LR-aided Detectors Research Articles

Reduced complexity lattice‐based multiple‐input multiple‐output schemes

Multiple-input multiple-output is being considered as a highly interesting topic for the new generation of wireless communication systems. The major challenge is how to enhance the link reliability while neither affecting the spectral efficiency nor the quality of service. To reduce the complexity of the maximum likelihood (ML) receiver, efforts have been focused on zero forcing (ZF) decoder using lattice reduction-aided (LRA) techniques. In this study, the authors propose a method that improves performance using the LRA-based precoding technique without increasing the decoder complexity. Their approach is to exploit the channel matrix to generate matrices for both the precoding and LRA detection technique. This makes it possible to achieve maximum orthogonality for the generated channel matrices while reducing considerably the noise effect. They focus on the use of the LRA-ZF for the ease of its implementation and low complexity. The proposed method outperforms LRA-minimum mean square error-based receiver and gets closer to ML receiver performance. The performance and complexity of the proposed scheme are discussed.

Lattice-Reduction-Aided Breadth-First Tree Searching Algorithm for MIMO Detection

We propose a lattice-reduction (LR)-aided breadth-first tree searching algorithm for MIMO detection achieving near-optimal performance with very low complexity. At each level of the tree in the search, only the paths whose accumulated metrics satisfy a particular restriction condition will be kept as the candidates. Furthermore, the number of child nodes expanded on each parent node, and the maximum number of candidates preserved at each level, are also restricted, respectively. All these measures ensure the proposed algorithm reaching a preset near-optimal performance and achieving very low average and maximum computational complexity. Simulation results verify the proposed algorithm’s higher efficiency in terms of the performance/complexity tradeoff than the existing LR-aided K-best detectors and LR-aided fixed-complexity sphere decoders.

Low-complexity lattice reduction algorithm for MIMO detectors with tree searching

In this paper, we propose a low-complexity lattice reduction (LR) algorithm for multiple-input multiple-output (MIMO) detectors with tree searching. Whereas conventional approaches are based exclusively on channel characteristics, we focus on joint optimisation by employing an early termination criterion in the context of MIMO detection. In this regard, incremental LR (ILR) was previously proposed. However, the ILR is limited to LR-aided successive interference cancellation (SIC) detectors which have considerable bit-error-rate (BER) performance degradation compared to optimal detectors. Hence, in this paper, we extend the conventional ILR to be applicable to the LR-aided detectors with near-optimal performance. Furthermore, we perform the hypothetical analysis and several novel modifications to handle the obstacles for the application of the ILR to LR-aided detectors other than the LR-aided SIC detectors. The simulation results demonstrate that the computational complexity is considerably reduced, with BER performance degradation of 10−5.

An improved scheme based on log-likelihood-ratio for lattice reduction-aided MIMO detection

Lattice reduction (LR)-aided detectors have been shown great potentials in wireless communications for their low complexity and low bit-error-rate (BER) performance. The LR algorithms use the unimodular transformation to improve the orthogonality of the channel matrix. However, the LR algorithms only utilize the channel state information (CSI) and do not take account for the received signal, which is also important information in enhancing the performance of the detectors. In this paper, we make a readjustment of the received signal in the LR domain and propose a new scheme which is based on the log-likelihood-ratio (LLR) criterion to improve the LR-aided detectors. The motivation of using the LLR criterion is that it utilizes both the received signal and the CSI, so that it can provide exact pairwise error probabilities (PEPs) of the symbols. Then, in the proposed scheme, we design the LLR-based transformation algorithm (TA) which uses the unimodular transformation to minimize the PEPs of the symbols by the LLR criterion. Note that the PEPs of the symbols affect the error propagation in the vertical Bell Laboratories Layered Space-Time (VBLAST) detector, and decreasing the PEPs can reduce the error propagation in the VBLAST detectors; thus, our LLR-based TA-aided VBLAST detectors will exhibit better BER performance than the previous LR-aided VBLAST detectors. Both the BER performance and the computational complexity are demonstrated through the simulation results.

