Joint power allocation and beamforming with users selection for cognitive radio networks via discrete stochastic optimization

Cognitive radio is a promising technique to dynamic utilize the spectrum resource and improve spectrum efficiency. In this paper, we study the problem of mutual interference cancellation among secondary users (SUs) and interference control to primary users (PUs) in spectrum sharing underlay cognitive radio networks. Multiple antennas are used at the secondary base station to form multiple beams towards individual SUs, and a set of SUs are selected to adapt to the beams. For the interference control to PUs, we study power allocation among SUs to guarantee the interference to PUs below a tolerable level while maximizing SUs?? QoS. Based on these conditions, the problem of joint power allocation and beamforming with SUs selection is studied. Specifically, we emphasize on the condition of imperfect channel sensing due to hardware limitation, short sensing time and network connectivity issues, which means that only the noisy estimate of channel information for SUs can be obtained. We formulate the optimization problem to maximize the sum rate as a discrete stochastic optimization problem, then an efficient algorithm based on a discrete stochastic optimization method is proposed to solve the joint power allocation and beamforming with SUs selection problem. We verify that the proposed algorithm has fast convergence rate, low computation complexity and good tracking capability in time-varying radio environment. Finally, extensive simulation results are presented to demonstrate the performance of the proposed scheme.     

Interference‐aware spectrum handover for cognitive radio networks

Cognitive radio (CR) is a promising technique for future wireless networks, which significantly improves spectrum utilization. In CR networks, when the primary users (PUs) appear, the secondary users (SUs) have to switch to other available channels to avoid the interference to PUs. However, in the multi‐SU scenario, it is still a challenging problem to make an optimal decision on spectrum handover because of the the accumulated interference constraint of PUs and SUs. In this paper, we propose an interference‐aware spectrum handover scheme that aims to maximize the CR network capacity and minimize the spectrum handover overhead by coordinating SUs’ handover decision optimally in the PU–SU coexisted CR networks. On the basis of the interference temperature model, the spectrum handover problem is formulated as a constrained optimization problem, which is in general a non‐deterministic polynomial‐time hard problem. To address the problem in a feasible way, we design a heuristic algorithm by using the technique of Branch and Bound. Finally, we combine our spectrum handover scheme with power control and give a convenient solution in a single‐SU scenario. Experimental results show that our algorithm can improve the network performance efficiently.Copyright © 2012 John Wiley & Sons, Ltd.     

Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.     

Optimal Resource Allocation in Heterogeneous MIMO Cognitive Radio Networks

The optimal resource allocation in MIMO cognitive radio networks with heterogeneous secondary users, centralized and distributed users, is investigated in this work. The core aim of this work is to study the joint problems of transmission time and power allocation in a MIMO cognitive radio scenario. The optimization objective is to maximize the total capacity of the secondary users (SUs) with the constraint of fairness. At first, the joint problems of transmission time and power allocation for centralized SUs in uplink is optimized. Afterwards, for the heterogeneous case with both the centralized and distributed secondary users, the resource allocation problem is formulated and an iterative power water-filling scheme is proposed to achieve the optimal resource allocation for both kinds of SUs. A dynamic optimal joint transmission time and power allocation scheme for heterogeneous cognitive radio networks is proposed. The simulation results illustrate the performance of the proposed scheme and its superiority over other power control schemes.     

Optimal Resource Allocation for Spectrum Sensing Based Cognitive Radio Networks with Statistical QoS Guarantees

Resource allocation under spectrum sensing based dynamic spectrum sharing strategy is a critically important issue for cognitive radio networks (CRNs), because they need to not only satisfy the interference constraint caused to the primary users (PUs), but also meet the quality-of-service (QoS) requirements for the secondary users (SUs). In this paper, we develop the optimal spectrum sensing based resource allocation scheme for the delay QoS constrained CRNs. Specifically, we aim at maximizing the maximum constant arrival rate of the SU that can be supported by the time-varying service process subject to the given statistical delay QoS constraint. In our derived power allocation scheme, not only the average transmit and interference power constraints are considered, but also the impact of the PUs?? transmission to the CRNs and the PUs?? spectrum-occupancy probability are taken into consideration. Moreover, the spectrum sensing errors are also taken into consideration. Simulation results show that, (1) the effective capacity of the secondary link decreases when the statistical delay QoS constraint becomes stringent; (2) given the QoS constraint, the effective capacity of the secondary link varies with the interference power constraint and the SNR of the primary link.     

