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.

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.

The number of Access Points (AP) in a network is much smaller than the network users. Hence, some users may be far from Aps requiring more power to transmit data. Cooperative communication has been proposed, which uses the broadcast nature of the wireless medium to exploit the spatial diversity in wireless networks. Users in cooperative communication system work cooperatively by relaying the packets through a relay node(RN). One issue is to find an near optimal positions for the limited number of available relays to reduce energy consumption of the users. In this paper, we investigate the position of the relays and its effect on energy saving in cooperative wireless networks. Initially, we use a greedy algorithm to determine the position of the relays and determine the energy saving achieved from this algorithm. Then, we use our improved iterative algorithm to place the relays. Using simulations, we compare both the algorithms in terms of average energy consumption, average number of hops and average r-cover (RNs form an r-cover if each user is within distance at most r from the RN). It is evident from the simulation results that the improved algorithm outperforms the greedy algorithm in all the above mentioned aspects. The results provide us an insight into positioning of the relays in a cooperative wireless network to reduce energy consumption of the users.

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 enhanced network performance. We investigate how to effectively integrate these two advanced wireless communications technologies. In particular, we focus on the two representative cooperative relay strategies, decode-and-forward (DF) and amplify-and-forward (AF), and develop optimal spectrum sensing and p-Persistent CSMA for spectrum access. We develop an analysis for the comparison of these two relay strategies in the context of CR networks, and derive closed-form expressions for network-wide throughput achieved by DF, AF and direct link transmissions. 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 significant gaps between the cooperative relay results and the direct link results exemplify the diversity gain achieved by cooperative relays in CR networks.

Compressive 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 the trade-off between 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.

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.

Radio communication has come a long way from the initially deployed 1G voice (Amps) to modern 3G and 4G networks. A technology that holds promise to provide the next leap in peformance is femtocell. Femtocell technology brings the network closer to the user by adding smaller cells, which provide the benefit of higher quality links and more spatial reuse. To fully exploit this diversity realized at the physical layer some challenging issues have to be addressed. Making a handoff decision is one such issue where the user has various power levels available from Macro Base Station (MBS) and Femto Base Station (FBS). From a Base Station's (BS) perspective, there may be many users with close SINR values needing service but all users cannot be accommodated due to bandwidth limitation. To make these decisions, appropriate handoff mechanisms need to be adapted to fully exploit the advantages of these networks in various scenarios. In this paper, we extensively study methods to optimize handoff decisions under the open access scheme of operation, maintaining Quality of Service (QoS) thresholds to maximize overall network capacity such that fairness among users is maintained as well. We aim at developing a low-complexity algorithm with a small dwell time before handing off a macrocell user to a nearby femtocell and vice-versa. When the number of users in the network is smaller in comparison to the available FBSs, we observe better performance in reducing unnecessary handoffs.

This chapter examines the problem of video over infrastructure-based cognitive radio (CR) networks. It considers cross-layer design factors such as scalable video coding, spectrum sensing, opportunistic spectrum access, primary user protection, scheduling, error control, and modulation. The chapter investigates two challenging problems of enabling video over CR networks: an Institute of Electrical and Electronics Engineers 802/22 wireless regional area network-like, infrastructure-based CR network where the base station coordinates spectrum sensing and access, and a multi-hop CR network, such as a wireless mesh network with CR-enabled nodes. The high potential of CRs has attracted considerable interest from industry, government, and academia. Video multicast, as one of the most important multimedia services, has attracted considerable interest from the research community. The time slot structure is the same as that in the case of infrastructure-based CR networks. During the transmission phase of a time slot, a CR user determines which channel(s) to access for transmission of video data based on spectrum sensing results.

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.

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.

In this paper, we investigate the problem of cooperative relay in CR networks for further enhanced network performance. In particular, we focus on the two representative cooperative relay strategies, and develop optimal spectrum sensing and $p$-Persistent CSMA for spectrum access. Then, we study the problem of cooperative relay in CR networks for video streaming. We incorporate interference alignment to allow transmitters collaboratively send encoded signals to all CR users. 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 bounded performance.

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.

Video content delivery over wireless networks is expected to grow drastically in the coming years. In this paper, we investigate the challenging problem of video over cognitive radio (CR) networks. Although having high potential, this problem brings about a new level of technical challenges. After reviewing related work, we first address the problem of video over infrastructure-based CR networks, and then extend the problem to video over non-infrastructure-based ad hoc CR networks. We present formulations of cross-layer optimization problems as well as effective algorithms to solving the problems. The proposed algorithms are analyzed with respect to their optimality and validate with simulations.

Cognitive radios (CR) are intelligent radio devices that can sense the radio environment and adapt to changes in the radio environment. CR represents a new paradigm of wireless communications and networking by efficiently sharing spectrum between licensed users and secondary users. To harvest the high potential of CRs, the mainstream CR research has focused on developing effective spectrum sensing and access techniques. Although considerable advances have been achieved, the important problem of guaranteeing application performance has not been well studied.The first part of this dissertation develops effective algorithms and protocols for spectrum sensing and access. First, we present a spectrum sensing error aware MAC protocol for a CR network collocated with multiple primary networks. Second, we consider the problem of interference mitigation via channel assignment and power allocation for CR users.The second part of this dissertation focuses on the problem of optimized video streaming over CR networks. First, we tackle the problem of scalable video multicast in emerging infrastructure-based CR networks. Second, we investigate the more challenging problem of streaming multiple videos over multi-hop CR networks.Cooperative CR networks are discussed in the third part of this dissertation. First, we investigate the problem of cooperative relay in CR networks for further enhanced network performance. Then, we study the problem of cooperative relay in CR networks for video streaming incorporating interference alignment techniques.In the fourth part of this dissertation, we consider femtocell CR networks, where femto base stations (FBS) are deployed to greatly improve network coverage and capacity. First, we investigate the problem of generic data multicast in femtocell networks. Second, we tackle the problem of streaming scalable videos in femtocell CR networks.This dissertation research provides a new perspective on how robust multi-user video streaming can be achieved in highly dynamic CR networks. It is among the first efforts to address the important area of video over CR networks, and offers systematic and comprehensive results and solutions. The findings may shed new light on the feasibility of CR networks in transporting realtime video and be useful for developing practical CR video systems.

In this paper, we consider femtocell CR networks, where femto base stations (FBS) are deployed to greatly improve network coverage and capacity. We investigate the problem of generic data multicast in femtocell networks. We reformulate the resulting MINLP problem into a simpler form, and derive upper and lower performance bounds. Then we consider three typical connection scenarios in the femtocell network, and develop optimal and near-optimal algorithms for the three scenarios. Second, we tackle the problem of streaming scalable videos in femtocell CR networks. A framework is developed to 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-opitmal solutions, and prove a closed-form lower bound on its performance.

Cognitive radio (CR) is a paradigm of sharing spectrum among licensed (or, primary) and unlicensed (or, CR) users. In CR networks, interference mitigation is crucial not only for primary user protection, but also for the quality of service of CR users. We consider the problem of interference mitigation via channel assignment and power allocation for CR users. We develop a cross-layer optimization framework for minimizing both co-channel and adjacent channel interference; the latter has been shown to have considerable impact in practical systems. Spectrum sensing, opportunistic spectrum access, channel assignment, and power allocation are considered in the problem formulation. We propose a reformulation-linearization technique (RLT) based centralized algorithm that computes near-optimal solutions in polynomial time, and a distributed greedy algorithm that uses local information. Both algorithms are evaluated with simulations and are shown quite effective for mitigating both types of interference and achieving high CR network capacity.

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.