Joint optimization of channel allocation and power control for cognitive radio networks with multiple constraints

Wireless Networks - With the increasing demands for wireless communication, efficiently using the spectrum resource has always been an important research topic. In this paper, the problem of N...     

Low-complexity joint beamforming and power allocation for SINR balancing in cognitive radio networks

We consider the Signal-to-Interference plus Noise Ratio (SINR) balancing problem involving joint beamfoming and power allocation in the Cognitive Radio (CR) network, wherein the Single-Input Multi-Output Multiple Access Channels (SIMO-MAC) are assumed. Subject to two sets of constraints: the interference temperature constraints of Primary Users (PUs) and the peak power constraints of Cognitive Users (CUs), a low-complexity joint beamforming and power allocation algorithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection (SDMCPA-CP) for SINR balancing is proposed. Compared with the existing algorithm, the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions significantly, especially when large numbers of PUs and CUs are active, while still providing the optimal balanced SINR level for all the CUs.     

认知无线网络中基于联合波束形成和功率分配的低复杂度SINR均衡算法

认知无线电作为提高频谱使用效率的有效手段成为当前通信领域的研究热点。本文研究了单输入多输出的多用户认知无线网络,在主用户的干扰功率约束和认知用户的发射功率约束下,针对认知用户的信干噪比均衡问题,提出了一种低复杂度的联合波束形成和功率分配算法。与已有算法相比,该算法可以在保证最优的均衡信干噪比水平的同时,大幅度降低矩阵求逆和矩阵特征值分解的次数,特别是当处于通信状态的主用户和认知用户数目很多时。本文理论证明了所提算法的收敛性和最优性,并进行了详细的复杂度分析。理论分析和仿真结果均表明在多用户的认知无线网络中,本文所提算法可以将已有算法的平均运算复杂度降低50%以上。     

Joint power and spectrum allocation in multi-hop cognitive radio networks

Various cognitive network technologies are developed rapidly. In the article, the power and spectrum allocation in multi-hop cognitive radio network （CRN） with linear topology is investigated. The overall goal is to minimize outage probability and promote spectrum utility, including total reward and fairness, while meeting the limits of total transmit power and interference threshold to primary user simultaneously. The problem is solved with convex optimization and artificial bee colony （ABC） algorithm jointly. Simulation shows that the proposed scheme not only minimizes outage probability, but also realizes a better use of spectrum.     

A stochastic game framework for joint frequency and power allocation in dynamic decentralized cognitive radio networks

Cognitive radio networks (CRNs) have been recognized as a promising solution to improve the radio spectrum utilization. This article investigates a novel issue of joint frequency and power allocation in decentralized CRNs with dynamic or time-varying spectrum resources. We firstly model the interactions between decentralized cognitive radio links as a stochastic game and then proposed a strategy learning algorithm which effectively integrates multi-agent frequency strategy learning and power pricing. The convergence of the proposed algorithm to Nash equilibrium is proofed theoretically. Simulation results demonstrate that the throughput performance of the proposed algorithm is very close to that of the centralized optimal learning algorithm, while the proposed algorithm could be implemented distributively and reduce information exchanges significantly.     

Joint power and rate allocation for spectrum sharing cognitive radio multicast networks under service outage constraint

This letter considers a downlink spectrum sharing cognitive radio (CR) multicast network coexisting with a primary network. The problem of joint power and rate allocation to maximize the average sum rate is studied. In this regard, a joint power and rate allocation scheme is proposed, where the allocated rate can be higher than the lowest rate of all the secondary users (SUs) within the same group, which is different from the conventional multicast transmission scheme. To guarantee the quality of service (QoS) of the SU, the service outage probability defined as the probability that the maximum achievable rate of the SU is lower than the rate of the group, is constrained to be under a certain threshold, which is termed as the service outage constraint. The numerical results show that the proposed scheme provides significant improvement over the conventional scheme in terms of average sum rate.     

Adaptive MIMO antenna selection via discrete stochastic optimization

Recently it has been shown that it is possible to improve the performance of multiple-input multiple-output (MIMO) systems by employing a larger number of antennas than actually used and selecting the optimal subset based on the channel state information. Existing antenna selection algorithms assume perfect channel knowledge and optimize criteria such as Shannon capacity or various bounds on error rate. This paper examines MIMO antenna selection algorithms where the set of possible solutions is large and only a noisy estimate of the channel is available. In the same spirit as traditional adaptive filtering algorithms, we propose simulation based discrete stochastic optimization algorithms to adaptively select a better antenna subset using criteria such as maximum mutual information, bounds on error rate, etc. These discrete stochastic approximation algorithms are ideally suited to minimize the error rate since computing a closed form expression for the error rate is intractable. We also consider scenarios of time-varying channels for which the antenna selection algorithms can track the time-varying optimal antenna configuration. We present several numerical examples to show the fast convergence of these algorithms under various performance criteria, and also demonstrate their tracking capabilities.     

Power optimization and subcarrier allocation for downlink MIMO-OFDMA based cognitive radio networks

Wireless Networks - This paper presents a novel resource allocation framework for downlink transmissions in MIMO-OFDMA based cognitive radio (CR) networks. In this literature, due to the...     

Robust energy efficiency power allocation for relay-assisted uplink cognitive radio networks

In this paper, we investigate a worst-case robust power allocation scheme to improve energy efficiency (EE) for an amply-and-forward relaying uplink underlay OFDM cognitive radio system with imperfect channel situation information about the channel between primary user (PU) and secondary user (SU) and the channel between SU and corresponding relay. Specifically, a max–min problem is formulated to transform the original optimization problem into maximum EE on minimum throughout channel, and an epigraph problem is introduced to obtain analytical expressions of objective power allocation. Simulation results show that the proposed EE power allocation scheme is valid and effective in EE and robustness.     

