A hybrid overlay/underlay spectrum sharing method for cognitive radio networks based on user classification and convex optimization is proposed. Interference radii are configured for the primary receiver and each cognitive receiver. Cognitive users are divided into four groups and allocated different spectrum sharing patterns according to their distance from the primary transmitter and receiver. An optimal power allocation scheme that achieves the maximum sum rate of cognitive radio system on the premise of satisfying the interference constraint of primary receiver is acquired through the convex optimization method. Performance analysis and simulation results show that, compared with existing methods, our method leads to improved performance of achievable sum rate of cognitive users while guarantees the transmission of primary users.

This letter investigates the performance of amplify-and-forward relaying systems using maximum ratio transmission at the source. A closed-form expression for the outage probability and a closed-form lower bound for the average bit error probability of the system are derived. Also, the approximate expressions for the outage probability and average bit error probability in the high signal-to-noise ratio regime are given, based on which the optimal power allocation strategies to minimize the outage probability and average bit error probability are developed. Furthermore, numerical results illustrate that optimizing the allocation of power can improve the system performance, especially in the high signal-to-noise ratio regime.

Recently, wireless multi-hop network using MIMO two-way relaying technique has been attracted much attention owing to its high network efficiency. It is well known that the MIMO two-way multi-hop network (MTMN) can provide its maximum throughput in uniform topology of node location. However, in realistic environments with non-uniform topology, network capacity degrades severely due to unequal link quality. Furthermore, the end-to-end capacity also degrades at high SNR due to far (overreach) interference existing in multi-hop relay scenarios. In this paper, we focus on several power allocation schemes to improve the end-to-end capacity performance of MTMN with non-uniform topology and far interference. Three conventional power allocation schemes are reformulated and applied under the system model of MTMN. The first two are centralized methods, i.e., Eigenvector based Power Allocation (EPA) which employs linear algebra and Optimal Power Allocation (OPA) using convex optimization. The last one is Distributed Power Allocation (DPA) using game theory. It is found from numerical analyses that the power allocation schemes are effective for MTMN in terms of end-to-end capacity improvement, especially in non-uniform node arrangement and at high SNR.