Adaptive Reduced-Rank Equalization Algorithms Based on Alternating Optimization Design Techniques for MIMO Systems

Abstract

This paper presents a novel adaptive reduced-rank multi-input multi-output (MIMO) equalization scheme and algorithms based on alternating optimization design techniques for MIMO spatial multiplexing systems. The proposed reduced-rank equalization structure consists of a joint iterative optimization of two equalization stages, namely, a transformation matrix that performs dimensionality reduction and a reduced-rank estimator that retrieves the desired transmitted symbol. The proposed reduced-rank architecture is incorporated into an equalization structure that allows both decision feedback and linear schemes for mitigating the inter-antenna and inter-symbol interference. We develop alternating least squares (LS) expressions for the design of the transformation matrix and the reduced-rank estimator along with computationally efficient alternating recursive least squares (RLS) adaptive estimation algorithms. We then present an algorithm for automatically adjusting the model order of the proposed scheme. An analysis of the LS algorithms is carried out along with sufficient conditions for convergence and a proof of convergence of the proposed algorithms to the reduced-rank Wiener filter. Simulations show that the proposed equalization algorithms outperform the existing reduced-rank and full-rank algorithms, while requiring a comparable computational cost.

Bidirectional algorithms for interference suppression in multiuser systems

“…The enforcement of the constraint is performed by the denominator of (27) which ensures that the receive filter w[i] does not tend towards a zero correlator as the adaptation progresses.…”

“…However, the stability of adaptive step-sizes and forgetting factors can be a concern unless they are constrained to lie within a predefined region [19]. Other alternative schemes include those based on processing the received data in subblocks [20][21][22] and subspace algorithms [23][24][25][26][27][28]. In addition, the fundamental problem of obtaining the unfaded symbols whilst suppressing MUI remains.…”

A low-complexity MIMO subspace detection algorithm

“…By allowing subspaces to overlap, additional diversity can be gathered by putting a low reliable data stream into several detection sets. Other subspace methods employ projection operators and lists to generate candidates for interference cancellation in equalization schemes (e.g., [40][41][42][43][44]). …”

New rank detection methods for reduced-rank MIMO systems

“…When the channel matrix has reduced rank, the number of its entries is larger than its real dimension, and thus designs based on full-rank channels become inefficient. This motivates the research on reduced-rank technologies for MIMO systems [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”