Complexity reduction for signal detection based on belief propagation in a massive MIMO system

Fukuda, Wataru; Takashi, Abiko; Nishimura, Toshihiko; Ohgane, Takeo; Ogawa, Yasutaka; Ohwatari, Yusuke; Kishiyama, Yoshihisa

doi:10.1109/ispacs.2013.6704555

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Techniques such as maximum‐likelihood detection and sphere decoder, whose complexities increase exponentially with the number of streams, are prohibitive at large‐scale antenna systems. Hence, signal detection/decoding turns out to be a challenging task in large‐array systems, due to the huge increase on the number of antennas and spatial multiplexing layers by orders of magnitude [47, 48]. In order to reduce computational complexity without losing reliability, some studies have proposed near‐optimal receivers by considering graph‐based solutions using belief propagation (BP) algorithms [47, 48].…”

Section: Signal Processing In Massive Mimomentioning

confidence: 99%

“…Furthermore, such a method is obtained from a graph model which gives the possibility of implementing it in a distributed fashion using message passing. In [48], the authors use other variants of BP, namely: layered BP (LBP) and BP with forced convergence (BP‐FC). In the original BP, the algorithm updates simultaneously the messages after processing all nodes, which is referred to as flooding scheduling.…”

Section: Signal Processing In Massive Mimomentioning

confidence: 99%

“…In the original BP, the algorithm updates simultaneously the messages after processing all nodes, which is referred to as flooding scheduling. The LBP schedules the messages in a sequential fashion instead of simultaneously, and this approach provides enhanced convergence [48]. The BP‐FC is based on the fact that some nodes converge faster than others.…”

Section: Signal Processing In Massive Mimomentioning

confidence: 99%

“…Therefore, it is possible to reduce complexity by skipping the update of such nodes. The results shown in [48] reveal that LBP and BP‐FC can greatly reduce the computational complexity of the detector without severe degradation of their corresponding BER performance. Approximate message passing (AMP) methods have also been used to derive new massive MIMO detectors [49, 50].…”

Section: Signal Processing In Massive Mimomentioning

confidence: 99%

See 3 more Smart Citations

Massive MIMO: survey and future research topics

Araújo¹,

Maksymyuk²,

Almeida³

et al. 2016

IET Communications

172

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Massive multiple-input multiple-output technology has been considered a breakthrough in wireless communication systems. It consists of equipping a base station with a large number of antennas to serve many active users in the same time-frequency block. Among its underlying advantages is the possibility to focus transmitted signal energy into very short-range areas, which will provide huge improvements in terms of system capacity. However, while this new concept renders many interesting benefits, it brings up new challenges that have called the attention of both industry and academia: channel state information acquisition, channel feedback, instantaneous reciprocity, statistical reciprocity, architectures, and hardware impairments, just to mention a few. This paper presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research.

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Section: Signal Processing In Massive Mimomentioning

confidence: 99%

Section: Signal Processing In Massive Mimomentioning

confidence: 99%

Section: Signal Processing In Massive Mimomentioning

confidence: 99%

Section: Signal Processing In Massive Mimomentioning

confidence: 99%

See 2 more Smart Citations

Massive MIMO: survey and future research topics

Araújo¹,

Maksymyuk²,

Almeida³

et al. 2016

IET Communications

172

View full text Add to dashboard Cite

show abstract

Low‐complexity soft‐output MIMO uplink detection for large systems iterative detection and decoding

Pereira

Sampaio‐Neto

2018

Trans Emerging Tel Tech

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Iterative detection and decoding techniques, based on the turbo principle, have been proposed in the literature to achieve near‐capacity on both single‐ and multiple‐antenna fading communication systems. In the multiple‐input–multiple‐output case, when a very large system is considered, one major issue is the overall system complexity. Here, we propose an iterative detection and decoding scheme for the uplink of coded multiple‐input–multiple‐output systems that replaces the exponential complexity maximum a posteriori detector by an approximate maximum a posteriori low‐complexity detector that uses a simple hard‐output inner detector at the first iteration followed by parallel interference cancelation and filtering matched to the channel coefficient matrix that is estimated with the aid of orthogonal pilot transmission. From the second iteration onward, the inner hard‐output detector is replaced by the results from the previous iteration. Simulation results demonstrate that the proposed strategy presents good bit‐error‐rate performance with low computational complexity, measured by the average number of floating‐point operations per message bit.

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