A low-complexity iterative MIMO detection and decoding scheme using dimension reduction
01 Jan 2016-Vol. 27, Iss: 1, pp 136-145
TL;DR: This paper proposes an IDD scheme employing dimension reduction soft demodulation (DRSD) principle based on maximum a posteriori criterion necessary for IDD operation, which can provide better trade‐off between complexity and performance.
Abstract: Spatial multiplexing multiple-input multiple-output is considered as a core advanced communication technology to increase data rate. Although it guarantees higher capacity, it gives huge burden on the receiver side especially when iterative detection and decoding IDD is employed. Therefore, the IDD receiver needs to be efficiently implemented considering the complexity as well as performance in practice. In this paper, we propose an IDD scheme employing dimension reduction soft demodulation DRSD principle based on maximum a posteriori criterion necessary for IDD operation. With separation into hard and soft streams in the DRSD, the proposed scheme employs all ordering successive interference cancellation with a simple modified slicing for hard detection to consider the effect of a priori information conveyed from past iteration, enabling complexity reduction. It can provide better trade-off between complexity and performance. Copyright © 2014 John Wiley & Sons, Ltd.
Citations
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01 Jan 2017
TL;DR: A new multiple‐input multiple‐output transmission technique called quadrature spatial modulation (QSM) is proposed and analyzed in the presence of imperfect channel estimation at the receiver and significant performance enhancements can be achieved as compared with SM, Alamouti, and spatial multiplexing systems.
Abstract: In this paper, a new multiple-input multiple-output transmission technique called quadrature spatial modulation (QSM) is proposed and analyzed in the presence of imperfect channel estimation at the receiver. In QSM, conventional spatial constellation diagram of spatial modulation (SM) system is expanded to include both in-phase and quadrature components. As such, significant enhancement in the overall spectral efficiency is achieved while retaining all inherent advantages of SM technique, such as inter-channel interference avoidance, single radio frequency chain transmitter and low receiver complexity. It is shown that significant performance enhancements can be achieved as compared with SM, Alamouti, and spatial multiplexing systems. Besides, the impact of Gaussian imperfect channel estimation on the performance of QSM system is studied. A closed-form expression for the pairwise error probability of generic QSM system is derived and used to calculate a tight upper bound of the average bit error probability over Rayleigh fading channels with perfect and imperfect channel knowledge. Also, simple asymptotic expression is derived and analyzed. Obtained Monte Carlo simulation results highlight the accuracy of the conducted analysis. Copyright © 2014 John Wiley & Sons, Ltd.
41 citations
01 Jul 2014
TL;DR: SM MIMO 기법인 maximum likelihood (ML) 관심이 증대되고 있다, 구현 가능한 대한 필요성을 크리, 표준들
Abstract: 전송 데이터 용량의 요구치가 급속히 증가하면서 공간 스트림마다 독립된 정보를 전송할 수 있는 spatial multiplexing (SM) 기반 multi-input multi-output (MIMO) 기술에 대한 관심이 증대되고 있다. 3GPP LTE-advanced, IEEE 802.11ac 등의 최근 표준들에서는 최대 8개까지의 공간 스트림을 지원하고 있으며, beyond 4G 시스템의 핵심 기술로 고려되고 있는 massive MIMO나 mm-wave 시스템에서는 수십~수백개 이상의 안테나까지도 지원을 고려하고 있다. SM MIMO 시스템의 최적 복조 기법인 maximum likelihood (ML) 방식의 연산복잡도는 안테나수에 지수적으로 증가하므로, 안테나 수의 급속한 증가는 연산량의 급격한 증가를 유발하게 되어 낮은 복잡도로 구현 가능한 수신 기법들에 대한 필요성을 증대시키게 되었다. 본 논문에서는 이러한 SM MIMO 복조 기법들에 대한 연구 결과들을 설명한다. 또한, 기존의 복조 기법들과 달리, 지수적으로 복잡도의 증가가 필요하지 않는 간단한 선형 기법에 기반한 massive MIMO 시스템용 수신 기법에 대해서도 설명하고 향후의 시스템 디자인 시 고려할 사항들에 대해 간략히 정리한다.
6 citations
01 Feb 2018
TL;DR: Simulation results demonstrate that the proposed iterative detection and decoding scheme for the uplink of coded multiple‐input–multiple‐output systems presents good bit‐error‐rate performance with low computational complexity, measured by the average number of floating‐point operations per message bit.