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.     

Joint sensing time and power allocation in cooperatively cognitive networks

The jointly optimal allocation of sensing time and power for a two-user amplify-and-forward overlay cognitive network is developed by maximizing the averaged aggregate throughput of the secondary network. In particular, observing that the sensing duration lies within a strict interval, the jointly optimal strategy of sensing time and power allocation is proved to be tractable by sequential optimization.     

MIMO认知无线电网络中的联合收发波束形成算法研究

利用半正定规划和最小均方误差准则,提出了一种最小化发射功率的MIMO认知波束形成算法,与已有对偶算法相比,降低了发射功率,提高了系统性能。此外,还提出了SINR均衡的波束形成算法,解决了MIMO认知网络中认知用户的均衡通信问题。数值仿真结果表明,新算法在避免对主用户造成干扰的同时,能保证每一个用户的通信质量,均衡SINR水平得到了大幅提升。     

Channel allocation and reallocation for cognitive radio networks

In this paper, a new channel allocation and re‐location scheme is proposed for cognitive radio users to efficiently utilize available spectrums. We also present a multiple‐dimension Markov analytical chain to evaluate the performance of this scheme. Both analytical results and simulation results demonstrate that the new scheme can enhance the radio system performance significantly in terms of blocking probability, dropping probability, and throughput of second users. The proposed scheme can work as a non‐server‐based channel allocation, which has practical values in real engineering design. Copyright ©2011 John Wiley & Sons, Ltd.     

Optimal power allocation for cognitive radio networks with relay‐assisted directional transmission

In this paper, the problem of power allocation in a cooperative cognitive radio (CR) network is investigated. An optimal power allocation is proposed to maximize efficiency of the secondary network in which secondary users transmit simultaneously over a spectral band assigned to the primary users. The CR network employs directional relays to improve efficiency of the communication links and minimize interference introduced to the primary users. Unlike the conventional cooperative relay‐assisted network, the directional relays are grouped in clusters. This clustering technique along with directional transmission can significantly reduce interference to the primary links and improve the system performance. Two algorithms are also developed on the basis of the penalty method to determine unknown transmission powers. Some corroborant numerical examples are provided to illustrate quick convergence behavior of the proposed algorithms and great superiority of employing clustered directional relays in cooperative CR networks. Copyright © 2013 John Wiley & Sons, Ltd.     

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

In cognitive radio (CR) networks, secondary users should effectively use unused licensed spectrums, unless they cause any harmful interference to the primary users. Therefore, spectrum sensing and channel resource allocation are the 2 main functionalities of CR networks, which play important roles in the performance of a CR system. To maximize the CR system utility, we propose a joint out‐of‐band spectrum sensing and operating channel allocation scheme based on genetic algorithm for frequency hopping–based CR networks. In this paper, to effectively sense the primary signal on hopping channels at each hopping slot time, a set of member nodes sense the next hopping channel, which is called out‐of‐band sensing. To achieve collision‐free cooperative sensing reporting, the next channel detection notification mechanism is presented. Using genetic algorithm, the optimum sensing and data transmission schedules are derived. It selects a sensing node set that participate the spectrum sensing for the next expected hopping channel during the current channel hopping time and another set of nodes that take opportunity for transmitting data on the current hopping channel. The optimum channel allocation is performed in accordance with each node's individual traffic demand. Simulation results show that the proposed scheme can achieve reliable spectrum sensing and efficient channel allocation.     

空天地一体化网络中联合地面基站选择及功率分配

针对由卫星通信网络、地面移动网络以及空中飞行平台所组成的空天地一体化网络(Hybrid Satellite-Aerial-Terrestrial Network,HSAT)下行链路的系统吞吐量问题,提出了基于多目标遗传算法的地面基站选择及功率分配算法。在这一网络中,空中飞行平台需要地面基站为其进行中继通信。因此,地面中继基站的选择和功率分配决定了系统的吞吐量。所提算法为了取得对系统吞吐量更好的优化效果,将优化系统吞吐量和满足约束条件建模成两个优化目标,通过设计特有的迭代选择过程使地面基站选择和功率分配不断优化。仿真分析表明,所提算法在保证用户最低通信需求的前提下,有效提升了系统的吞吐量。     

Joint channel selection and power control for video streaming over D2D communications based cognitive radio networks

A joint channel selection and power control scheme is developed for video streaming in device-to-device (D2D) communications based cognitive radio networks. In particular, physical queue and virtual queue models by applying ‘M/G/1 queue ’and ‘M/G/1 queue with vacations’ theories are built up, respectively, to evaluate the delays experienced by various video traffics. Such delays play a vital role in calculating the packet loss rate for video streaming, which reflects the video distortion. Based on the distortion model, a video distortion minimization problem is formulated, subject to the rate constraint, maximum power constraint, primary users’ tolerant interference constraint, and secondary users’ minimum data rate requirement constraint. The optimization problem turns out to be a mixed integer nonlinear programming (MINLP), which is generally nondeterministic in polynomial time. A Lagrange dual method is thus employed to reformulate the video distortion minimization problem, based on which the sub-gradient algorithm is used to determine a relaxed solution. Thereafter, applying the iterative user removal yields the optimal joint channel selection and power control solution to the original MINLP problem. Extensive simulations validate our proposed scheme and demonstrate that it significantly increases the peak signal-to-noise ratio (PSNR) compared with the existing schemes.