Various efficient generalized sphere decoding (GSD) algorithms have been proposed to approach optimal ML performance for underdetermined linear systems, by transforming the original problem into the full-column-rank o...Various efficient generalized sphere decoding (GSD) algorithms have been proposed to approach optimal ML performance for underdetermined linear systems, by transforming the original problem into the full-column-rank one so that standard SD can be fully applied. However, their design parameters are heuristically set based on observation or the possibility of an ill-conditioned transformed matrix can affect their searching efficiency. This paper presents a better transformation to alleviate the ill-conditioned structure and provides a systematic approach to select design parameters for various GSD algorithms in order to high efficiency. Simulation results on the searching performance confirm that the proposed techniques can provide significant improvement.展开更多
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-pru...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.展开更多
Multiple Input Multiple Output (MIMO) technology is of great significance in high data rate wireless communication. The K-Best Sphere Decoding (K-Best SD) algorithm was proposed as a powerful method for MIMO detection...Multiple Input Multiple Output (MIMO) technology is of great significance in high data rate wireless communication. The K-Best Sphere Decoding (K-Best SD) algorithm was proposed as a powerful method for MIMO detection that can approach near-optimal performance. However, some extra computational complexity is contained in K-Best SD. In this paper, we propose an improved K-Best SD to reduce the complexity of conventional K-Best SD by assigning K for each level dynamically following some rules. Simulation proves that the performance degradation of the improved K-Best SD is very little and the complexity is significantly reduced.展开更多
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 ...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.展开更多
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） detec...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.展开更多
文摘Various efficient generalized sphere decoding (GSD) algorithms have been proposed to approach optimal ML performance for underdetermined linear systems, by transforming the original problem into the full-column-rank one so that standard SD can be fully applied. However, their design parameters are heuristically set based on observation or the possibility of an ill-conditioned transformed matrix can affect their searching efficiency. This paper presents a better transformation to alleviate the ill-conditioned structure and provides a systematic approach to select design parameters for various GSD algorithms in order to high efficiency. Simulation results on the searching performance confirm that the proposed techniques can provide significant improvement.
基金supported by the National Natural Science Foundation of China(61071083)
文摘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.
文摘Multiple Input Multiple Output (MIMO) technology is of great significance in high data rate wireless communication. The K-Best Sphere Decoding (K-Best SD) algorithm was proposed as a powerful method for MIMO detection that can approach near-optimal performance. However, some extra computational complexity is contained in K-Best SD. In this paper, we propose an improved K-Best SD to reduce the complexity of conventional K-Best SD by assigning K for each level dynamically following some rules. Simulation proves that the performance degradation of the improved K-Best SD is very little and the complexity is significantly reduced.
基金Supported by the National Natural Science Foundation of China under Grant Nos. 62101489, 62171405 and 62225114.
文摘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.
基金Supported by the National Basic Research Program of China (973 Program) (Grant No. 2009CB320403)the National Defense Pre-researchProject of the 11th Five-Year-Plan of China (Grant No. 1060741001020102)
文摘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.
