The issue of energy-aware resource allocation in an amplify-and-forward (AF) relay-assisted multiple-antenna interference channel (IC) is considered. A novel interference neutralization (IN) scheme is proposed for relay design and, based on the IN relay matrix design, two algorithms are developed to jointly allocate the users’ transmit powers, beamforming (BF) and receive filters. The first algorithm considers a competitive scenario and employs a non-cooperative game-theoretic approach to maximize the individual energy efficiency (EE) of each communication link, defined as the ratio of the achievable rate over the consumed power. The resulting algorithm converges to a unique fixed point, has limited complexity, and can be implemented in a distributed fashion. The second algorithm employs fractional programming tools and sequential convex optimization to centrally allocate the users’ transmit powers, BF and receive filters for global energy efficiency (GEE) maximization. The resulting algorithm is guaranteed to converge and has limited computational complexity. Numerical results show that the competitive IN design achieves virtually the same performance as the cooperative design if IN is feasible, while the gap is small if perfect IN is not achievable.
Energy efficiency and interference neutralization in two-hop MIMO interference channels
A. Zappone;BUZZI, Stefano
2014-01-01
Abstract
The issue of energy-aware resource allocation in an amplify-and-forward (AF) relay-assisted multiple-antenna interference channel (IC) is considered. A novel interference neutralization (IN) scheme is proposed for relay design and, based on the IN relay matrix design, two algorithms are developed to jointly allocate the users’ transmit powers, beamforming (BF) and receive filters. The first algorithm considers a competitive scenario and employs a non-cooperative game-theoretic approach to maximize the individual energy efficiency (EE) of each communication link, defined as the ratio of the achievable rate over the consumed power. The resulting algorithm converges to a unique fixed point, has limited complexity, and can be implemented in a distributed fashion. The second algorithm employs fractional programming tools and sequential convex optimization to centrally allocate the users’ transmit powers, BF and receive filters for global energy efficiency (GEE) maximization. The resulting algorithm is guaranteed to converge and has limited computational complexity. Numerical results show that the competitive IN design achieves virtually the same performance as the cooperative design if IN is feasible, while the gap is small if perfect IN is not achievable.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