Lattice Reduction-Aided Detection for Overloaded MIMO Using Slab Decoding

Lattice Reduction-Aided Detection for Overloaded MIMO Using Slab Decoding

Lattice reduction aided tabu search with channel‐dependent stopping criterion for MIMO systems

An initial solution based on lattice reduction (LR) and a novel stopping criterion for the tabu search (TS) detection algorithm are proposed for use in multiple input multiple output (MIMO) antenna systems. LR aided-detection is performed and its result is utilised as an initial solution to the TS algorithm, while a measure of the condition of the lattice-reduced channel is used as a criterion for early termination of the algorithm. On the basis of the simulation results, this approach outperforms other initial solutions by about 3.5 dB at a BER of 10−3 for the 16-QAM 16 × 16 MIMO with only a slight increment in computational cost. The proposed stopping criterion reduces the complexity further with virtually no loss in BER performance.

Approximate maximum a posteriori detection for multiple‐input–multiple‐output systems with bit‐level lattice reduction‐aided detectors and successive interference cancellation

For iterative detection and decoding (IDD) in multiple-input–multiple-output (MIMO) systems, although the maximum a posteriori (MAP) detector can achieve an optimal performance, because of its prohibitively high computational complexity, various low-complexity approximate MAP detectors are studied. Among the existing MIMO detectors for the non-IDD receivers, lattice reduction (LR)-aided detectors can provide a near maximum-likelihood (ML) detector's performance with reasonably low complexity, and they could be modified to be used in the IDD receivers. In this study, the authors propose a bit-level LR-aided MIMO detector whose performance can approach that of the MAP detector, where a priori information is taken into account for soft-decisions. Furthermore, the proposed method can be extended to large dimensional MIMO systems by channel matrix decomposition and successive interference cancellation, by which a significant complexity reduction can be achieved. Through simulations and complexity analysis, it is shown that a near-optimal performance is obtained by the authors proposed low-complexity bit-level LR-aided detector for the IDD in the MIMO systems.

LR-Aided MIMO Detectors under Correlated and Imperfectly Estimated Channels

In this contribution, lattice reduction (LR) technique is applied to improve the multiple-input multiple-output (MIMO) detector performance under correlated channels and imperfect channel estimation constrains. Zero-forcing, minimum mean squared error, ordered successive interference cancellation and sphere decoding (SD) detectors are analysed taking into consideration (a) different correlated fading channel indexes, (b) increasing spectral efficiency, by combining number of transmit antennas and modulation formats, and (c) channel coefficient error estimations. Analysis of correlated channel effects over the MIMO system performance equipped with different LR-aided detectors are carried out, indicating the robustness of those detectors and the SD–MIMO detector deficiency to deal with such channel condition. Besides, computational complexities are compared aiming to determine the best LR–MIMO detection scheme under the perspective of performance-complexity tradeoff.

Computationally Efficient Lattice Reduction Aided Detection for MIMO-OFDM Systems under Correlated Fading Channels

We analyze the relationship between channel coherence bandwidth and two complexity-reduced lattice reduction aided detection (LRAD) algorithms for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems in correlated fading channels. In both the adaptive LR algorithm and the fixed interval LR algorithm, we exploit the inherent feature of unimodular transformation matrix P that remains the same for the adjacent highly correlated subcarriers. Complexity simulations demonstrate that the adaptive LR algorithm could eliminate up to approximately 90 percent of the multiplications and 95 percent of the divisions of the brute-force LR algorithm with large coherence bandwidth. The results also show that the adaptive algorithm with both optimum and globally suboptimum initial interval settings could significantly reduce the LR complexity, compared with the brute-force LR and fixed interval LR algorithms, while maintaining the system performance.

Implementation of parallel lattice reduction-aided MIMO detector using graphics processing unit

Since H. Yao proposed the lattice reduction (LR)-aided detection algorithm for the MIMO detector, one can exploit the diversity gain provided by the LR method to achieve performance comparable to the maximum likelihood (ML) algorithm but with complexity close to the simple linear detection algorithms such as zero forcing (ZF), minimum mean squared error, and successive interference cancellation, etc. In this paper, in order to reduce the processing time of the LR-aided detector, a graphics processing unit (GPU) has been proposed as the main modem processor in such a way that the detections can be performed in parallel using multiple threads in the GPU. A 2X2 multiple input multiple output (MIMO) WiMAX system has been implemented using a GPU to verify that various MIMO detection algorithms such as ZF, ML, and LR-aided methods can be processed in real-time. From the experimental results, we show that GPUs can realize a 2X2 WiMAX MIMO system adopting an LR-aided detector in real-time. We achieve a processing time of 2.75 ms which meets the downlink duration specification of 3 ms. BER performance of experimental tests also indicates that the LR-aided MIMO detector can fully exploit diversity gain as well as ML detector.

