Abstract
Abstract In this paper, we propose a generalized minimum mean square error (MMSE) beamforming for downlink multicell multiple-input multiple-output (MIMO) systems with local BS cooperations. Unlike the previous beamforming strategies which have been designed for an idealized multicell MIMO system model, we consider a complicated but realistic multicell MIMO system model. Our realistic multicell MIMO system model captures (i) different average SNRs of users due to random geometrical distribution of users within cells, (ii) channel feedback latency due to air propagation time and signal processing time, and (iii) different channel aging effects for intracell channel information and intercell channel information due to an additional backhaul latency during the exchange of information between pairs of neighboring cells. The key novelty of this paper is that we derive the closed-form beamforming expressions of the generalized MMSE beamforming using convex optimization technique. The closed-form beamforming expression gives insights on how the geometry of users and channel feedback latency affect the construction of optimal beamforming vectors. Numerical results verify that the proposed generalized MMSE beamforming outperforms previous beamforming strategies in both BER and average throughput performances.
Highlights
With increasing demands of various multimedia service, the generation wireless communication systems are expected to support much higher data rate than before
This confirms that the conventional minimum mean square error (MMSE) beamforming for single-cell multiple-input multiple-output (MIMO) systems is a special case of the proposed generalized MMSE beamforming for multicell MIMO systems when interference-to-noise ratio (INR) signal-to-noise ratio (SNR)
5 Numerical results we present numerical results to evaluate the proposed generalized MMSE beamforming in terms of BER and average throughput
Summary
With increasing demands of various multimedia service, the generation wireless communication systems are expected to support much higher data rate than before. A beamforming technique that computes the best beamforming vector by defining a new metric called signal-to-generating interference-plusnoise ratio (SGINR) has been proposed for multicell MIMO systems in [9]. We consider the design of downlink beamforming technique for multicell MIMO systems with local CSI sharing. In downlink multicell MIMO systems with local CSI sharing, MSs need to acquire channel information such as gk,m,j and ηk,m,j by monitoring reference signals from neighboring cells. After the MSs report the information to their own BSs with channel feedback, neighboring BSs share it through the backhaul network It has been proven in [18,19] that the first-order Gauss-Markov process model well describes relatively small delays in the communication systems. As shown in [19], the maximum Doppler frequency can be represented by fD vk ,m fc c where vk,m is the velocity of the kth user in the mth cell, fc is the carrier frequency, and c is the speed of light, respectively
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