无线协作网络中存在干扰时的中继选择协议

针对无线协作网络中共道干扰影响中继选择的问题，提出一种存在干扰的中继选择协议（RSI）．该协议以最小化系统中断概率为目标，选择具有最大瞬时端到端信干比的中继节点转发源节点信息，并且在考虑直接传输时，目的节点采用选择合并策略合并直接传输和中继传输，从而充分利用空间分集增益，降低了系统的中断概率．RSI协议不需要中继节点之间交互信息，通过竞争的方式完成数据传输，节省了系统开销，具有较好的实用性．仿真结果表明，在瑞利信道中，当中继节点个数为4、中断概率为10^-2时，与最大最小中继选择协议相比，RSI协议能够取得约3～4dB的信噪比增益．

基于能量收集技术的无线协作网络中继选择

针对目前能量收集技术能够收集到的可用能量受限,导致无线协作网络中继节点处易出现能量短板的问题,为了避免整个网络因中继节点大量死亡而瘫痪,提出了一种基于能量收集技术的无线协作网络中继选择方案,即联合最大能量和最大数据传输链路的中继选择方案。首先,基于节点的能量收集状况,选出每跳中能量最大的节点进行解码转发;然后,结合每连续两跳的链路传输状态,选出与源节点和目的节点之间的数据传输信道最优者作为中继节点。结合Nakagami-m信道衰落模型,将该方案与随机选择方案、最大数据链路信道增益(MaDs)方案和基于中继-窃听链路最小信道增益(BNBF)方案进行对比分析,结果表明:在满足收集的能量足够用于下一时隙能量收集和数据传输的前提下,用于能量收集的比例越小,网络中断概率越小;联合最大能量和最大数据传输链路的中继选择方案在网络中断性能方面优于其他方案,其中断概率随信噪比的增大而减小,特别是当平均信噪比为38dB时,网络中断概率降到10^-5。

基于SWIPT无线协作网络的中继选择算法研究

尽管无线协作网络(Wireless Cooperative Network,WCN)显著改善了通信质量和网络吞吐量,但由于中继节点能量限制,造成WCN仍存在网络生命续航和能量补充问题。提出基于携能通信技术的WCN,分析在Nakagami-m信道假设下的多中继节点模型,并研究最优中继节点的选取问题。推导出不同最优中继选择策略下的系统中断概率,并通过蒙特卡洛仿真实验验证其正确性;进一步分析影响系统中断概率的诸多因素。利用前馈神经网络提出基于机器学习的中继选择算法,仿真实验表明:最优中继选择正确率约为93%。

综合上下行链路的无线能量收集协作网络资源分配

为了提高和平衡时分双工系统上下行链路之间的能效,在无线能量收集(EH)协作网络中开展节能资源分配算法研究.建立考虑无线能量收集、频谱共享、系统功率受限的协作网络模型;基于所建模型,形成综合上下行链路、功率约束、带宽约束和上下行速率匹配的优化问题,分别优化系统的绿色能效和传统能效.对优化问题进行数学分析,得到资源分配的最优解,即源节点、中继节点和目的节点的最优发射功率,上下行链路最优的带宽分配,能量收集节点最优的功率分割以及最优的系统能效;归纳得到分别基于系统绿色能效最大化和基于系统能效最大化的资源分配算法.对所建模型与所得资源分配算法开展蒙特卡洛仿真分析,将所得算法与文献算法进行对比.仿真结果表明,通过综合优化系统上下行链路的资源分配,所得算法有效提升了绿色能效和传统能效.

协作认知无线网络中的功率优化及中继选择策略

如何在协作认知网络中有效地实现主要用户和认知用户的频谱共享,即如何在众多认知用户中选择合适的认知中继集是一个基本问题。通过确定并优化主要用户和认知用户效用函数来解决该问题,因采用了纳什均衡理论,故称之为基于博弈论的多中继选择算法(multiple relay selection based on game theory,GTMRS)。在任一认知中继集合中,认知用户之间能够形成非合作功率的博弈模型,可基于纳什均衡得到认知用户的优化协作功率分配算法。在寻找一组确定的中继集合来实现主要用户效用的最大化过程中,引入了修改的信道调和平均数因子,其目的是移除信噪比较小的中继节点,以最大化系统的信噪比。仿真结果显示,该算法能够使更多的认知用户接入到授权频谱中,同时使得主要用户获得更大的效用以及传输速率。因此,基于博弈的多中继选择算法能够有效选择合适的认知中继,并获得主要用户和认知用户在效用上的最优化。

