Saleem Ahmed

Efficient list-sphere detection scheme for joint iterative multiple-input multiple-output detection

List-sphere detection (LSD) is a sub-optimal multiple-input multiple-output (MIMO) detection scheme which searches candidate symbol vectors that lie within a sphere of a given radius. This study presents an efficient LSD based method for a joint iterative MIMO detection scheme. The proposed method utilises a channel condition in order to define the list size. During the search process, the radius is adaptively updated to reduce the computational complexity. Owing to the list size and corresponding radius are adaptively determined by the channel condition, the authors can operate the detector at the most appropriate complexity to produce the required performance. Simulation results show that the proposed methods provide substantial complexity reduction without bit error rate performance degradation.

Efficient soft bit estimation for joint iterative multiple-input multiple-output detection

In this study, the authors propose a joint iterative detection and decoding (JIDD) method for a turbo coded multiple-input multiple-output (MIMO) system, with a linear order of complexity. Accurate estimation of soft information should be conditioned for excellent performance of the JIDD, but it usually requires an exponential order of complexity. They propose a method which improves the performance of soft interference cancellation minimum mean-squared error (SIC-MMSE) method by increasing the reliability of the soft information utilised for interference cancellation. The proposed method utilises a posteriori probabilities from the MIMO detector as well as a priori probabilities from the turbo decoder, and perform soft minimum mean-squared error filtering for symbol level detection. With this approach, soft information is fully fed into the symbol detection process, and thus the reliability of soft symbol is increased. In addition, they can separate out the bit-level soft estimation process by using a simple linear method. Simulation results show that the proposed method provides substantial complexity reduction, with a bit error rate performance comparable to the conventional SIC-MMSE methods.

Low-complexity iterative PIC-MMSE based detection with symbol mapping

The iterative detection and decoding technique based on parallel interference cancellation (PIC) has received considerable attention. To reduce the computational complexity in the estimation of soft bit information, this paper proposes to apply the symbol mapping technique to the PIC-MMSE based MIMO detector. In each layer, the PIC-MMSE filtered symbol is normalized and mapped to a specific range of the first quadrant in a recursive manner. In addition, we present an efficient method to calculate the soft symbols in the PIC process. Simulation results show that the complexity of the proposed method with symbol mapping is reduced to linear-order without performance degradation, and the complexity of the soft symbols computation is greatly reduced as well.

An efficient iterative detection scheme for coded MIMO systems

This paper presents a complexity reduced maximum likelihood (ML) or maximum a posteriori (MAP) based iterative MIMO detection scheme. The a priori loglikelihood ratios (LLRs) provided by the decoder have a Gaussian distribution. By using this statistical characteristics of the LLRs, we calculate the mean and variance of LLRs provided by the decoder. Subsequently, a threshold value is calculated, and on the basis of the threshold value the MIMO detector makes the decision about calculating the extrinsic LLRs or using the same LLRs values provided by the decoder for a particular symbol transmitted from each transmit antenna. Simulation results show that the proposed complexity reduction method produces nearly the same bit error rate (BER) performance as the full search MAP with about a 30% of the complexity reduction. Complexity reduction can be controlled by adjusting the parameters of the proposed method.

Evaluation of reduced search iterative MIMO detection schemes

This paper presents a complexity reduction method for iterative MIMO detection scheme. We first investigate the statistical characteristics of the log-likelihood ratios (LLRs) across the MIMO detector and decoder, and set a threshold value for the reduced search MIMO detector. Based on this threshold value, the decision is made whether re-estimation of MIMO detection using the soft information provided by the decoder is necessary. This is to eliminate unnecessary repetitive calculation for symbols with high reliability. As iteration goes by, complexity is largely reduced. We compare the performance of the proposed scheme by using maximum likelihood (ML) or maximum a posteriori (MAP). Simulation results show that the proposed complexity reduction method reduces the complexity by about 30% with negligible bit error rate (BER) performance degradation.

