Reduced-complexity mimo detector with close-to ml error rate performance

Hess, Cyrill; Wenk, M.; Burg, Andreas; Luethi, P.; Studer, Christoph; Felber, N.; Fichtner, W.

doi:10.1145/1228784.1228836

Cited by 38 publications

(33 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to deal with these two aspects, the authors in [13] have proposed a sub-optimal solution denoted as the K-best [13,14], where K is the number of stored neighbors given a layer. However, even with a fixed computational complexity and a parallel nature of implementation, some optimizations are required especially for high-order constellation and large number of antennas (due to the large K required in this case) [15][16][17][18]. Aiming at reducing the neighborhood size (namely K, over all layers), different solutions are proposed.…”

Section: Introductionmentioning

confidence: 99%

A simplified hard output sphere decoder for large MIMO systems with the use of efficient search center and reduced domain neighborhood study

Nasser

Aubert

Nouvel

et al. 2015

J Wireless Com Network

View full text Add to dashboard Cite

Multiple-input multiple-output (MIMO) with a spatial-multiplexing (SM) scheme is a topic of high interest for the next generation of wireless communications systems. At the receiver, neighborhood studies (NS) and lattice reduction (LR)-aided techniques are common solutions in the literature to approach the optimal and computationally complex maximum likelihood (ML) detection. However, the NS and LR solutions might not offer optimal performance for large dimensional systems, such as large number of antennas, and high-order constellations when they are considered separately. In this paper, we propose a novel equivalent metric dealing with the association of these solutions by introducing a reduced domain neighborhood study. We show that the proposed metric presents a relevant complexity reduction while maintaining near-ML performance. Moreover, the corresponding computational complexity is shown to be independent of the constellation size, but it is quadratic in the number of transmit antennas. For instance, for a 4 × 4 MIMO system with 16-QAM modulation on each layer, the proposed solution is simultaneously near-ML with perfect and real channel estimation and ten times less complex than the classical neighborhood-based K-best solution.

show abstract

Section: Introductionmentioning

confidence: 99%

A simplified hard output sphere decoder for large MIMO systems with the use of efficient search center and reduced domain neighborhood study

Nasser

Aubert

Nouvel

et al. 2015

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…Subset Enumeration More elaborate solutions for SE enumeration were presented in [10,12,22]. The main idea of these approaches is to divide the complexvalued (i.e., two-dimensional) constellation into one-dimensional subsets, which only require to compute and store one PED per subset.…”

Section: Schnorr-euchner Enumerationmentioning

confidence: 99%

“…The second method shown in Fig. 6(b) was proposed in [22] and employs pulse-amplitude modulation (PAM) subsets (i.e., stripes). Both methods suffer from the fact that the number of required subsets becomes large when targeting higher modulation orders (e.g., 64-QAM requires eight PAM subsets), which contributes considerably to the resulting circuit area and timing of the entire architecture (as sorting across all subsets is required).…”

Section: Schnorr-euchner Enumerationmentioning

confidence: 99%

VLSI Implementation of Hard- and Soft-Output Sphere Decoding for Wide-Band MIMO Systems

Studer

Wenk

Burg

2012

VLSI-SoC: Forward-Looking Trends in IC and Systems Design

View full text Add to dashboard Cite

Abstract. Multiple-input multiple-output (MIMO) technology in combination with orthogonal frequency-division multiplexing (OFDM) is the key to meet the demands for data rate and link reliability of modern wideband wireless communication systems, such as IEEE 802.11n or 3GPP-LTE. The full potential of such systems can, however, only be achieved by high-performance data-detection algorithms, which typically exhibit prohibitive computational complexity. Hard-output sphere decoding (SD) and soft-output single tree-search (STS) SD are promising means for realizing high-performance MIMO detection and have been demonstrated to enable efficient implementations in practical systems. In this chapter, we consider the design and optimization of register transfer-level implementations of hard-output SD and soft-output STS-SD with minimum area-delay product, which are well-suited for wide-band MIMO systems. We explain in detail the design, implementation, and optimization of VLSI architectures and present corresponding implementation results for 130 nm CMOS technology. The reported implementations significantly outperform the area-delay product of previously reported hard-output SD and soft-output STS-SD implementations.

show abstract

“…Many SD implementations involve parallelism, but without meeting the characteristics discussed above. For example, both depth-first [2], [7] and breadth-first [8] SDs perform several Euclidean distance calculations in parallel, at each level of the SD tree, exploiting a limited level of data parallelism. However, after performing a set of parallel computations, before the next set is processed in parallel, the sorting operations required introduce significant dependencies.…”

Section: Introductionmentioning

confidence: 99%

“…• A novel tree traversal and enumeration strategy that minimizes unnecessary Euclidean distance calculations, and in contrast to existing approaches ( [3], [7], [15]) that apply only to sequential SDs, it can also apply to both sequential and parallel ones. MultiSphere applies to any kind of SD.…”

Section: Introductionmentioning

confidence: 99%

MultiSphere: Massively Parallel Tree Search for Large Sphere Decoders

Nikitopoulos

Daniil

Jayawardena

et al. 2016

2016 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

Abstract-This work introduces MultiSphere, a method to massively parallelize the tree search of large sphere decoders in a nearly-independent manner, without compromising their maximum-likelihood performance, and by keeping the overall processing complexity at the levels of highly-optimized sequential sphere decoders. MultiSphere employs a novel sphere decoder tree partitioning which can adjust to the transmission channel with a small latency overhead. It also utilizes a new method to distribute nodes to parallel sphere decoders and a new tree traversal and enumeration strategy which minimize redundant computations despite the nearly-independent parallel processing of the subtrees. For an 8 × 8 MIMO spatially multiplexed system with 16-QAM modulation and 32 processing elements MultiSphere can achieve a latency reduction of more than an order of magnitude, approaching the processing latency of linear detection methods, while its overall complexity can be even smaller than the complexity of well-known sequential sphere decoders. For 8×8 MIMO systems, MultiSphere's sphere decoder tree partitioning method can achieve the processing latency of other partitioning schemes by using half of the processing elements. In addition, it is shown that for a multi-carrier system with 64 subcarriers, when performing sequential detection across subcarriers and using MultiSphere with 8 processing elements to parallelize detection, a smaller processing latency is achieved than when parallelizing the detection process by using a single processing element per subcarrier (64 in total).

show abstract

Reduced-complexity mimo detector with close-to ml error rate performance

Cited by 38 publications

References 9 publications

A simplified hard output sphere decoder for large MIMO systems with the use of efficient search center and reduced domain neighborhood study

A simplified hard output sphere decoder for large MIMO systems with the use of efficient search center and reduced domain neighborhood study

VLSI Implementation of Hard- and Soft-Output Sphere Decoding for Wide-Band MIMO Systems

MultiSphere: Massively Parallel Tree Search for Large Sphere Decoders

Contact Info

Product

Resources

About