Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems

In this paper, we consider a two-hop relay-assisted cognitive downlink OFDMA system (named as secondary system) dynamically accessing a spectrum licensed to a primary network, thereby improving the efficiency of spectrum usage. A cluster-based relay-assisted architecture is proposed for the secondary system, where relay stations are employed for minimizing the interference to the users in the primary network and achieving fairness for cell-edge users. Based on this architecture, an asymptotically optimal solution is derived for jointly controlling data rates, transmission power, and subchannel allocation to optimize the average weighted sum goodput where the proportional fair scheduling (PFS) is included as a special case. This solution supports decentralized implementation, requires small communication overhead, and is robust against imperfect channel state information at the transmitter (CSIT) and sensing measurement. The proposed solution achieves significant throughput gains and better user-fairness compared with the existing designs. Finally, we derived a simple and asymptotically optimal scheduling solution as well as the associated closed-form performance under the proportional fair scheduling for a large number of users. The system throughput is shown to be $\mathcal{O}\left(N(1-q_p)(1-q_p^N)\ln\ln K_c\right)$, where $K_c$ is the number of users in one cluster, $N$ is the number of subchannels and $q_p$ is the active probability of primary users.Comment: 29 pages, 9 figures, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSIN

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