Base Station Mobile Station Research Articles

Beamforming Optimization for Full-Duplex Wireless-Powered MIMO Systems

We propose techniques for optimizing transmit beamforming in a full-duplex multiple-input-multiple-output wireless-powered communication system, which consists of two phases. In the first phase, the wireless-powered mobile station (MS) harvests energy using signals from the base station (BS), whereas in the second phase, both MS and BS communicate to each other in a full-duplex mode. When complete instantaneous channel state information (CSI) is available, the BS beamformer and the time-splitting (TS) parameter of energy harvesting are jointly optimized in order to obtain the BS–MS rate region. The joint optimization problem is non-convex, however, a computationally efficient optimum technique, based upon semidefinite relaxation and line-search, is proposed to solve the problem. A sub-optimum zero-forcing approach is also proposed, in which a closed-form solution of TS parameter is obtained. When only the second-order statistics of transmit CSI is available, we propose to maximize the ergodic information rate at the MS while maintaining the outage probability at the BS below a certain threshold. An upper bound for the outage probability is also derived and an approximate convex optimization framework is proposed for efficiently solving the underlying non-convex problem. Simulations demonstrate the advantages of the proposed methods over the sub-optimum and half-duplex ones.

Dynamic channel management in MIMO OFDM cellular systems

AbstractWe consider dynamic channel management (DCM), more specifically power control and channel allocation, in multi‐carrier multi‐antenna wireless cellular systems. A quasi‐static channel model is assumed such that the channels remain approximately time‐invariant within each packet. On a packet‐by‐packet basis, the mobile station (MS) receivers feed back the DCM parameters to the corresponding base station (BS) transmitters in order to minimize the transmission power and strive to satisfy every individual user's data‐rate request. While optimum, centralized DCM for all active cells typically requires solution of a non‐convex optimization problem (which is subject to local minima) and requires intense channel feedback to a central controller. We instead consider sub‐optimal distributed DCM, where each individual cell locally optimizes its own performance based on the local channel information. In particular, we compare three distributed DCM schemes, namely the two‐dimensional DCM, which jointly adjusts both spatial and frequency sub‐channels of each BS–MS link, the one‐dimensional DCM, which adjusts only the frequency sub‐channels and the scalar DCM, which adjusts each BS–MS link by one scalar power coefficient. These DCM schemes are investigated in multi‐cell environments with realistic parameters; and it is seen that due to the multiple antennas, the maximum supportable rate (i.e. the rate supported by practically finite transmission power) is significantly increased, for example by a factor of two for users using four antennas and close to cell boundary. Copyright © 2004 John Wiley & Sons, Ltd.