In this paper, we present a novel soft-input soft-output detector for large-scale MIMO systems that offers substantial complexity reduction over the existing detectors and performance close to the full dimensional symbol detector. The proposed algorithm, termed soft-input soft-output successive group (SSG) detector, detects a subset of symbols successively with an aid of the preprocessing designed to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of interfering symbol groups using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results performed on large-scale multi-input multi-output (MIMO) systems demonstrate that the proposed SSG detector achieves significant complexity reduction over the conventional approaches with negligible performance loss.

Recently, a variety of low complexity soft-input soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-input soft-output detector detector at feasible complexity for massive MIMO systems due to the heavy burden in computational complexity. In this paper, we present a novel soft-input soft-output detector for massive MIMO systems that offers substantial complexity reduction over the existing detectors performance close to the full dimensional symbol detector. The proposed soft-input soft-output successive group (SSG) detector detects a subset of symbols successively with an aid of the deliberately designed preprocessing to suppress the inter-group interference. Simulation results performed on 8×8 MIMO flat fading channels demonstrate that the proposed SSG detector achieves significant complexity reduction over the conventional approaches with negligible performance loss.

In this paper, we present a low-complexity list sphere detection algorithm for achieving near-optimal a posteriori probability (APP) detection in an iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of candidates is computationally inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and then derive a hypersphere radius satisfying this condition. Further, in order to exploit the original sphere constraint as it is instead of using necessary conditioned version, we combine a probabilistic tree pruning strategy and the proposed list sphere search. Two features, tightened hypersphere radius and probabilistic tree pruning, collaborate and improve the search efficiency in a complementary fashion. Through simulations on 4x4 MIMO system, we show that the proposed method provides substantial reduction in complexity while achieving negligible performance loss over the conventional list sphere 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.

In this paper, we consider a low-complexity multiuser detection for downlink of high speed packet access (HSPA) system. Instead of attempting to perform the 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. Further, 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 the simulations on realistic downlink communication scenario in HSPA systems, we show that the proposed method offers substantial performance gain over conventional receiver algorithms with implementable complexity.

In this paper, we present a low-complexity list sphere search algorithm for achieving near-optimal a posteriori probability (APP) detection in iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of lattice points is inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and derive the optimal sphere radius satisfying this requirement. Further, in order to exploit the sphere constraint as it is instead of using necessary conditioned versions, we incorporate a probabilistic tree pruning strategy into the list sphere search. Through simulations on realistic IDD systems, we show that the proposed method provides considerable complexity savings while maintaining near-optimal performance.

In this letter, we propose an extension of the probabilistic tree pruning sphere decoding (PTP-SD) algorithm that provides further improvement of the computational complexity with minimal extra cost and negligible performance penalty. In contrast to the PTP-SD that considers the tightening of necessary conditions in the sphere search using per-layer radius adjustment, the proposed method focuses on the sphere radius control strategy when a candidate lattice point is found. For this purpose, the dynamic radius update strategy depending on the lattice point found as well as the lattice independent radius selection scheme are jointly exploited. As a result, while maintaining the effectiveness of the PTP-SD, further reduction of the computational complexity, in particular for high SNR regime, can be achieved. From simulations in multiple-input and multiple-output (MIMO) channels, it is shown that the proposed method provides a considerable improvement in complexity with near-ML performance.

In this paper, we propose a novel radius control strategy for sphere decoding referred to as inter search radius control that provides further improvement of the computational complexity with minimal extra cost and negligible performance penalty. The proposed method focuses on the sphere radius control strategy when a candidate lattice point is found. For this purpose, the dynamic radius update strategy as well as the lattice independent radius selection scheme are jointly exploited. From simulations in multiple-input and multiple-output (MIMO) channels, it is shown that the proposed method provides a substantial improvement in complexity with near-ML performance.

This letter considers a low-complexity multiuser detection based on the closest lattice point search (CLPS) for high speed packet access (HSPA) system. Instead of attempting to solve the ML detection problem in the presence of intersymbol and inter-cell interference, we utilize interference cancelled chips obtained from a bidirectional decision feedback operation to detect symbols. As a result, the worst case complexity of the CLPS is bounded to a controllable level irrespective of multipath spans. From the simulation on single and multi cell downlink communications in HSPA systems, we show that the proposed method offers substantial performance gain over conventional RAKE and MMSE equalizer.

In this letter, we propose a simple yet effective modulation classification method for maximum likelihood multiuser detection. Our method is a modification of generalized likelihood ratio test (GLRT) that approximates the optimal classifier in the Bayesian sense. We show that the proposed method can be implemented by modifying the sphere decoding algorithm to support multimodulation. Simulation results in multiuser detection in high-speed downlink packet access (HSDPA) system show that the proposed method offers considerable performance gain over conventional RAKE and MMSE algorithms.

In this paper, we present a near ML-achieving sphere search technique that reduces the number of search operations significantly over existing sphere decoding (SD) algorithms. While the SD algorithm relies only on causal symbols in evaluating path metric, proposed method accounts for the contribution of non-causal symbols with the aid of per-path minimum mean square error (MMSE) symbol estimation. The ML and MMSE combined cost metric results in the tight necessary condition for sphere decision and hence expedites the pruning of subtrees unlikely to be survived. From the simulations performed over multi-input multi-output (MIMO) wireless channels, it is shown that the computational complexity of the proposed approach is substantially smaller than the existing SD algorithms while providing negligible performance loss.

We propose a novel approach to efficiently estimate multipath channel parameters, which is particularly useful in sparse multipath channels. Conventional methods do not fully exploit the inherent structure present in the combined channel response; the excess number of parameters to be estimated by conventional methods makes the identification difficult. By utilizing a priori knowledge of the transmission data pulse, the channel identification problem is transformed into the mode estimation problem. Then, the parameters directly related to the multipath propagation are extracted in the the modal analysis framework, and hence, the number of estimation parameters are significantly reduced. Finally, the multipath channel parameters are obtained by inverse-transforming the mode parameters. Simulation results show significant improvement in the normalized mean square error over existing approaches.