Likelihood function for QRM-MLD suitable for soft-decision turbo decoding and its performance for OFCDM MIMO multiplexing in multipath fading channel

Higuchi, Kenichi; Kawai, H.; Maeda, Noriko; Sawahashi, Mamoru; Itoh, T.; Kakura, Yoshikazu; Ushirokawa, Akihisa; Seki, Hiroyuki

doi:10.1109/pimrc.2004.1373877

Cited by 38 publications

(51 citation statements)

References 7 publications

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“…14 shows the block diagram of Fig. 14, we first compute a symbol reliability for each constellation point based on (10). Next, the bit LLR value is computed using these symbol reliability values based on (12).…”

Section: E Llr Calculation Unit (Lcu)mentioning

confidence: 99%

Trellis-Search Based Soft-Input Soft-Output MIMO Detector: Algorithm and VLSI Architecture

Sun

Cavallaro

2012

IEEE Trans. Signal Process.

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Abstract-In this paper, we propose a trellis-search based soft-input soft-output detection algorithm and its very large scale integration (VLSI) architecture for iterative multiple-input multiple-output (MIMO) receivers. We construct a trellis diagram to represent the search space of a transmitted MIMO signal. With the trellis model, we evenly distribute the workload of candidates searching among multiple trellis nodes for parallel processing. The search complexity is significantly reduced because the number of candidates is greatly limited at each trellis node. By leveraging the trellis structure, we develop an approximate Log-MAP algorithm by using a small list of largest exponential terms to compute the LLR (log-likelihood ratio) values. The trellis-search based detector has a fixed-complexity and is very suitable for parallel VLSI implementation. As a case study, we have designed and synthesized a trellis-search based soft-input soft-output MIMO detector for a 4 4 16-QAM system using a 1.08 V TSMC 65 nm technology. The detector can achieve a maximum throughput of 1.7 Gb/s with a core area of 1.58 mm .Index Terms-ASIC, MIMO algorithm, soft-input soft-output MIMO detection, trellis-search algorithm, VLSI.

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Section: E Llr Calculation Unit (Lcu)mentioning

confidence: 99%

Trellis-Search Based Soft-Input Soft-Output MIMO Detector: Algorithm and VLSI Architecture

Sun

Cavallaro

2012

IEEE Trans. Signal Process.

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“…Unless noted otherwise, the parameters summarized in Table 1 are used as follows as in [1]. The number of transmit or receive antennas is N t = N r = 3.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…As in [1], the channel estimation is carried out firstly on each sub-carrier by using the timemultiplexed pilot channel. Given the channel model in (6), the received signal vector at the N r antennas is given by…”

Section: Channel Estimationmentioning

confidence: 99%

“…To improve the channel estimation, ξ n r ,n t [n c ] is further averaged over adjacent sub-carriers in frequency domain due to the coherence of adjacent sub-carriers [1]. Using a sliding window average, the element,h n r ,n t [n c ], of the final channel estimation matrix,H [n c ], for the n c -th sub-carrier is given bỹ…”

Section: Channel Estimationmentioning

confidence: 99%

“…a very-small cell with high traffic density, hotspot area, or indoor environment [1]. Multipleinput multiple-output (MIMO) systems, employing multiple transmit and receive antennas, are known to provide much higher capacity and therefore much higher throughput than single-input single-output systems [2][3][4].…”

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confidence: 99%

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Multiple LDPC-Encoder Layered Space-Time-Frequency Architectures for OFDM MIMO Multiplexing

Huang

Wang

2010

Wireless Pers Commun

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This paper is focused on the study of layered space-time-frequency (LSTF) architectures with channel coding for orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) multiplexing systems for high speed wireless communications over a frequency-selective fading channel. In order to achieve the available spatial, temporal and frequency diversities, and also make the system implementation feasible for high speed OFDM MIMO multiplexing, a novel LSTF architecture with multiple channel encoders is proposed with each independently coded layer being threaded in the three-dimensional space-time-frequency transmission resource array. Non-iterative receiver is adopted which consists of list sphere detector and irregular low-density parity-check codes as the constituent codes. Simulation results show that the performance of the proposed multipleencoder LSTF architecture is very close to that of the conventional single-encoder LSTF where coding is applied across the whole information stream. However, due to the use of multiple parallel encoders/decoders with a shorter codeword length, the proposed LSTF architecture has much lower hardware processing speed and complexity than the conventional LSTF.

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Multiple‐encoder layered space–time–frequency architecture with QR decomposition and M‐algorithm maximum‐likelihood detection

Huang

2011

Wireless Communications

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In this paper, to achieve the available spatial, temporal, and frequency diversities, and also to make the system implementation feasible for high-speed orthogonal frequency division multiplexing multiple-input multiple-output multiplexing, a novel layered space-time-frequency (LSTF) transmitter architecture with multiple channel encoders is proposed with each independently coded information sub-stream being threaded in the three-dimensional (3-D) space-time-frequency transmission resource array. The performance of a non-iterative receiver consisting of a maximum-likelihood detector with QR decomposition and M-algorithm maximum-likelihood detection is exploited, by employing irregular low-density paritycheck code as the channel code. The threaded distribution of each coded information sub-stream in the proposed LSTF architecture makes it achieve the spatial, temporal, and frequency diversities the same as the conventional single-encoder LSTF architecture where coding is applied across the whole information stream, and simulation results show that the performance of the proposed multiple-encoder LSTF architecture is very close to that of the conventional single-encoder LSTF architecture. However, because of the use of multiple parallel encoders/decoders with a shorter codeword length, the proposed LSTF architecture has much lower hardware processing speed and complexity than that of the conventional LSTF architecture.

show abstract

Likelihood function for QRM-MLD suitable for soft-decision turbo decoding and its performance for OFCDM MIMO multiplexing in multipath fading channel

Cited by 38 publications

References 7 publications

Trellis-Search Based Soft-Input Soft-Output MIMO Detector: Algorithm and VLSI Architecture

Trellis-Search Based Soft-Input Soft-Output MIMO Detector: Algorithm and VLSI Architecture

Multiple LDPC-Encoder Layered Space-Time-Frequency Architectures for OFDM MIMO Multiplexing

Multiple‐encoder layered space–time–frequency architecture with QR decomposition and M‐algorithm maximum‐likelihood detection

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