Abstract-We consider a Gaussian multiple-access channel (MAC) with an amplify-and-forward (AF) relay, where all nodes except the receiver have multiple antennas and the direct links between transmitters and receivers are neglected. Thus, spatial processing can be applied both at the transmitters and at the relay, which is subject to optimization for increasing the data rates. In general, this optimization problem is non-convex and hard to solve. While in prior work on this problem, it is assumed that all transmitters access the channel jointly, we propose a solution where each transmitter accesses the channel exclusively, using a time-division multiple-access (TDMA) scheme. It is shown that this scheme provides higher achievable sum rates, which raises the question of the need for TDMA to achieve the general capacity region of MACs with AF relay.

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In this paper the cognitive interference channel with a common message, a variation of the classical cognitive interference channel in which the cognitive message is decoded at both receivers, is studied. For this channel model new outer and inner bounds are developed as well as new capacity results for both the discrete memoryless and the Gaussian case. The outer bounds are derived using bounding techniques originally developed by Sato for the classical interference channel and Nair and El Gamal for the broadcast channel. A general inner bound is obtained combining rate-splitting, superposition coding and binning. Inner and outer bounds are shown to coincide in the "very strong interference" and the "primary decodes cognitive" regimes. The first regime consists of channels in which there is no loss of optimality in having both receivers decode both messages while in the latter regime interference pre-cancellation at the cognitive receiver achieves capacity. Capacity for the Gaussian channel is shown to within a constant additive gap and a constant multiplicative factor.

SUMMARYIn this paper, we consider a multiple-input-multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) downlink scenario, where each receiving mobile station has quality of service requirements, namely minimum rate requirements. For this problem we propose three heuristic resource allocation algorithms, which have a much lower complexity than the existing optimal solution (opt). We compare and evaluate these algorithms according to sum rate performance and complexity. The first strategy is based on a heuristic sum rate maximisation algorithm using the so-called eigenvalue updates. In our second algorithm, we make use of the duality of uplink and downlink, which allows us to do the allocation in the dual uplink. Finally, our third algorithm is based on the well-known zero-forcing dirty paper coding (ZF-DPC) principles, which use the Gram-Schmidt process to orthogonalise the transmissions towards the different users.