Quantized Ergodic Radio Resource Allocation in OFDMA-Based Cognitive DF Relay-Assisted Networks

In this paper, the downlink ergodic resource allocation (ERA) in a relay-assisted OFDMA-based cognitive network is considered with the objective of maximizing the average secondary service sum-rate. This is subject to the average total transmission power constraint and the collision probability constraint on each subcarrier at each hop of transmission to guarantee the primary quality of service with any arbitrarily high probability. In the proposed scheme, no interaction between secondary and primary networks is necessary as opposed to previously proposed frameworks. To reduce the signaling overhead between secondary base station and secondary users, which is considerably higher in relay-assisted networks compared to ordinary networks, we propose to use channel quantization. In channel quantization instead of channel gain values, the index of the fading region corresponding to that value is fed back. Due to the probabilistic nature of the collision probability constraint, the proposed ERA problem cannot be solved by conventional methods such as the dual decomposition method. Hence, we propose two novel sub-optimal solutions called Iterative Approach and Analytical Approach. Simulations results indicate the efficiency of the proposed solutions with the iterative approach slightly outperforming the analytical approach at the expense of higher complexity. We also compare continuous and quantized ERA. Simulation results demonstrate a trade-off between the volume of required feedback information and performance.