Low-complexity SIC-MMSE for joint multiple-input multiple-output detection

Iterative detection and decoding based on a soft interference cancellation–minimum mean squared error (SIC-MMSE) scheme provides efficient performance for coded MIMO systems. The critical computational burden for a SIC-MMSE detector in a MIMO system lies in the multiple inverse operations of the complex matrix. In this paper, we present a new method to reduce the complexity of the SIC-MMSE scheme based on a MIMO detection scheme that uses a single universal matrix with a non-layer-dependent inversion process. We apply the Taylor series expansion approach and derive a simple non-layer-dependent inverse matrix. The simulation results reveal that the utilization of the universal matrices presented in this paper produces almost the same performance as the conventional SIC-MMSE scheme but with low computational complexity.

Iterative MMSE-based soft MIMO detection with parallel interference cancellation

Minimum mean square error (MMSE)-based techniques are often used for the joint iterative detection and decoding that is for coded multi-input-multi-output (MIMO) system due to a sound complexity and the performance trade-off. This study proposes an enhanced MMSE-based soft MIMO-detection scheme by using three main ideas. The first idea is an efficient complexity-reduced soft-bit estimation technique, the second one is a performance improvement method utilised inside the MMSE detection process, and the third one is a complexity-reduced soft-symbol estimation method for quadrature amplitude modulation. The proposed ideas enable the interference-cancellation processes to be activated in parallel on each symbol layer, thereby reducing the processing time. The simulation results show that the proposed method efficiently contributes to the improvement of the performance in addition to its reduction of the linear-order complexity.

Optimization of iterative PIC-MMSE based detection with symbol mapping

Recently, the iterative detection and decoding technique based on parallel interference cancellation with minimum mean square error (PIC-MMSE) has received considerable attention. To improve the performance, the detector usually adopts a self-iteration which iteratively estimate the soft bit information (SBI). This paper proposes two main idea to improve the performance as well as to reduce the complexity of the PIC-MMSE based MIMO detector. In order to reduce the complexity, we map PIC-MMSE filtered symbol to a specific region so that the detector does not require any search process to find the minima. In addition, we propose an optimization technique to increase the reliability of the soft symbols. Simulation results show that the complexity of the proposed method is reduced to linear-order without performance degradation, and the proposed optimization method can efficiently improve the performance with reasonable complexity.

Complexity reduced SBI estimation in iterative MIMO systems

In this paper, we demonstrate the performance of low complexity soft interference cancellation minimum mean-squared error (SIC-MMSE) detection method for a turbo coded multiple-input multiple-output (MIMO) system with joint iterative detection and decoding (JIDD) principle. The main computational burden of SIC-MMSE detector lies in the multiple inverse operation of the filtering process and maximum a posteriori (MAP) based soft bit information (SBI) estimation. In order to reduce the complexity we apply hybrid approach for SBI estimation. Based on the reliability of soft information provided by channel decoder, the SBI estimation process is switched between the hard decision threshold based (HDT) method with a single distance calculation and a MAP based estimation. Furthermore, we employ a scaling method for the HDT method which can reduce the over estimated SBI values, resulting in performance improvement. Simulation results show that the hybrid approach for SIC-MMSE method highly reduces computational complexity, without appreciable bit error rate performance degradation.

Energy efficient implementation of soft iterative MIMO detection

The performance of coded MIMO systems can be enhanced by effectively utilizing soft iterative estimation techniques at the receiver. The main concern on this iterative techniques is computational complexity and feasibility of the hardware implementation. This paper evaluates a number of soft iterative MIMO detection schemes in terms of the bit error rate performance as well as computational complexity. We focus on the methods with a fixed and minimum hardware complexity to produce the best performance, for energy efficient implementation. After investigating a number of possible configurations of iterative loops, we suggest the most efficient structure and its algorithm to perform iterative estimations.