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Energy-efficient transceiver designs for multiuser MIMO cognitive radio networks via interference alignment

This paper studies the transceiver design for multiuser multiple-input multiple-output cognitive radio networks. Different from the conventional methods which aim at maximizing the spectral efficiency, this paper focuses on maximizing the energy efficiency (EE) of the network. First, we formulate the precoding and decoding matrix designs as optimization problems which maximize the EE of the network subject to per-user power and interference constraints. With a higher priority in accessing the spectrum, the primary users (PUs) can design their transmission strategies without awareness of the secondary user (SU) performance. Thus, we apply a full interference alignment technique to eliminate interference between the PUs. Then, the EE maximization problem for the primary network can be reformulated as a tractable concave-convex fractional program which can be solved by the Dinkelbach method. On the other hand, the uncoordinated interference from the PUs to the SUs cannot be completely eliminated due to a limited coordination between the PUs with the SUs. The secondary transceivers are designed to optimize the EE while enforcing zero-interference to the PUs. Since the EE maximization for the secondary network is an intractable fractional programming problem, we develop an iterative algorithm with provable convergence by invoking the difference of convex functions programming along with the Dinkelbach method. In addition, we also derive closed-form expressions for the solutions in each iteration to gain insights into the structures of the optimal transceivers. The simulation results demonstrate that our proposed method outperforms the conventional approaches in terms of the EE.     

A low‐complexity resource allocation algorithm in OFDMA‐based cooperative cognitive radio networks

In this paper, we propose a low‐complexity resource allocation algorithm for the orthogonal frequency division multiplexing cooperative cognitive radio networks, where multiple primary users (PUs) and multiple secondary users (SUs) coexist. Firstly, we introduce a new concept of ‘efficiency capacity’ to represent the channel conditions of SUs by considering both of the interference caused by the PUs and the channel gains of the SUs with the assist of the relays. Secondly, we allocate the relay, subcarrier and transmission power jointly under the constraint of limiting interference caused to the PUs. Simulation results show that the proposed algorithm can achieve a high data rate with a relative low power level. Copyright © 2014 John Wiley & Sons, Ltd.     

Reinforcement Learning Enhanced Iterative Power Allocation in Stochastic Cognitive Wireless Mesh Networks

As the scarce spectrum resource is becoming over-crowded, cognitive wireless mesh networks have great flexibility to improve the spectrum utilization by opportunistically accessing the licensed frequency bands. One of the critical challenges for realizing such network is how to adaptively allocate transmit powers and frequency resources among secondary users (SUs) of the licensed frequency bands while maintaining the quality-of-service (QoS) requirement of the primary users (PUs). In this paper, we consider the power control problem in the context of cognitive wireless mesh networks formed by a number of clusters under the total transmit power constraint by each SU as well as the mean-squared error (MSE) constraint by PUs. The problem is modeled as a non-cooperative game. A distributed iterative power allocation algorithm is designed to reach the Nash equilibrium (NE) between the coexisting interfered links. It offers an opportunity for SUs to negotiate the best use of power and frequency with each other. Furthermore, how to adaptively negotiate the transmission power level and spectrum usage among the SUs according to the changing networking environment is discussed. We present an intelligent policy based on reinforcement learning to acquire the stochastic behavior of PUs. Based on the learning approach, the SUs can adapt to the dynamics of the interference environment state and reach new NEs quickly through partially cooperative information sharing via a common control channel. Theoretical analysis and numerical results both show effectiveness of the intelligent policy.     

认知中继协同无线通信系统功率分配和混合频谱接入方案

文章首先对Underlay 认知-中继协作（cognitive radio relay cooperation, CR-RC）系统信源和中继的功率分配问题进行了研究,获得了Underlay CR-RC系统信源、中继独立功率分配(independent power allocation, IPA）和联合功率分配（joint power allocation, JPA）方案,并给出了基于IPA和JPA的CR-RC系统的中断概率和各态历经容量的封闭解析解;其次通过对Underlay CR-RC系统中断性能的比较分析,提出了高频谱效率、高能量效率的混合Interweave-Underlay CR-RC方案。在该方案中,定义了主、从用户中断概率约束,充分考虑了主、从系统的服务质量(quality of service, QoS),当即使从用户的发射功率为零,主系统的QoS仍不能满足时,从用户以最大功率发送信号,无需考虑其对主用户的影响;当由于很强的主用户干扰,使得从用户的QoS不能得到满足时,从用户不再发射信号,发信功率为零;当主、从系统的QoS能够同时满足时,以Underlay模式工作。      

