2005/02/07 by M.J.M. Peacock, Iain B. Collings, Peacock, Matthew J. M. +3
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Microwave Imaging and Scattering Analysis #Sparse and Compressive Sensing Techniques #Wireless Communication Networks Research #Wireless Communication Security Techniques
paper · pdf · doi:10.48550/arxiv.cs/0502042
openalex publication_date 2005/02/07 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
We present a unified large system analysis of linear receivers for a class of\nrandom matrix channels. The technique unifies the analysis of both the\nminimum-mean-squared-error (MMSE) receiver and the adaptive least-squares (ALS)\nreceiver, and also uses a common approach for both random i.i.d. and random\northogonal precoding. We derive expressions for the asymptotic\nsignal-to-interference-plus-noise (SINR) of the MMSE receiver, and both the\ntransient and steady-state SINR of the ALS receiver, trained using either\ni.i.d. data sequences or orthogonal training sequences. The results are in\nterms of key system parameters, and allow for arbitrary distributions of the\npower of each of the data streams and the eigenvalues of the channel\ncorrelation matrix. In the case of the ALS receiver, we allow a diagonal\nloading constant and an arbitrary data windowing function. For i.i.d. training\nsequences and no diagonal loading, we give a fundamental relationship between\nthe transient/steady-state SINR of the ALS and the MMSE receivers. We\ndemonstrate that for a particular ratio of receive to transmit dimensions and\nwindow shape, all channels which have the same MMSE SINR have an identical\ntransient ALS SINR response. We demonstrate several applications of the\nresults, including an optimization of information throughput with respect to\ntraining sequence length in coded block transmission.\n