认知协作无线电网络下基于能效的资源联合分配

在认知协作无线电网络中,当次用户盲目地参与到中继主用户中,会选择不利于次用户系统能效的主用户,从而降低次用户系统整体的能效。针对该问题,提出一种资源联合分配方案。次用户有权拒绝不利于自身系统的协助申请,而是选择有利于其系统能效的主用户进行协助。在确定主用户集合的情况下,通过有效地分配带宽和功率来提高次用户系统的能效。仿真结果表明,该方案能够在保证各用户服务质量的情况下,实现次用户系统能效的提高。

认知无线电网络频谱分配与协作集划分算法

针对协作认知无线电网络中较为复杂的多主用户与多次级用户共存场景,提出联合频谱分配与协作集划分问题,并将该问题形式化描述为整数0-1非线性规划问题,证明其是NP-hard的.首先,设计了集中式的遗传算法CGA(centralized genetic algorithm)对问题求解,对该算法进行齐次有限马尔可夫链建模并对其全局收敛性进行了分析;随后,提出了一种包含两阶段的分布式遗传算法DGA(distributed genetic algorithm),包括基于最小支配集的分簇与频谱预分配阶段和簇间协作集协商与簇内适应值精化阶段.此外,还提出一种快速收敛的DGA算法(fast-convergent DGA,简称FDGA)缩短分布式算法运行时间.仿真实验结果表明,根据能反映出算法性能的适应值结果对各算法进行比较:(1)小规模网络下CGA获得的解平均为通过穷举算法得到的最优值的92%;(2)随着网络规模的扩大,由于CGA搜索空间增大,DGA,FDGA在达到相同停机条件时获得的适应值比CGA提高约20%;(3)与DGA相比,FDGA虽能得到与DGA相近的结果,但却大大缩短了算法收敛的时间,更适应于大规模网络应用.

协作认知无线网络中基于优先级队列的两级中心频谱共享机制

针对协作认知无线网络(CCRN)设计了一种基于优先级队列的两级中心协作频谱共享机制(PQTL-CSS)。通过招募认知用户作为中继,协助完成授权通信,并将传统的数据协作拓展至管理协作,由协作认知节点协调其他认知节点的接入,形成由主用户和协作认知节点构成的两级中心管理结构。在保障主用户最高优先级的同时,作为对认知节点协助授权业务传输的回报,赋予其高于非协作认知节点的信道接入权限。本文对不同用户的时延和吞吐量性能进行仿真,结果表明,PQTL-CSS能够在业务随机性较强的情况下,实现多种类型节点的动态、高效频谱共享。

基于多维资源自适应分配的协作认知传输机制

该文针对协作认知无线电网络提出一种时域、频域和空域资源联合分配的传输机制,在改善授权用户传输性能的前提下使参与协作的认知用户获得合理的回报。所提机制利用多天线认知节点担任中继,通过自适应的时隙划分与带宽分配,对两跳传输链路中的瓶颈予以消除;并在包含多个协作认知用户的场景中,给出一种考虑公平性的中继选择算法。该方法一方面采用比例公平的思想实现认知用户间的公平性,另一方面通过调整奖励因子,使中继获得合理的回报。仿真结果表明,所提算法能够改善授权与认知系统的数据速率,同时给予认知中继公平的回报。

基于优先级QoS选择策略的水下网络中继算法

针对水下传感器网络通信过程中能量效率低、误码率高等问题,提出一种基于优先级服务质量(Quality of Service,QoS)选择策略的水下网络中继算法(PQSS)。算法首先采用有限状态马尔科夫链来建立源节点和中继节点之间的无线信道模型,并得到中继节点信噪比的状态转移概率矩阵。接着提出链路QoS函数,该函数考虑了传输能耗、误码率和数据速率作为链路QoS的评价度量,通过各度量并结合状态转移概率的计算来求出中继节点的链路QoS值范围,从而选择链路QoS值更大的中继节点来作为最佳的下一跳节点。最后仿真结果表明,上述算法相比基于分簇的水下传感器网络覆盖保持路由算法和基于洪泛的逐层能量高效路由协议,能量效率分别提高了21.4%和13.1%,网络平均生命周期分别提高了13.4%和7.6%。

Reputation-Based Hierarchically Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.

Broadband ＆ Digital Content-Couple Subscriber Cooperative Relaying Networks for Uplink Transmission using Hierarchical Game Approach

Cooperation allows wireless network users to benefit from various gains such as an in- crease in the achieved rate or an improvement in the bit error rate. In the paper, we propose a distributed Hierarchical Game （HG） theoretic framework over multi-user cooperative communication networks to stimulate cooperation and improve the network performance. First, we study a two- user decision making game in the OFDMA based subscriber cooperative relaying network, in which subscribers transmit their own data in the first phase, while helping to retransmit their partner＇s or choosing to freeride in the second phase. Instead of consulting to a global optimal solution, we decouple the cooperation resource allocation into two level subproblems： a user level Nash game for distributed cooperation decision and a Base Station （BS） level coalition game for centralized resource allocation. In the proposed HG algorithm, where mutual cooperation is preferred and total payoff is transferable, we prove it converges to a unique optimal equilibrium and resolve the subcarrier as-signment and power allocation among the couples. Besides, we discuss the existence of the publishing and rewarding coefficients in order to encourage cooperation. Then, we extend the HG to multi-user cases by coupling among subscribers according to the location information. The simulation results show that the proposed scheme with the distributed HG game achieves a well tradeoff between fairness and efficiency by improving the transmission efficiency of adverse users and outperforms those employing centralized schemes.