基于稳定匹配的认知无线网络协作物理层安全机制

在Underlay认知无线网络中,次用户被允许在主用户进行数据发送时接入主用户的频谱.此时,主用户将对次用户和窃听者造成干扰.利用协作干扰技术,主用户产生的干扰可以被用来改善次用户的物理层安全.基于此,本文针对包含多个主次用户的Underlay认知无线网络,提出了一种新的协作物理层安全机制.为了在保证主用户通信质量的前提下,最大化网络中次用户的总的安全容量,该机制将对次用户进行合理的频谱接入选择和功率控制.另外,考虑到个体理性和自私性对于频谱接入稳定性的影响,该机制利用稳定匹配理论将频谱接入选择问题建模为一对一的双边匹配问题,通过构建主次用户之间的稳定匹配来保证频谱接入的稳定性.仿真结果表明,使用本文所提安全机制,可以在保证主用户通信质量的前提下,稳定而又有效地改善网络中次用户获得的总的安全容量.     

Opportunistic power allocation strategies and fair subcarrier allocation in OFDM-based cognitive radio networks

In this paper we have studied the subcarrier and optimal power allocation strategy for OFDM-based cognitive radio (CR) networks. Firstly, in order to protect the primary user communication from the interference of the cognitive user transmissions in fading wireless channels, we design an opportunistic power control scheme to maximize the cognitive user capacity without degrading primary user’s QoS. The mathematical optimization problem is formulated as maximizing the capacity of the secondary users under the interference constraint at the primary receiver and the Lagrange method is applied to obtain the optimal solution. Secondly, in order to limit the outage probability within primary user’s tolerable range we analyze the outage probability of the primary user with respect to the interference power of the secondary user for imperfect CSI. Finally, in order to get the better tradeoff between fairness and system capacity in cognitive radio networks, we proposed an optimal algorithm of jointing subcarrier and power allocation scheme among multiple secondary users in OFDM-based cognitive radio networks. Simulation results demonstrate that our scheme can improve the capacity performance and efficiently guarantee the fairness of secondary users.     

多用户认知网络中基于Grassmann流形梯度法的干扰对齐算法

针对多个主用户和多个次用户的MIMO认知网络,本文给出了一种不依赖信道互惠性和不需要前后向链路交替式迭代的干扰对齐方法。对于次用户,首先,通过对其进行编码,建立了消除主次用户间相互干扰后的等效模型。然后,在等效模型的基础上,以最大化总容量为目标函数设计预编码矩阵,并采用基于Grassmann流形上的梯度法对目标函数进行求解得到预编码。最后,在接收端以最大信干噪比准则来设计接收滤波器矩阵。仿真结果显示,在低信噪比时,本文算法与现有典型算法性能相同,而在高信噪比时本文算法性能更优。      

Reliability-Intercept Gap Analysis of Underlay Cognitive Networks Under Artificial Noise and Primary Interference

Wireless Personal Communications - Underlay mode of cognitive radio networks (CRNs) permits secondary users (SUs) to simultaneously operate with primary users (PUs), inducing mutual interference...     

Robust Probabilistic Distributed Power Control Algorithm for Underlay Cognitive Radio Networks under Channel Uncertainties

Due to limited cooperation among users and erratic nature of wireless channel, it is difficult for secondary users (SUs) to obtain exact values of system parameters, which may lead to severe interference to primary users (PUs) and cause communication interruption for SUs. In this paper, we study robust power control problem for spectrum underlay cognitive radio networks with multiple SUs and PUs under channel uncertainties. Precisely, our objective is to minimize total transmit power of SUs under the constraints that the satisfaction probabilities of both interference temperature of PUs and signal-to-interference-plus-noise ratio of SUs exceed some thresholds. With knowledge of statistical distribution of fading channel, probabilistic constraints are transformed into closed forms. Under a weighted interference temperature constraint, a globally distributed power control iterative algorithm with forgetting factor to increase convergence speed is obtained by dual decomposition methods. Numerical results show that our proposed algorithm outperforms worst case method and non-robust method.     

Spectrum sharing in multi-channel cooperative cognitive radio networks: a coalitional game approach

In this paper, we study a coalitional game approach to resource allocation in a multi-channel cooperative cognitive radio network with multiple primary users (PUs) and secondary users (SUs). We propose to form the grand coalition by grouping all PUs and SUs in a set, where each PU can lease its spectrum to all SUs in a time-division manner while the SUs in return assist PUs’ data transmission as relays. We use the solution concept of the core to analyze the stability of the grand coalition, and the solution concept of the Shapley value to fairly divide the payoffs among the users. Due to the convexity of the proposed game, the Shapley value is shown to be in the core. We derive the optimal strategy for the SU, i.e., transmitting its own data or serving as a relay, that maximizes the sum rate of all PUs and SUs. The payoff allocations according to the core and the Shapley value are illustrated by an example, which demonstrates the benefits of forming the grand coalition as compared with non-coalition and other coalition schemes.     

