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.     

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.     

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.     

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...     

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.     

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.     

认知无线网络中功率和信道接入的联合优化

在认知无线网络中,针对系统中所有用户能否接入同一信道进行通信的问题,提出了适用于不同情形的联合优化方案.在系统不可行的情形下,提出了联合优化功率和接入控制的方案.由于接入控制是NP问题,于是利用所提LP-PSO算法来实现优化控制,并分析和证明了所提算法的收敛性.在系统可行的情形下,提出了联合优化功率和传输速率分配的方案,通过改进拉格朗日对偶(Lagrange duality)算法来实现优化控制.数值结果分析表明,相比其他接入控制算法,虽然所提算法的时间复杂度有所增加但能够快速收敛,在有效地提高接入量的同时还能降低功率消耗,并能使传输速率得到更公平的分配.     

Optimal resource allocation scheme for cognitive radio networks with relay selection based on game theory

In this paper,we present a non-transferable utility coalition graph game(NTU-CGG) based resource allocation scheme with relay selection for a downlink orthogonal frequency division multiplexing(OFDMA) based cognitive radio networks to maximize both system throughput and system fairness.In this algorithm,with the assistance of others SUs,SUs with less available channels to improve their throughput and fairness by forming a directed tree graph according to spectrum availability and traffic demands of SUs.So this scheme can effectively exploit both space and frequency diversity of the system.Performance results show that,NTU-CGG significantly improves system fairness level while not reducing the throughput comparing with other existing algorithms.     

Distributed joint spectrum detection and power allocation in multi-band cognitive radio networks

This article proposes a novel dynamic spectrum sharing scheme in distributed multi-band cognitive radio networks. A non-cooperative game has been utilized to model the spectrum sharing among secondary base stations (SBSs). A distributed joint spectrum detection and power allocation algorithm is designed for maximizing the downlink throughput of secondary networks. Simulation results demonstrate that the proposed algorithm converges fast and achieves a better throughput performance than uniform threshold case. Meanwhile, the convergence of algorithm is proved by Nikaido-Isoda (N-I) function method.     

Stochastic approach for channel selection in cognitive radio networks using optimization techniques

Telecommunication Systems - Secondary Users (SU) are guided by Cognitive Radio Device in identifying a channel licensed to Primary Users (PU) when it is free. Whenever PU arrives, SU shall vacate...     

Adaptive power control and beam-forming joint optimization in cognitive radio networks

A novel adaptive power control and beam-forming joint optimization algorithm is proposed in cognitive radio(CR) underlay networks,where cognitive network share spectrum with primary network which spectrum is licensed.In this paper,both primary base station(PBS) and cognitive base station(CBS) are all equipped with multi antennas,while each primary user(PU) and cognitive user(CU) has only one antenna.Different from traditional algorithms,an adaptive weight factor generating solution is supplied to different access users(both PUs and CUs) in this paper,and the different priority of users is also considered,because PUs have higher priority,the weight factor of PUs is fixed as constant and signal-to-interference and noise ratio(SINR) threshold is unchanged,while for CUs,it is set adaptively and SINR threshold is also changed accordingly.Using this algorithm,the transmit power is decreased,which relax the strict requirements for power amplifier in communication systems.And moreover,owing to PUS has fixed SINR threshold,the calculated SINR at receiver is nearly unchanged,but for CUs,the SINR is changing with the adaptive weight factor.Under the assurance of quality of service(QoS) of PUs,the solution in this paper can enable CRs access to the CR network according to adaptive SINR threshold,therefore which supplies higher spectrum utilization efficiency.     

Joint optimization of power control and time slot allocation for wireless body area networks via deep reinforcement learning

Wireless Networks - E-healthcare system based on wireless body area network (WBAN) promises to produce potential benefits in health-care industry. A major issue of such an on-body networked system...     

Fuzzy logic for cross-layer optimization in cognitive radio networks [cognitive radio communications and networks]

The search for the ultimate architecture for cross-layer optimization in cognitive radio networks is characterized by challenges such as modularity, interpretability, imprecision, scalability, and complexity constraints. In this article we propose fuzzy logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.