Systolic arrays for lattice-reduction-aided mimo detection

Multiple-input multiple-output (MIMO) technology provides high data rate and enhanced quality of service for wireless communications. Since the benefits from MIMO result in a heavy computational load in detectors, the design of low-complexity sub- optimum receivers is currently an active area of research. Lattice- reduction-aided detection (LRAD) has been shown to be an effective low-complexity method with near-maximum-likelihood performance. In this paper, we advocate the use of systolic array architectures for MIMO receivers, and in particular we exhibit one of them based on LRAD. The Lenstra-Lenstra-Lovasz (LLL) lattice reduction and the ensuing linear detections or successive spatial-interference cancellations can be located in the same array, which is considerably hardware-efficient. Since the conventional form of the LLL algorithm is not immediately suitable for parallel processing, two modified LLL algorithms are considered here for the systolic array. LLL algorithm with full-size reduction- LLL is one of the versions more suitable for parallel processing. Another variant is the all-swap lattice-reduction (ASLR) algorithm for complex-valued lattices, which processes all lattice basis vectors simultaneously within one iteration. Our novel systolic array can operate both algorithms with different external logic controls. In order to simplify the systolic array design, we replace the Lovasz condition in the definition of LLL-reduced lattice with the looser Siegel condition. Simulation results show that for LR-aided linear detections, the bit-error-rate performance is still maintained with this relaxation. Comparisons between the two algorithms in terms of bit-error-rate performance, and average field-programmable gate array processing time in the systolic array are made, which shows that ASLR is a better choice for a systolic architecture, especially for systems with a large number of antennas.

Low complexity lattice reduction scheme for STBC two-user uplink MIMO systems

Abstract Recently, a lattice reduction-aided (LRA) multiple-input multiple-output detection scheme has been proposed in junction with linear (as well as nonlinear) detectors. It is well known that these schemes provide a full diversity, and its complexity is comparable to that of linear detectors for the block fading channels. For the fast varying channels, however, the decoding complexity of LRA detection scheme is unreasonably high. This article proposes an efficient iterative lattice reduction (LR) scheme for an uplink system with two receive antennas at the base station and two users, each employing the Alamouti space-time block code (STBC). By taking advantage of the certain inherent STBC structure of transmitted symbols from users, the proposed scheme provides the same performance as a conventional LR while saving about 80% computational complexity. We also show that it can be successfully extended to handle the scenario where another interfering user, who is also employing the Alamouti STBC, is present.

A New Lattice Reduction Algorithm for LR-Aided MIMO Linear Detection

Lattice reduction (LR) has recently emerged as a promising technique for improving the performance of suboptimal multiple-input-multiple-output (MIMO) detectors. For LR-aided MIMO detection, the Lenstra-Lenstra-Lovasz (LLL) and Seysen's algorithm (SA) have been considered almost exclusively to date. In this paper, we introduced a new LR algorithm for LR-aided linear detection (LD). In contrast to the LLL and SA, which are targeted to search for bases with relatively short basis vectors, the proposed algorithm has been designed to improve the minimum Euclidean distance of the LR-aided linear detector, thus exhibiting improved error rate at high SNR. The error-rate performance of the proposed algorithm as well as the required complexity has been demonstrated through extensive computer simulations.

Pre-Processed Recursive Lattice Reduction for Complexity Reduction in Spatially and Temporally Correlated MIMO Channels

In this letter, a pre-processed lattice reduction (PLR) scheme is developed for the lattice reduction aided (LRA) detection of multiple input multiple-output (MIMO) systems in spatially correlated channel. The PLR computes the LLL-reduced matrix of the equivalent matrix, which is the product of the present channel matrix and unimodular transformation matrix for LR of spatial correlation matrix, rather than the present channel matrix itself. In conjunction with PLR followed by recursive lattice reduction (RLR) scheme [7], pre-processed RLR (PRLR) is shown to efficiently carry out the LR of the channel matrix, especially for the burst packet message in spatially and temporally correlated channel while matching the performance of conventional LRA detection.