Adaptive Network Coding for Spectrum Sharing Systems

In this paper, we propose an adaptive network coding scheme for cognitive relay networks comprising multiple secondary sources communicating with a common destination in the presence of multiple primary users. Conventional network coding schemes developed for cognitive radio networks normally use global encoding kernels to achieve the minimum end-to-end outage probability. Finding the global kernel is computationally inefficient especially when the number of nodes in a network changes. To this end, we propose a network coding scheme that evenly groups the codewords into multiple subsets, linearly combines the network encoded codewords over the reduced subset, and dynamically adjusts the encoding set size to minimize the end-to-end outage probability. An advantage of the proposed network coding scheme is that it achieves lower end-to-end outage probability as compared to the conventional network coding scheme over the whole signal-to-noise ratio (SNR) range with a small additional overhead. We derive closed-form expressions for the link outage probability while taking the interference constraints into consideration. We also derive the exact end-to-end outage probability of the proposed scheme and compare its performance to that of conventional fixed network coding. We show that the proposed scheme provides a trade-off between the probability of relay cooperation and network coding gain. We demonstrate through numerical examples that the proposed adaptive network coding scheme achieves gains of more than 4 dB at a target outage probability of 10^-2 as compared to conventional fixed network coding schemes.