Robust Cognitive Beamforming With Bounded Channel Uncertainties

This paper studies the robust beamforming design for a multi-antenna cognitive radio (CR) network, which transmits to multiple secondary users (SUs) and coexists with a primary network of multiple users. We aim to maximize the minimum of the received signal-to-interference-plus-noise ratios (SINRs) of the SUs, subject to the constraints of the total SU transmit power and the received interference power at the primary users (PUs) by optimizing the beamforming vectors at the SU transmitter based on imperfect channel state information (CSI). To model the uncertainty in CSI, we consider a bounded region for both cases of channel matrices and channel covariance matrices. As such, the optimization is done while satisfying the interference constraints for all possible CSI error realizations. We shall first derive equivalent conditions for the interference constraints and then convert the problems into the form of semi-definite programming (SDP) with the aid of rank relaxation, which leads to iterative algorithms for obtaining the robust optimal beamforming solution. Results demonstrate the achieved robustness and the performance gain over conventional approaches and that the proposed algorithms can obtain the exact robust optimal solution with high probability.      

Optimal Precoder Design for Non-Regenerative MIMO Cognitive Two-Way Relay Systems with Underlay Spectrum Sharing

We study the optimal precoder design for a MIMO cognitive two-way relay system with underlay spectrum sharing. The system consists of two secondary users (SUs) and one relay station (RS). We jointly optimize the precoders for SUs and RS with perfect and imperfect channel state information (CSI) between SUs/RS and the primary user, where our design approach is based on the alternate optimization method. For the perfect CSI case, we derive the optimal structure of the RS precoding matrix, which generalizes the result for single-antenna SUs and helps to reduce the search complexity. We develop gradient projection (GP) algorithm to calculate the optimal RS precoder numerically. When the RS precoder is given, we propose a fast algorithm based on generalized water-filling theorem to compute the optimal SU precoders. For the imperfect CSI case, we derive equivalent conditions for the interference power constraints and convert the robust SU precoder optimization into the form of semi-definite programming. As for the robust RS precoder optimization, we relax the interference power constraint related with the RS precoder to be convex and then the GP algorithm can be applied. Finally, simulation results demonstrate the effectiveness of the proposed schemes.     

认知无线电中基于MIMO波束成形的协同频谱接入设计

协同认知无线电技术由于其高效的频谱利用效率已经吸引越来越多的关注。在协同认知网络中,当第一用户（Primary User,PU）之间的信道状况恶劣时,特定的第二用户（Secondary User, SU）被选为中继协同PU完成信息传递,作为回报,PU分配一定的信道资源给SU,使其用于传输自己的信息。当SU系统中一个节点拥有多天线时,假设其可以很好的获悉其与PU之间的信道状态信息,通过波束成形设计可以使其在中继PU信息的同时完成自身信息的发送,而且使两者之间的信号互不干扰。这种频谱共享式的接入方式可以节约信道资源,提高频谱利用率。本文对采用放大转发（Amplify and Forward, AF）中继协议的SU发射端分别采用最小化加权均方误差和准则（Minimizing Sum of Weighted Mean Square Errors, MSWMSE）和迫零准则（Zero Forcing, ZF）对波束成形参数进行设计。仿真结果表明,两种设计方式都可以满足消除用户间干扰的要求;另外,由于基于MSWMSE准则的波束成形参数能够更好的平衡噪声和用户间干扰项对信号的损耗,因而获得更优的性能表现,而且通过调整加权系数可以满足PU用户不同的性能要求。      

Energy-Efficiency-Based Power Allocation Scheme in Multi-user Single-DF-Relay Cognitive Networks

Due to the enhancement in both spectral efficiency and energy efficiency, cognitive radio (CR) being combined with relay cooperation technique is deemed as a promising way to realize green-broadband communication in the fifth generation (5G) networks. In this paper, for such CR-relay networks operating in underlay mode, when multiple secondary cognitive users (SUs) share a common cognitive relay in decode-and-forward manner to complete their physical transmissions, system power consumption is investigated. For the scenarios where the co-channel interference to primary users and the peak transmit power of SUs and cognitive relay are constrained, the problem of power allocation in CR-relay network is formulated to minimizing sum-system-consumption. Then, based on the principle that power-consumption minimization is equivalent to energy-efficiency maximization, a novel power allocation scheme is proposed. Further numerical simulation is used to verify the optimality of the proposed power allocation scheme.