4 citations
Additional excerpts
...evaluate the a posteriori LLR of a code bit “+1” and “−1” for every code bit of every transmitted symbol of each user, Λ1 i , i = 1, 2, · · · ,Nt, as Λ1 i (m, t) ≜ log P[bi(m, t) = +1|yt] P[bi(m, t) = −1|yt] , (19)...
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TL;DR: In this article, the authors reviewed the results on the detection schemes for SM MIMO systems and proposed a simple linear filtering scheme for low-complexity low-rate detection.
Abstract: With the increasing demands on high data rate, there has been growing interests in multi-input multi-output (MIMO) technology based on spatial multiplexing (SM) since it can transmit independent information in each spatial stream. Recent standards such as 3GPP LTE-advanced and IEEE 802.11ac support up to eight spatial streams, and massive MIMO and mm-wave systems that are expected to be included in beyond 4G systems are considering employment of tens to hundreds of antennas. Since the complexity of the optimum maximum likelihood based detection method increases exponentially with the number of antennas, low-complexity SM MIMO detection becomes more critical as the number of antenna increases. In this paper, we first review the results on the detection schemes for SM MIMO systems. In addition, massive MIMO reception schemes based on simple linear filtering which does not require exponential increment of complexity will be explained, followed by brief description on receiver design for future high dimensional SM MIMO systems. 논문 14-39A-07-08 The Journal of Korea Information and Communications Society '14-07 Vol.39A No.07 http://dx.doi.org/10.7840/kics.2014.39A.7.413 413 ※ 본 논문은 미래창조과학부에서 지원하는 대구경북과학기술원 RD Revised July 9, 2014; Accepted July 9, 2014 The Journal of Korea Information and Communications Society '14-07 Vol.39A No.07
2 citations
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TL;DR: Using log-likelihood algebra, it is shown that any decoder can be used which accepts soft inputs-including a priori values-and delivers soft outputs that can be split into three terms: the soft channel and aPriori inputs, and the extrinsic value.
Abstract: Iterative decoding of two-dimensional systematic convolutional codes has been termed "turbo" (de)coding. Using log-likelihood algebra, we show that any decoder can be used which accepts soft inputs-including a priori values-and delivers soft outputs that can be split into three terms: the soft channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes but also for any linear binary systematic block code. The iteration is controlled by a stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates and convolutional codes for lower rates less than 2/3. Any combination of block and convolutional component codes is possible. Several interleaving techniques are described. At a bit error rate (BER) of 10/sup -4/ the performance is slightly above or around the bounds given by the cutoff rate for reasonably simple block/convolutional component codes, interleaver sizes less than 1000 and for three to six iterations.
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"A low-complexity iterative MIMO det..." refers background in this paper
...where H D QR, Q y D QHy, Q and R are an MR MR unitary matrix and an MR NS upper triangular matrix, respectively,‡ and PŒs is a priori information term represented as [26]...
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TL;DR: This work provides a simple method to iteratively detect and decode any linear space-time mapping combined with any channel code that can be decoded using so-called "soft" inputs and outputs and shows that excellent performance at very high data rates can be attained with either.
Abstract: Recent advancements in iterative processing of channel codes and the development of turbo codes have allowed the communications industry to achieve near-capacity on a single-antenna Gaussian or fading channel with low complexity. We show how these iterative techniques can also be used to achieve near-capacity on a multiple-antenna system where the receiver knows the channel. Combining iterative processing with multiple-antenna channels is particularly challenging because the channel capacities can be a factor of ten or more higher than their single-antenna counterparts. Using a "list" version of the sphere decoder, we provide a simple method to iteratively detect and decode any linear space-time mapping combined with any channel code that can be decoded using so-called "soft" inputs and outputs. We exemplify our technique by directly transmitting symbols that are coded with a channel code; we show that iterative processing with even this simple scheme can achieve near-capacity. We consider both simple convolutional and powerful turbo channel codes and show that excellent performance at very high data rates can be attained with either. We compare our simulation results with Shannon capacity limits for ergodic multiple-antenna channel.
2,291 citations
"A low-complexity iterative MIMO det..." refers background or methods in this paper
...number of computations than the direct calculation of H [3, 10]....
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...The iterative MIMO detector calculates Ls,b, which is the LLR of the b-th bit of the s-th stream bs,b according to [3, 25]...
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...For higher data rate support, iterative detection and decoding (IDD) technology using a priori information has drawn more attention because it can achieve significantly better performance than one-way non-iterative decoding [3]....
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...Sphere decoding (SD) is modified for IDD to have a list of candidates left at the detector [3, 4]....
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