Lattice-Reduction-Aided Conditional Detection for MIMO Systems (original) (raw)
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A Diagonal Lattice Reduction Algorithm for MIMO Detection
IEEE Signal Processing Letters, 2000
Recently, an efficient lattice reduction method, called the effective LLL (ELLL) algorithm, was presented for the detection of multiinput multioutput (MIMO) systems. In this letter, a novel lattice reduction criterion, called diagonal reduction, is proposed. The diagonal reduction is weaker than the ELLL reduction, however, like the ELLL reduction, it has identical performance with the LLL reduction when applied for the sphere decoding and successive interference cancelation (SIC) decoding. It improves the efficiency of the ELLL algorithm by significantly reducing the size-reduction operations. Furthermore, we present a greedy column traverse strategy, which reduces the column swap operations in addition to the size-reduction operations.
A Hybrid Method for Lattice-Reduction-Aided MIMO Detection
Proceedings of the 2nd World Congress on Electrical Engineering and Computer Systems and Science, 2016
Lattice reduction has been successfully applied to data detection in multiple-input multiple-output (MIMO) systems. In this paper, we introduce a polynomial time algorithm for lattice-reduction-aided (LR-aided) MIMO detection. The hybrid method we present integrates the length-based size reduction technique into an angle-measured method. To assess the performance of the algorithm, we compare it with the LLL algorithm, a widely used algorithm in MIMO and wireless communications. Our experimental results show that despite that the two algorithms have the same complexity, the hybrid method is empirically more efficient than the LLL algorithm, and the communication channels improved by our hybrid method have smaller bit error rate (BER) than those improved by the LLL algorithm in data detection.
On Decreasing the Complexity of Lattice-Reduction-Aided K-Best MIMO Detectors
2009
The application of Lattice Reduction techniques over the MIMO channel matrix is known to improve the performance of MIMO detectors. Several authors have proposed Lattice-Reduction-Aided K-Best detectors for improving the performance of conventional K-Best algorithms. In this paper, efficient ways of decreasing the computational complexity of previously proposed schemes are presented. The knowledge about how the Lattice Reduction stage affects the transmitted symbols is exploited in order to significantly decrease the complexity without performance loss.
2010
Lattice reduction-aided linear detectors for MIMO systems are a promising receiver structure for low-complexity implementations. In this paper we present a lattice reductionaided MIMO data detection architecture based on Seysen's algorithm, where the algorithm is carried out exclusively on the Gram matrix of the channel and its inverse. Furthermore, we describe and evaluate several modification of Seysen's algorithm tailored to reduce the total complexity with respect to hardware implementation. By means of complexity-performance trade-offs we demonstrate the potential benefit of the various algorithmic modifications. First, novel schemes to identify the update steps in each iteration of Seysen's algorithm are presented. Second, we show that further complexity reduction of Seysen's algorithm can be obtained by severely constraining the update coefficients. Eventually, methods are devised that terminate Seysen's algorithm prematurely and thus result in a reduction of the average complexity.
On the robustness of lattice-reduction aided detectors in correlated MIMO systems
IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004, 2000
Recently the use of lattice-reduction for signal detection in multiple antenna systems has been proposed. In combination with simple successive interference cancellation this scheme achieves near maximum-likelihood performance. To this end, the given MIMO channel is transformed into an almost orthogonal matrix leading to less noise enhancement within the detection. In this paper, we investigate the performance of common and lattice-reduction-aided detection schemes for correlated fading channels. We show, that the new scheme achieves significant gain in comparison to common algorithms. Thus, the new algorithm clearly outperforms existing methods with comparable complexity and is also more robust with respect to spatial correlation.
Are all basis updates for lattice-reduction-aided MIMO detection necessary?
