Effect of Primary Interference on Cognitive Relay Network

Cognitive relay network is a method for optimizing frequency spectrum utilization. What’s important in these networks is to transmit data such that none of primary and secondary users cause destructive interference to other users. Although primary interference affect cognitive network performance, but is neglected in former researches. In this paper, we show cognitive network performance by calculating outage probability. We consider both primary and secondary interference links. Finally, our study is corroborated by representative numerical example. Simulation results demonstrate that increasing interference threshold increase outage probability and increasing data transmit rate cause outage probability increase

Power allocation in wireless multi-user relay networks

In this paper, we consider an amplify-and-forward wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. Conventionally, each relay equally distributes the available resources to its relayed sources. This approach is clearly sub-optimal since each user experiences dissimilar channel conditions, and thus, demands different amount of allocated resources to meet its quality-of-service (QoS) request. Therefore, this paper presents novel power allocation schemes to i) maximize the minimum signal-to-noise ratio among all users; ii) minimize the maximum transmit power over all sources; iii) maximize the network throughput. Moreover, due to limited power, it may be impossible to satisfy the QoS requirement for every user. Consequently, an admission control algorithm should first be carried out to maximize the number of users possibly served. Then, optimal power allocation is performed. Although the joint optimal admission control and power allocation problem is combinatorially hard, we develop an effective heuristic algorithm with significantly reduced complexity. Even though theoretically sub-optimal, it performs remarkably well. The proposed power allocation problems are formulated using geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal solution can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach

Modulation-adaptive cooperation schemes for wireless networks

Abstract-Cooperative communications can exploit the distributed spatial diversity-gain to improve the link performance. In this paper, we investigate the application of adaptive modulation concept to the decode-and-forward (DF) based cooperative network. With the relay nodes geographically close to the destination, we assume the perfect channel feedback is available only at the relay nodes, and propose a class of novel modulation-adaptive cooperation schemes (MACSs). The proposed schemes are first investigated in the single-relay scenario, and then extended to the multi-relay scenario. Simulation results show that the proposed schemes can offer the significant throughput-improvement in comparison with conventional DF systems

Optimal Distributed Resource Allocation for Decode-and-Forward Relay Networks

This paper presents a distributed resource allocation algorithm to jointly optimize the power allocation, channel allocation and relay selection for decode-and-forward (DF) relay networks with a large number of sources, relays, and destinations. The well-known dual decomposition technique cannot directly be applied to resolve this problem, because the achievable data rate of DF relaying is not strictly concave, and thus the local resource allocation subproblem may have non-unique solutions. We resolve this non-strict concavity problem by using the idea of the proximal point method, which adds quadratic terms to make the objective function strictly concave. However, the proximal solution adds an extra layer of iterations over typical duality based approaches, which can significantly slow down the speed of convergence. To address this key weakness, we devise a fast algorithm without the need for this additional layer of iterations, which converges to the optimal solution. Our algorithm only needs local information exchange, and can easily adapt to variations of network size and topology. We prove that our distributed resource allocation algorithm converges to the optimal solution. A channel resource adjustment method is further developed to provide more channel resources to the bottleneck links and realize traffic load balance. Numerical results are provided to illustrate the benefits of our algorithm