Mathematical optimization techniques for resource allocation and spatial multiplexing in spectrum sharing networks

Due to introduction of smart phones with data intensive multimedia and interactive applications and exponential growth of wireless devices, there is a shortage for useful radio spectrum. Even though the spectrum has become crowded, many spectrum occupancy measurements indicate that most of the allocated spectrum is underutilised. Hence radically new approaches in terms of allocation of wireless resources are required for better utilization of radio spectrum. This has motivated the concept of opportunistic spectrum sharing or the so-called cognitive radio technology that has great potential to improve spectrum utilization. The cognitive radio technology allows an opportunistic user namely the secondary user to access the spectrum of the licensed user (known as primary user) provided that the secondary transmission does not harmfully affect the primary user. This is possible with the introduction of advanced resource allocation techniques together with the use of wireless relays and spatial diversity techniques. In this thesis, various mathematical optimization techniques have been developed for the efficient use of radio spectrum within the context of spectrum sharing networks. In particular, optimal power allocation techniques and centralised and distributed beamforming techniques have been developed. Initially, an optimization technique for subcarrier and power allocation has been proposed for an Orthogonal Frequency Division Multiple Access (OFDMA) based secondary wireless network in the presence of multiple primary users. The solution is based on integer linear programming with multiple interference leakage and transmission power constraints. In order to enhance the spectrum efficiency further, the work has been extended to allow multiple secondary users to occupy the same frequency band under a multiple-input and multiple-output (MIMO) framework. A sum rate maximization technique based on uplink-downlink duality and dirty paper coding has been developed for the MIMO based OFDMA network. The work has also been extended to handle fading scenarios based on maximization of ergodic capacity. The optimization techniques for MIMO network has been extended to a spectrum sharing network with relays. This has the advantage of extending the coverage of the secondary network and assisting the primary network in return for the use of the primary spectrum. Finally, instead of considering interference mitigation, the recently emerged concept of interference alignment has been used for the resource allocation in spectrum sharing networks. The performances of all these new algorithms have been demonstrated using MATLAB based simulation studies

Power Control and Resource Allocation for QoS-Constrained Wireless Networks

Developments such as machine-to-machine communications and multimedia services are placing growing demands on high-speed reliable transmissions and limited wireless spectrum resources. Although multiple-input multiple-output (MIMO) systems have shown the ability to provide reliable transmissions in fading channels, it is not practical for single-antenna devices to support MIMO system due to cost and hardware limitations. Cooperative communication allows single-antenna devices to share their spectrum resources and form a virtual MIMO system where their quality of service (QoS) may be improved via cooperation. Most cooperative communication solutions are based on fixed spectrum access schemes and thus cannot further improve spectrum efficiency. In order to support more users in the existing spectrum, we consider dynamic spectrum access schemes and cognitive radio techniques in this dissertation. Our work includes the modelling, characterization and optimization of QoS-constrained cooperative networks and cognitive radio networks. QoS constraints such as delay and data rate are modelled. To solve power control and channel resource allocation problems, dynamic power control, matching theory and multi-armed bandit algorithms are employed in our investigations. In this dissertation, we first consider a cluster-based cooperative wireless network utilizing a centralized cooperation model. The dynamic power control and optimization problem is analyzed in this scenario. We then consider a cooperative cognitive radio network utilizing an opportunistic spectrum access model. Distributed spectrum access algorithms are proposed to help secondary users utilize vacant channels of primary users in order to optimize the total utility of the network. Finally, a noncooperative cognitive radio network utilizing the opportunistic spectrum access model is analyzed. In this model, primary users do not communicate with secondary users. Therefore, secondary users are required to find vacant channels on which to transmit. Multi-armed bandit algorithms are proposed to help secondary users predict the availability of licensed channels. In summary, in this dissertation we consider both cooperative communication networks and cognitive radio networks with QoS constraints. Efficient power control and channel resource allocation schemes have been proposed for optimization problems in different scenarios.Cambridge Overseas Trust; China Scholarship Counci

Energy Efficiency in MIMO Underlay and Overlay Device-to-Device Communications and Cognitive Radio Systems

This paper addresses the problem of resource allocation for systems in which a primary and a secondary link share the available spectrum by an underlay or overlay approach. After observing that such a scenario models both cognitive radio and D2D communications, we formulate the problem as the maximization of the secondary energy efficiency subject to a minimum rate requirement for the primary user. This leads to challenging non-convex, fractional problems. In the underlay scenario, we obtain the global solution by means of a suitable reformulation. In the overlay scenario, two algorithms are proposed. The first one yields a resource allocation fulfilling the first-order optimality conditions of the resource allocation problem, by solving a sequence of easier fractional problems. The second one enjoys a weaker optimality claim, but an even lower computational complexity. Numerical results demonstrate the merits of the proposed algorithms both in terms of energy-efficient performance and complexity, also showing that the two proposed algorithms for the overlay scenario perform very similarly, despite the different complexity.Comment: to appear in IEEE Transactions on Signal Processin