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
The question in the title is relevant when considering latticereduction-aided MIMO detectors, which achieve the same diversity as the maximum-likelihood detector while exhibiting lower complexity. In this paper we examine if all basis updates, which account for the largest complexity contribution in the Lenstra, Lenstra, Lovász lattice reduction algorithm, are necessary for lattice reduction in the context of MIMO detection. We first provide an abstract answer to this question in the form of an idealized experiment that demonstrates the potential for a large reduction in the number of basis updates even when spatial correlation is present. Encouraged by these results, we seek a practical answer to this question by formulating a joint lattice reduction and symbol detection algorithm based on successive interference cancellation. Experimental results of the proposed method demonstrate that on average only 10% to 25% of basis updates are necessary on average depending on the degree of spatial correlation. Therefore, the answer to the question in the title is an encouraging no.
An enhanced Jacobi method for lattice-reduction-aided MIMO detection
2013 IEEE China Summit and International Conference on Signal and Information Processing, 2013
Lattice reduction aided decoding has been successfully used for signal detection in multiinput and multioutput (MIMO) systems and many other wireless communication applications. In this paper, we propose a novel enhanced Jacobi (short as EJacobi) method for lattice basis reduction. To assess the performance of the new EJacobi method, we compared it with the LLL algorithm, a widely used algorithm in wireless communications. Our experimental results show that the EJacobi method is more efficient and produces better results measured by both orthogonality defect and condition number than the LLL algorithm.
A low complexity ZF-based lattice reduction detection using curtailment parameter in MIMO systems
2016
As the large-scale multiple-input multiple-output (MIMO) systems have been advanced in wireless communication, the low complexity and high performance receiver technique have been required. Recently, lattice reduction technique have attracted attention in MIMO detection. Especially, lattice reduction aided linear detection like zero-forcing (ZF) can largely improve the performance with low complexity. Among all the lattice reduction algorithms, the Lenstra-Lenstra-Lovasz (LLL) algorithm is well-known and widely used algorithm. LLL algorithm reduces the basis of channel matrix by using two conditions and it adopts a parameter in order to get nearly orthogonal basis. Although the parameter is defined range, a constant value is commonly used. However it can't be said that the constant value is the best value for all MIMO system. In this paper, we propose an appropriate parameter to reduce computational complexity in LLL algorithm for ZF detection and we also propose an architecture to adapt our proposed parameter in MIMO detection. The proposed parameter is showed simply and corresponds to the number of antennas. Simulation results show that the proposed parameter provides LLL lattice reduction aided ZF detection achieving the same performance as the widely used constant value with lower complexity and also correspond to every MIMO system including large-scale MIMO system.
Near-maximum-likelihood detection of MIMO systems using MMSE-based lattice reduction
2004
In recent publications the use of lattice-reduction for signal detection in multiple antenna systems has been proposed. In this paper, we adopt these lattice-reduction-aided schemes to the MMSE criterion. We show that an obvious way to do this is infeasible and propose an alternative method based on an extended system model, which in conjunction with simple successive interference cancellation nearly reaches the performance of maximum-likelihood detection. Furthermore, we demonstrate that a sorted QR decomposition can significantly reduce the computational effort associated with lattice-reduction. Thus, the new algorithm clearly outperforms existing methods with comparable complexity.
Channel-adaptive complex K-best MIMO detection using lattice reduction
2014 IEEE Workshop on Signal Processing Systems (SiPS), 2014
Lattice reduction (LR) aided detectors mitigate the exponentially increasing complexity of large multiple-input, multiple-output (MIMO) systems while achieving near-optimal performance with low computational complexity. In this paper, a channel-adaptive complex-domain LR-aided K-best MIMO detector is presented that reduces the gap between the K-best sphere decoding (SD) detector and the maximum likelihood (ML) optimal MIMO detector. While maintaining BER performance, computational complexity is reduced by 50% over a conventional complex domain K-best SD detector by implementing a new ondemand complex-domain candidate symbol selection algorithm. Two tunable variables in the candidate selection process are introduced to enable both coarse-grained and fine-grained adaptation of computational complexity to channel conditions.