Non-convex distributed power allocation games in cognitive radio networks

In this thesis, we explore interweave communication systems in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio user by optimizing jointly both the detection operation based on sensing and the power allocation across channels, taking into account the influence of the sensing accuracy and the interference limitation to the primary users. The optimization problem is addressed in single and multiuser cognitive radio networks for both single-input single-output and multi-input multi-output channels. Firstly, we study the resource allocation optimization problem for single-input single-output single user cognitive radio networks, wherein the cognitive radio aims at maximizing its own sum-rate by jointly optimizing the sensing information and power allocation over all the channels. In this framework, we consider an opportunistic spectrum access model under interweave systems, where a cognitive radio user detects active primary user transmissions over all the channels, and decides to transmit if the sensing results indicate that the primary user is inactive at this channel. However, due to the sensing errors, the cognitive users might access the channel when it is still occupied by active primary users, which causes harmful interference to both cognitive radio users and primary users. This motivates the introduction of a novel interference constraint, denoted as rate-loss gap constraint, which is proposed to design the power allocation, ensuring that the performance degradation of the primary user is bounded. The resulting problem is non-convex, thus, an exhaustive optimization algorithm and an alternating direction optimization algorithm are proposed to solve the problem efficiently. Secondly, the resource allocation problem for a single-input single-output multiuser cognitive radio network under a sensing-based spectrum sharing scheme is analyzed as a strategic non-cooperative game, where each cognitive radio user is selfish and strives to use the available spectrum in order to maximize its own sum-rate by considering the effect of imperfect sensing information. The resulting game-theoretical formulations belong to the class of non-convex games. A distributed cooperative sensing scheme based on a consensus algorithm is considered in the proposed game, where all the cognitive radio users can share their sensing information locally. We start with the alternating direction optimization algorithm, and prove that the local Nash equilibrium is achieved by the alternating direction optimization algorithm. In the next step, we use a new relaxed equilibrium concept, namely, quasi-Nash equilibrium for the non-convex game. The analysis of the sufficient conditions for the existence of the quasi-Nash equilibrium for the proposed game is provided. Furthermore, an iterative primal-dual interior point algorithm that converges to a quasi-Nash equilibrium of the proposed game is also proposed. From the simulation results, the proposed algorithm is shown to yield a considerable performance improvement in terms of the sum-rate of each cognitive radio user, with respect to previous state-of-the-art algorithms. Finally, we investigate a multiple-input multiple-output multiuser cognitive radio network under the opportunistic spectrum access scheme. We focus on the throughput of each cognitive radio user under correct sensing information, and exclude the throughput due to the erroneous decision of the cognitive radio users to transmit over occupied channels. The optimization problem is analyzed as a strategic non-cooperative game, where the transmit covariance matrix, sensing time, and detection threshold are considered as multidimensional variables to be optimized jointly. We also use the new relaxed equilibrium concept quasi-Nash equilibrium and prove that the proposed game can achieve a quasi-Nash equilibrium under certain conditions, by making use of the variational inequality method. In particular, we prove theoretically the sufficient condition of the existence and the uniqueness of the quasi-Nash equilibrium for this game. Furthermore, a possible extension of this work considering equal sensing time is also discussed. Simulation results show that the iterative primal-dual interior point algorithm is an efficient solution that converges to the quasi-Nash equilibrium of the proposed game

Contributions to Resource Allocation in Cognitive Radio Networks

The continuous increase in the number of wireless devices and the huge demand for higher data rates have promoted the development of new wireless communications technologies with improved spectrum sharing features. Recently, the concept of cognitive radio (CR) has gained increased popularity for the efficient utilization of radio frequency (RF) spectrum. A CR is characterized as a communication system which is capable to learn the spectrum environment through sensing, and to adapt its signaling schemes for a better utilization of the radio frequency resources. Resource allocation, which involves scheduling of spectrum and power resources, represents a crucial problem for the performance of CR networks in terms of system throughput and bandwidth utilization. In this dissertation, we investigate resource allocation problems in a CR network by exploring a variety of optimization techniques. Specifically, in the first part of the dissertation, our goal is to maximize the total throughput of secondary users (SUs) in an orthogonal frequency division multiple access (OFDMA) CR network. In addition, the power of SUs is controlled to keep the interference introduced to primary users (PUs) under certain limits, which gives rise to a non-convex mixed integer non-linear programming (MINLP) optimization problem. It is illustrated that the original non-convex MINLP formulation admits a special structure and the optimal solution can be achieved efficiently using any standard convex optimization method under a general and practical assumption. In the second part of the dissertation, considering the imperfect sensing information, we study the joint spectrum sensing and resource allocation problem in a multi-channel-multi-user CR network. The average total throughput of SUs is maximized by jointly optimizing the sensing threshold and power allocation strategies. The problem is also formulated as a non-convex MINLP problem. By utilizing the continuous relaxation and convex optimization tools, the dimension of the non-convex MINLP problem is significantly reduced, which helps to reformulate the optimization problem without resorting to integer variables. A newly-developed optimization technique, referred to as the monotonic optimization, is then employed to obtain an optimal solution. Furthermore, a practical low-complexity spectrum sensing and resource allocation algorithm is proposed to reduce the computational cost

Symbol Based Precoding in The Downlink of Cognitive MISO Channels

This paper proposes symbol level precoding in the downlink of a MISO cognitive system. The new scheme tries to jointly utilize the data and channel information to design a precoding that minimizes the transmit power at a cognitive base station (CBS); without violating the interference temperature constraint imposed by the primary system. In this framework, the data information is handled at symbol level which enables the characterization the intra-user interference among the cognitive users as an additional source of useful energy that should be exploited. A relation between the constructive multiuser transmissions and physical-layer multicast system is established. Extensive simulations are performed to validate the proposed technique and compare it with conventional techniques.Comment: CROWNCOM 201

Studies on efficient spectrum sharing in coexisting wireless networks.

