Linear Power Constraints Research Articles

Characterising the Pareto frontier of multiple access rate region: a study on the effect of decoding order on achievable performance

For a multiple access channel, each user has its own power constraint. However, when a multiple access channel is being considered as the dual of a broadcast channel, it must exploit the additional freedom in power allocation and the constraint should be a sum power constraint. Duality helps us to transform the non-convex problems in a broadcast channel to convex problems in a multiple access channel. This helps to solve sum-rate optimisation problems with linear power constraints. However, maximising the sum-rate does not completely characterise the entire rate region boundary, formally called as the Pareto frontier. This work first reviews some of the existing results of polymatroid formulation from the Pareto optimality perspective and then proposes a complete characterisation of the Pareto frontier to show its relationship with the sum-rate optimisation problem. The significance of decoding order on the achievable rate region is considered. The work also shows some of the decoding orders to be suboptimal in Pareto sense and proposes an algorithm to find the correct decoding order based on power allocation. Simulation results are presented to support the theoretical arguments.

Joint MMSE Transceiver Designs and Performance Benchmark for CoMP Transmission and Reception

Coordinated Multipoint (CoMP) transmission and reception has been suggested as a key enabling technology of future cellular systems. To understand different CoMP configurations and to facilitate the configuration selection (and thus determine channel state information (CSI) feedback and data sharing requirements), performance benchmarks are needed to show what performance gains are possible. A unified approach is also needed to enable the cluster of cooperating cells to systematically take care of the transceiver design. To address these needs, the generalized iterative approach (GIA) is proposed as a unified approach for the minimum mean square error (MMSE) transceiver design of general multiple-transmitter multiple-receiver multiple-input-multiple-output (MIMO) systems subject to general linear power constraints. Moreover, the optimum decoder covariance optimization approach is proposed for downlink systems. Their optimality and relationships are established and shown numerically. Five CoMP configurations (Joint Processing-Equivalent Uplink, Joint Processing-Equivalent Downlink, Joint Processing-Equivalent Single User, Noncoordinated Multipoint, and Coordinated Beamforming) are studied and compared numerically. Physical insights, performance benchmarks, and some guidelines for CoMP configuration selection are presented.

Optimality Properties, Distributed Strategies, and Measurement-Based Evaluation of Coordinated Multicell OFDMA Transmission

The throughput of multicell systems is inherently limited by interference and the available communication resources. Coordinated resource allocation is the key to efficient performance, but the demand on backhaul signaling and computational resources grows rapidly with number of cells, terminals, and subcarriers. To handle this, we propose a novel multicell framework with dynamic cooperation clusters where each terminal is jointly served by a small set of base stations. Each base station coordinates interference to neighboring terminals only, thus limiting backhaul signalling and making the framework scalable. This framework can describe anything from interference channels to ideal joint multicell transmission. The resource allocation (i.e., precoding and scheduling) is formulated as an optimization problem (P1) with performance described by arbitrary monotonic functions of the signal-to-interference-and-noise ratios (SINRs) and arbitrary linear power constraints. Although (P1) is non-convex and difficult to solve optimally, we are able to prove: 1) Optimality of single-stream beamforming; 2) Conditions for full power usage; and 3) A precoding parametrization based on a few parameters between zero and one. These optimality properties are used to propose low-complexity strategies: both a centralized scheme and a distributed version that only requires local channel knowledge and processing. We evaluate the performance on measured multicell channels and observe that the proposed strategies achieve close-to-optimal performance among centralized and distributed solutions, respectively. In addition, we show that multicell interference coordination can give substantial improvements in sum performance, but that joint transmission is very sensitive to synchronization errors and that some terminals can experience performance degradations.