Reduced K-best sphere decoding algorithm based on minimum route distance and noise variance

This paper focuses on reducing the complexity of K-best sphere decoding （SD） algorithm for the detection of uncoded multi-ple input multiple output （MIMO） systems. The proposed algorithm utilizes the threshold-pruning method to cut nodes with partial Euclidean distances （PEDs） larger than the threshold. Both the known noise value and the unknown noise value are considered to generate the threshold, which is the sum of the two values. The known noise value is the smal est PED of signals in the detected layers. The unknown noise value is generated by the noise power, the quality of service （QoS） and the signal-to-noise ratio （SNR） bound. Simulation results show that by considering both two noise values, the proposed algorithm makes an efficient reduction while the performance drops little.

Complexity study of sphere decoding in multi-antenna systems

In this article a bridge between the expected complexity and performance of sphere decoding （SD） is built. The expected complexity of SD for infinite lattices is then investigated, which naturally is the upper-bound of those for all the finite lattices if given by the same channel matrix and signal noise ratio （SNR）. Such expected complexity is an important characterization of SD in multi-antenna systems, because no matter what modulation scheme is used in practice （generally it has finite constellation size） this upper-bound holds. Above bridge also leads to a new method of determining the radius for SD. The numerical results show both the real value and upper-bound of average searched number of candidates in SD for 16-QAM modulated system using the proposed sphere radius determining method. Most important of all new understandings of expected complexity of SD are given based on above mentioned theoretic analysis and numerical results.

Multiple symbol differential detection based on sphere decoding for unitary space-time modulation

Recently, a multiple symbol differential （MSD） sphere decoding （SD） algorithm for unitary spacetime modulation over quasi-static channel has been proved to achieve the performance of maximumlikelihood （ML） detection with relatively low complexity. However, an error floor occurs if the algorithm is applied over rapid-fading channels. Based on the assumption of continuous fading, a multiple symbol differential automatic sphere decoding （MSDASD） algorithm is developed by incorporating a recursive form of an ML metric into automatic SD （ASD） algorithm. Furthermore, two algorithms, termed as MSD approximate ASD （MSDAASD） and MSD pruning ASD （MSDPASD）, are proposed to reduce computational complexity and the number of comparisons, respectively. Compared with the existing typical algorithms, i.e., multiple symbol differential feedback detection （MS-DFD） and noncoherent sequence detection （NSD）, the performance of the proposed algorithms is much superior to that of MS-DFD and a little inferior to that of NSD, while the complexity is lower than that of MS-DFD in most cases and significantly lower than that of NSD.

Combined simplified maximum likelihood and sphere decoding algorithm for MIMO system

In this article, a new system model for sphere decoding （SD） algorithm is introduced. For the 2 × 2 multipleinput multiple-out （MIMO） system, a simplified maximum likelihood （SML） decoding algorithm is proposed based on the new model. The SML algorithm achieves optimal maximum likelihood （ML） performance, and drastically reduces the complexity as compared to the conventional SD algorithm. The improved algorithm is presented by combining the sphere decoding algorithm based on Schnorr-Euchner strategy （SE-SD） with the SML algorithm when the number of transmit antennas exceeds 2. Compared to conventional SD, the proposed algorithm has low complexity especially at low signal to noise ratio （SNR）. It is shown by simulation that the proposed algorithm has performance very close to conventional SD.

基于信道短化和圆球译码的水声通信迭代序列检测

The demand for high-data-rate underwater acoustic communications(UACs)in marine development is increasing;however,severe multipaths make demodulation a challenge.The decision feedback equalizer(DFE)is one of the most popular equalizers in UAC;however,it is not the optimal algorithm.Although maximum likelihood sequence estimation(MLSE)is the optimal algorithm,its complexity increases exponentially with the number of channel taps,making it challenging to apply to UAC.Therefore,this paper proposes a complexity-reduced MLSE to improve the bit error rate(BER)performance in multipath channels.In the proposed algorithm,the original channel is first shortened using a channel-shortening method,and several dominant channel taps are selected for MLSE.Subsequently,sphere decoding(SD)is performed in the following MLSE.Iterations are applied to eliminate inter-symbol interference caused by weak channel taps.The simulation and sea experiment demonstrate the superiority of the proposed algorithm.The simulation results show that channel shortening combined with SD can drastically reduce computational complexity,and iterative SD performs better than DFE based on recursive least squares(RLS-DFE),DFE based on improved proportionate normalized least mean squares(IPNLMS-DFE),and channel estimation-based DFE(CE-DFE).Moreover,the sea experimental results at Zhairuoshan Island in Zhoushan show that the proposed receiver scheme has improved BER performance over RLSDFE,IPNLMS-DFE,and CE-DFE.Compared with the RLS-DFE,the BER,after five iterations,is reduced from 0.0076 to 0.0037 in the 8–12 k Hz band and from 0.1516 to 0.1145 in the 13–17 k Hz band at a distance of 2000 m.Thus,the proposed algorithm makes it possible to apply MLSE in UAC in practical scenarios.

Improved List Sphere Decoder for Multiple Antenna Systems

An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fixed initial radius, the ILSD adopts an adaptive radius to accelerate the list cdnstruction. Characterized by low-complexity and radius-insensitivity, the proposed algorithm makes iterative joint detection and decoding more realizable in multiple-antenna systems. Simulation results show that computational savings of ILSD over LSD are more apparent with more transmit antennas or larger constellations, and with no performance degradation. Because the complexity of the ILSD algorithm almost keeps invariant with the increasing of initial radius, the BER performance can be improved by selecting a sufficiently large radius.

VLSI implementation of MIMO detection for 802.11n using a novel adaptive tree search algorithm

A 4×4 64-QAM multiple-input multiple-output （MIMO） detector is presented for the application of an IEEE 802.1 In wireless local area network. The detector is the implementation of a novel adaptive tree search （ATS） algorithm, and multiple ATS cores need to be instantiated to achieve the wideband requirement in the 802.11 n standard. Both the ATS algorithm and the architectural considerations are explained. The latency of the detector is 0.75 μs, and the detector has a gate count of 848 k with a total of 19 parallel ATS cores. Each ATS core runs at 67 MHz. Measurement results show that compared with the floating-point ATS algorithm, the fixed-point imple- mentation achieves a loss of 0.9 dB at a BER of 10^-3.

Multistage soft decision equalization technique for MIMO systems in frequency selective channels

This article proposes a multistage soft decision equalization （SDE） technique for block transmission over frequency selective multi-input multi-output （MIMO） channels. Using the Toeplitz structure, the general signal model can be converted into a series of small-sized sub-signal models. For each sub-signal model, soft interference cancellation （SIC） is used firstly to remove partial effects of interfering symbols, then max-log-map sphere decoder is performed to get the desired a posteriori information. Simulation shows that with lower complexity the proposed method outperforms the probability data association SDE and the Schnorr-Euchner sphere decoder.