programming (MINLP) problem formulation, with objectives to maximize the overall received video quality and to achieve fairness among the video sessions, while bounding the collision rate with primary users under the presence of spectrum sensing errors. We first solve the MINLP problem using a centralized sequential fixing algorithm, and derive upper and lower bounds for the objective value. We then apply dual decomposition to develop a distributed algorithm and prove its optimality and convergence conditions. The proposed algorithms are evaluated with simulations and are shown to be effective in supporting concurrent scalable video sessions in multi-hop CR networks.

In this paper, we present a spectrum sensing error-aware MAC protocol for a cognitive radio (CR) network collocated with multiple primary networks. We explicitly consider sensing errors in the CR MAC design, since such errors are inevitable for practical spectrum sensors. Two spectrum sensing polices are presented with which secondary users collaboratively sense the licensed channels. The sensing policies are then incorporated into p-Persistent CSMA to coordinate dynamic spectrum access for CR network users. We present an analysis of the interference and throughput performance of the proposed CR MAC, and find the analysis highly accurate in our simulation studies. The proposed sensing error-aware CR MAC protocol outperforms two existing approaches with considerable margins in our simulations, which justify the importance of considering spectrum sensing errors in CR MAC design.

We investigate the challenging problem of enabling multicast video service in emerging cognitive radio (CR) networks. We propose a cross-layer optimization approach to multicast video in CR networks. Specifically, we model CR video multicast as an optimization problem, while considering important design factors including scalable video coding, video rate control, spectrum sensing, dynamic spectrum access, modulation, scheduling, retransmission, and primary user protection. The objective is to optimize the overall received video quality as well as achieving proportional fairness among multicast users, while keeping the interference to primary users below a prescribed threshold. Although the problem can be solved using advanced optimization techniques, we propose a sequential fixing algorithm and a greedy algorithm with low complexity and proven optimality gap. Our simulations using MPEG-4 fine grained scalability (FGS) demonstrate the efficacy and superior performance of the proposed approach as compared with an alternative equal allocation scheme.