Low-cost Interference Mitigation and Relay Processing for Cooperative DS-CDMA Systems

In wireless communications, propagation aspects such as fading, shadowing and path loss are the major constraints that seriously limit the overall performance of systems. Indeed, severe fading has a detrimental effect on the received signals and can lead to a degradation of the transmission of information and the reliability of the network. In this case, diversity techniques are introduced in order to mitigate fading. Among various kinds of diversity techniques, cooperative diversity with relaying nodes is a modern technique that has been widely considered in recent years as an effective tool to deal with this problem. Several cooperative protocols have been proposed in the literature, and among the most effective ones are Amplify-and-Forward (AF) and Decode-and-Forward (DF). Cooperative diversity can be combined with direct sequence code division multiple access (DS-CDMA) systems to further enhance the information security. However, due to the multiple access interference (MAI) that arises from nonorthogonal received waveforms in the DS-CDMA systems, the system performance may easily be affected. To deal with this issue, novel multiuser detection (MUD) technique is introduced as a useful relay processing strategy for the uplink of cooperative DS-CDMA systems. Apart from that, distributed space-time coding (DSTC) is another effective approach that can be combined with cooperative diversity to further improve the transmission performance. Moreover, in order to increase the throughput of the cooperative DS-CDMA network, physical-layer network coding (PNC) scheme is then adopted together with the cooperative DS-CDMA network. Clearly, better performance gain and lower power consumption can be obtained when appropriate relaying strategies are applied

Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding

In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052