Interference alignment (IA) is a novel technique to achieve the optimal degree of freedom of wireless communication systems through efficient interference management. In a large size multi-cell network, IA over a full...
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Interference alignment (IA) is a novel technique to achieve the optimal degree of freedom of wireless communication systems through efficient interference management. In a large size multi-cell network, IA over a full connected model requires the impractical number of transmit/receive antennas. Moreover, extensive channel state information is delivered over the backhaul between different base-stations. For realistic scenarios with limited quantity of transceiver antennas, such a full IA scheme may even become infeasible. In this study, by exploiting the heterogeneous path losses, the authors propose a novel partial IA scheme to enhance the throughput of multi-cell networks, which requires relatively small amounts of antennas and hence can be practically implemented. They first formulate the partial IA problem in terms of a mixedinteger bi-level non-linear optimal program. Then, they decompose the problem into two sub-problems to reduce the computation complexity and, furthermore, introduce two algorithms for interference links selection. It is shown that, in a 19 hexagonal wrap-around-cell layout, their proposed algorithm outperforms a standard multi-user multi-input-multi-output technique with far less transmit antennas. The present scheme is therefore of great promise to practical applications.
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