On Adaptive Lattice Reduction over Correlated Fading Channels (original) (raw)
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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 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.
Lattice-Reduction-Aided Conditional Detection for MIMO Systems
IEEE Transactions on Communications, 2014
We introduce a low-complexity detector with nearoptimal performance for transmission over multi-antenna systems. By using lattice basis reduction for generating almost orthogonal channel submatrices, we enhance the conditional optimization technique to implement a fast yet efficient detector. The lattice-reduction-aided (LRA) conditional method is presented as a general detection technique over fading channels to yield significant saving in computational complexity while achieving close to Maximum Likelihood (ML) error performance. By employing the orthogonality defect factor as a universal measure to select a near-orthogonal channel submatrix for conditional detection, we implement efficient detectors for MIMO systems. In particular, an almost optimal decoder with linear complexity for the Golden code is presented over quasi-static channels.
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.
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.
Interference cancellation assisted lattice-reduction aided detection for MIMO systems
2006
In this paper, we proposed and investigated the optimal successive interference cancellation (SIC) strategy designed for latticereduction aided multiple-input multiple-output (MIMO) detectors. For the sake of generating the optimal MIMO symbol estimate at each SIC detection stage, we model the so-called effective symbols generated with the aid of lattice-reduction as joint Gaussian distributed random variables. However, after lattice-reduction, the effective symbols become correlated and exhibit a non-zero mean. Hence, we derive the optimal minimum-mean-squared-error (MMSE) SIC detector, which updates the mean and variance of the effective symbols at each SIC detection stage. As a result, the proposed detector achieves an approximately 3 dB E b /N 0 gain and performs close to the maximum likelihood detector.
Dual-lattice-aided MIMO detection for slow fading channels
2011
Abstract This paper presents a lattice detection strategy for spatial multiplexing (SM) which takes advantage of a pre-processing stage based on the geometric relations between the points in the primal lattice and the ones in the dual lattice. The first part of the paper clarifies this geometric relationship that will be exploited later on in the design of a pre-processing stage for the proposed receiver. This pre-processing finds a set of successive minima in the dual lattice, and is only required at each channel update.
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.
A Comparison of Two Lattice-Reduction-Based Receivers for MIMO Systems
2008 IEEE Sarnoff Symposium, 2008
In this paper, we compare a new practical lattice reduction method, Seysen's algorithm, with the existing LLL lattice reduction approach. Seysen's algorithm considers all vectors in the lattice simultaneously and performs global search for lattice reduction, while the LLL algorithm concentrates on local optimization to produce a reduced lattice We also study their performance, when combined with linear detectors (Zero Forcing, MMSE and extended MMSE), and successive interference cancellation (SIC) detector. For MIMO digital communications, Seysen-based linear detectors achieve the same diversity order as the optimum ML detector. It outperforms the existing LLL-based linear detectors. Moreover, Seysen requires less computational time than the LLL scheme. However, this gap disappears in SIC scenario: Seysen-based SIC detector functions the same as LLL-based one, due to the efficiency of SIC itself.