POWER CONTROL BASED COOPERATIVE OPPORTUNISTIC ROUTING IN WIRELESS SENSOR NETWORKS

This paper proposes an approach called PC-CORP (Power Control based Cooperative Opportunistic Routing Protocol) for WSN (Wireless Sensor Networks), providing robustness to the random variations in network connectivity while ensuring better data forwarding efficiency in an energy efficient manner. Based on the realistic radio model, we combine the region-based routing, rendezvous scheme, sleep discipline and cooperative communication together to model data forwarding by cross layer design in WSN. At the same time, a lightweight transmission power control algorithm called PC-AIMD (Power Control Additive Increase Multiplicative Decrease) is introduced to utilize the co- operation of relay nodes to improve the forwarding efficiency performance and increase the robustness of the routing protocol. In the simulation, the performance of PC-COPR is investigated in terms of the adaptation of variations in network connectivity and satisfying the QoS requirements of application.

A novel weighed cooperative bandwidth spectrum sensing for spectrum occupancy of cognitive radio network

In cognitive radio network(CRN), a secondary user(SU) may utilize the spectrum resource of the primary user(PU) and avoid causing harmful interference to the primary network(PN) via spectrum sensing. In the traditional time spectrum sensing, the SU cannot detect the PU's presence during its transmission, thus increasing interference to the PN. In this work, a novel weighed cooperative bandwidth spectrum sensing method is proposed, which allows multiple SUs to use part of the bandwidth to perform cooperative spectrum sensing throughout the whole frame in order to detect the PU's reappearance in time. The SU's spectrum efficiency is maximized by jointly optimizing sensing bandwidth proportion, number of cooperative SUs and detection probability, subject to the constraints on the SU's interference and the false alarm probability. Simulation results show significant decrease on the interference and improvement on the spectrum efficiency using the proposed weighed cooperative bandwidth spectrum sensing method.

Bidirectional secondary transmissions with energy harvesting in cognitive wireless sensor networks

To the existing spectrum sharing schemes in wireless-powered cognitive wireless sensor networks,the protocols are limited to either separate the primary and the secondary transmission or allow the secondary user to transmit signals in a time slot when it forwards the primary signal.In order to address this limitation,a novel cooperative spectrum sharing scheme is proposed,where the secondary transmission is multiplexed with both the primary transmission and the relay transmission.Specifically,the process of transmission is on a three-phase time-switching relaying basis.In the first phase,a cognitive sensor node SU1 scavenges energy from the primary transmission.In the second phase,another sensor node SU2 and primary transmitter simultaneously transmit signals to the SU1.In the third phase,the node SU1 can assist the primary transmission to acquire the opportunity of spectrum sharing.Joint decoding and interference cancellation technique is adopted at the receivers to retrieve the desired signals.We further derive the closed-form expressions for the outage probabilities of both the primary and secondary systems.Moreover,we address optimization of energy harvesting duration and power allocation coefficient strategy under performance criteria.An effective algorithm is then presented to solve the optimization problem.Simulation results demonstrate that with the optimized solutions,the sensor nodes with the proposed cooperative spectrum sharing scheme can utilize the spectrum in a more efficient manner without deteriorating the performance of the primary transmission,as compared with the existing one-directional scheme in the literature.

A Sensor-Service Collaboration Approach for Target Tracking in Wireless Camera Networks

Mobile target tracking is a necessary function of some emerging application domains, such as virtual reality, smart home and intelligent healthcare. However, existing portable devices for target tracking are resource intensive and high-cost. Camera tracking is an effective location tracking way for those emerging applications which can reuse the existing ubiquitous video monitoring system. This paper proposes a dynamic community-based camera collaboration(D3C) framework for target location and tracking. The contributions of D3C mainly include that(1) nonlinear perspective projection model is selected as the camera sensing model and sequential Monte Carlo is employed to predict the target location;(2) a dynamic collaboration scheme is proposed, it is based on the local community-detection theory deriving from social network analysis. The performance of proposed approach is validated by both synthetic datasets and real-world application. The experiment results show that D3C meets the versatility, real-time and fault tolerance requirements of target tracking applications.

A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks

A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.