Wireless communication is facing serious challenges worldwide: the severe spectrum shortage along with the explosive increase of the wireless communication demands. Moreover, different communication networks may coexist in the same geographical area. By allowing multiple communication networks cooperatively or opportunistically sharing the same frequency will potentially enhance the spectrum efficiency. This dissertation aims to investigate important spectrum sharing schemes for coexisting networks. For coexisting networks operating in interweave cognitive radio mode, most existing works focus on the secondary network’s spectrum sensing and accessing schemes. However, the primary network can be selfish and tends to use up all the frequency resource. In this dissertation, a novel optimization scheme is proposed to let primary network maximally release unnecessary frequency resource for secondary networks. The optimization problems are formulated for both uplink and downlink orthogonal frequency-division multiple access (OFDMA)-based primary networks, and near optimal algorithms are proposed as well. For coexisting networks in the underlay cognitive radio mode, this work focuses on the resource allocation in distributed secondary networks as long as the primary network’s rate constraint can be met. Global optimal multicarrier discrete distributed (MCDD) algorithm and suboptimal Gibbs sampler based Lagrangian algorithm (GSLA) are proposed to solve the problem distributively. Regarding to the dirty paper coding (DPC)-based system where multiple networks share the common transmitter, this dissertation focuses on its fundamental performance analysis from information theoretic point of view. Time division multiple access (TDMA) as an orthogonal frequency sharing scheme is also investigated for comparison purpose. Specifically, the delay sensitive quality of service (QoS) requirements are incorporated by considering effective capacity in fast fading and outage capacity in slow fading. The performance metrics in low signal to noise ratio (SNR) regime and high SNR regime are obtained in closed forms followed by the detailed performance analysis

State of the Art, Taxonomy, and Open Issues on Cognitive Radio Networks with NOMA

The explosive growth of mobile devices and the rapid increase of wideband wireless services call for advanced communication techniques that can achieve high spectral efficiency and meet the massive connectivity requirement. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are envisioned to be important solutions for the fifth generation wireless networks. Integrating NOMA techniques into CR networks (CRNs) has the tremendous potential to improve spectral efficiency and increase the system capacity. However, there are many technical challenges due to the severe interference caused by using NOMA. Many efforts have been made to facilitate the application of NOMA into CRNs and to investigate the performance of CRNs with NOMA. This article aims to survey the latest research results along this direction. A taxonomy is devised to categorize the literature based on operation paradigms, enabling techniques, design objectives and optimization characteristics. Moreover, the key challenges are outlined to provide guidelines for the domain researchers and designers to realize CRNs with NOMA. Finally, the open issues are discussed.Comment: This paper has been accepted by IEEE Wireless Communications Magazine. Pages 16, Figures

Resource allocation and optimization techniques in wireless relay networks

Relay techniques have the potential to enhance capacity and coverage of a wireless network. Due to rapidly increasing number of smart phone subscribers and high demand for data intensive multimedia applications, the useful radio spectrum is becoming a scarce resource. For this reason, two way relay network and cognitive radio technologies are required for better utilization of radio spectrum. Compared to the conventional one way relay network, both the uplink and the downlink can be served simultaneously using a two way relay network. Hence the effective bandwidth efficiency is considered to be one time slot per transmission. Cognitive networks are wireless networks that consist of different types of users, a primary user (PU, the primary license holder of a spectrum band) and secondary users (SU, cognitive radios that opportunistically access the PU spectrum). The secondary users can access the spectrum of the licensed user provided they do not harmfully affect to the primary user. In this thesis, various resource allocation and optimization techniques have been investigated for wireless relay and cognitive radio networks

Distributed Channel Assignment in Cognitive Radio Networks: Stable Matching and Walrasian Equilibrium

We consider a set of secondary transmitter-receiver pairs in a cognitive radio setting. Based on channel sensing and access performances, we consider the problem of assigning channels orthogonally to secondary users through distributed coordination and cooperation algorithms. Two economic models are applied for this purpose: matching markets and competitive markets. In the matching market model, secondary users and channels build two agent sets. We implement a stable matching algorithm in which each secondary user, based on his achievable rate, proposes to the coordinator to be matched with desirable channels. The coordinator accepts or rejects the proposals based on the channel preferences which depend on interference from the secondary user. The coordination algorithm is of low complexity and can adapt to network dynamics. In the competitive market model, channels are associated with prices and secondary users are endowed with monetary budget. Each secondary user, based on his utility function and current channel prices, demands a set of channels. A Walrasian equilibrium maximizes the sum utility and equates the channel demand to their supply. We prove the existence of Walrasian equilibrium and propose a cooperative mechanism to reach it. The performance and complexity of the proposed solutions are illustrated by numerical simulations.Comment: submitted to IEEE Transactions on Wireless Communicaitons, 13 pages, 10 figures, 4 table