Iterative Linear Minimum Mean Square Research Articles

Efficient Hybrid Linear Massive MIMO Detector Using Gauss–Seidel And Successive Over-Relaxation

The initial solution of a massive multiple-input multiple-output (M-MIMO) detector for uplink (UL) is greatly influence the balance between the bit error rate (BER) performance and the computational complexity. Although the maximum likelihood (ML) detector obtains the best BER performance, it has an extremely high computational complexity. Iterative linear minimum mean square error (MMSE) detector based on the Gauss–Seidel (GS), the successive over-relaxation (SOR), and the Jacobi (JA), obtains a good performance-complexity profile when the base station (BS)-to-user-antenna-ratio (BUAR) is large. However, when the BUAR is small, the system suffers from a considerable performance loss. In this paper, a hybrid detector based on the joint GS and SOR methods is proposed where the initial solution is determined by the first iteration of GS method. Numerical results show a considerable complexity reduction and performance enhancement using the proposed GS-SOR method over all methods when the BUAR is small.

Efficient initialisation of iterative linear massive MIMO detectors using a stair matrix

Several approximate matrix inversion methods have been used in linear massive MIMO uplink detectors where their convergence rate, performance, and complexity are greatly affected by the initial solution. In this Letter, the authors exploit a stair matrix, instead of a diagonal matrix, in initialising iterative linear minimum mean square error massive MIMO detector based on several approximate matrix inversion methods, namely, the Gauss-Seidel, successive over relaxation, Richardson iteration, and Newton iteration methods. Numerical results show a significant performance enhancement without a burden of extra complexity using a stair matrix over a diagonal matrix in all methods.

Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection

This paper considers a low-complexity iterative Linear Minimum Mean Square Error (LMMSE) multi-user detector for the Multiple-Input and Multiple-Output system with Non-Orthogonal Multiple Access (MIMO-NOMA), where multiple single-antenna users simultaneously communicate with a multiple-antenna base station (BS). While LMMSE being a linear detector has a low complexity, it has suboptimal performance in multi-user detection scenario due to the mismatch between LMMSE detection and multi-user decoding. Therefore, in this paper, we provide the matching conditions between the detector and decoders for MIMO-NOMA, which are then used to derive the achievable rate of the iterative detection. We prove that a matched iterative LMMSE detector can achieve (i) the optimal capacity of symmetric MIMO-NOMA with any number of users, (ii) the optimal sum capacity of asymmetric MIMO-NOMA with any number of users, (iii) all the maximal extreme points in the capacity region of asymmetric MIMO-NOMA with any number of users, (iv) all points in the capacity region of two-user and three-user asymmetric MIMO-NOMA systems. In addition, a kind of practical low-complexity error-correcting multiuser code, called irregular repeat-accumulate code, is designed to match the LMMSE detector. Numerical results shows that the bit error rate performance of the proposed iterative LMMSE detection outperforms the state-of-art methods and is within 0.8dB from the associated capacity limit.

ISI Free Channel Equalization for Future 5G Mobile Terminal Using Bio-inspired Neural Networks

This article presents the concept of future 5th generation (5G) wireless communications as an existing beyond 4G systems like long term evolution (LTE) which means the indicate of 5G scenarios can be introduced in near future. Therefore, this paper deals of a future 5G mobile terminal for applications to uplink transmission in a multiuser LTE scheme. Unfortunately, LTE-uplink inherently generates significant inter-symbol interference especially high bandwidth scenarios. The result is a rise to mutual interference among active users with an increased error rate. This incidence eventually causes non-orthogonal user spreading codes. Moreover, this drawback is known as the multiple access interference episodes which demonstrate high computational complexity and enhances symbol error rate at the receiving end and degrades the communication quality. Most of the related work has been claimed iterative linear minimum mean square error (LMMSE) detection requires a matrix inversion role which has a high computational complexity and contains a combinatorial optimization problem. Consequently, the LMMSE does not meet the requirement to implement real-time detection with low complexity and thus limiting its application. Therefore, we propose an acceptable bio-inspired neural network (NN) in the case of single and multilayer NNs with supervised learning particularly Levenberg---Marquardt backpropagation learning algorithm to improve the convergence speed. Simulation results performed with highest approaches highlights a better act for the proposed system.

Power Allocation for Linearly Precoded OFDM Systems with Imperfect CSIT

This paper is concerned with the energy-efficient transmission in linearly precoded orthogonal frequency division multiplexing (OFDM) systems with imperfect channel state information at the transmitter (CSIT). Convolutionally channel coding and iterative linear minimum mean square error (LMMSE) detection are employed. We focus on the design of power allocation strategies for this system to exploit the potential benefit of available CSIT. Numerical results demonstrate that the proposed designs can achieve the advantage provided by the available CSIT efficiently, and meanwhile are robust to the CSIT uncertainty.

Towards the Performance of ML and the Complexity of MMSE: A Hybrid Approach for Multiuser Detection

In this paper, we consider a low-complexity multiuser detection for downlink of high speed packet access (HSPA) of universal mobile telecommunications system (UMTS). Instead of attempting to perform maximum likelihood (ML) detection of all users in multiple cells, which is impractical for battery powered mobile receiver, we utilize interference cancelled chips obtained from iterative linear minimum mean square error (LMMSE) estimation to perform a near ML detection in a reduced dimensional system. As a result, the worst case complexity of the detection process, achieved by the closest lattice point search (CLPS), is bounded to a controllable level irrespective of multipath spans. Furthermore, by exploiting the LMMSE estimate in tightening the hypersphere condition of the CLPS algorithm so called sphere decoding, we achieve significant improvement in search complexity. From simulations on realistic downlink communication scenario in HSPA systems, we show that the proposed method offers substantial performance gain over conventional receiver algorithms with reasonable complexity.

Decision-directed channel estimation based on iterative linear minimum mean square error for orthogonal frequency division multiplexing systems

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.

On water-filling precoding for coded single-carrier systems

We study the spectrum shaping gain in single carrier systems through water-filling precoding. We show that water-filling precoding outperforms other existing alternatives when iterative linear minimum mean square error (LMMSE) detection is involved, which is confirmed by both evolution analysis and simulation results. This technique does not incur any extra receiver cost when it is used together with frequency domain equalization (FDE).