Reduced-Rank Adaptive Least Bit-Error-Rate Detection in Hybrid Direct-Sequence Time-Hopping Ultrawide Bandwidth Systems

Abstract

Abstract-The design of high-efficiency low-complexity detection schemes for ultrawide bandwidth (UWB) systems is highly challenging. This paper proposes a reduced-rank adaptive multiuser detection (MUD) scheme that is operated in least bit-error-rate (LBER) principles for hybrid direct-sequence time-hopping UWB (DS-TH UWB) systems. The principal component analysis (PCA)-assisted rank-reduction technique is employed to obtain a detection subspace, where the reduced-rank adaptive LBER-MUD is carried out. The reduced-rank adaptive LBER-MUD is free from channel estimation and does not require knowledge about the number of resolvable multipaths and the multipaths' strength. In this paper, the BER performance of the hybrid DS-TH UWB systems using the proposed detection scheme is investigated, assuming communications over UWB channels modeled by the Saleh-Valenzuela channel model. Our studies and performance results show that, given a reasonable rank of the detection subspace, the reduced-rank adaptive LBER-MUD can efficiently mitigate both multiuser and intersymbol interference (ISI) and achieve the diversity gain promised by the UWB systems.Index Terms-Adaptive detection, direct sequence, least bit error rate (LBER), principal component analysis (PCA), reduced-rank detection, time hopping, ultrawide bandwidth (UWB).

Adaptive Reduced-Rank Receive Processing Based on Minimum Symbol-Error-Rate Criterion for Large-Scale Multiple-Antenna Systems

“…In particular, we consider a commonly used configuration with P = 3, I = 12 and D = 10. From these results, we find that the proposed algorithms have significantly lower complexity than the existing MSER-MSWF reduced-rank algorithm 6 [38]. Although the complexity of the proposed algorithms increases as the number B of branches increases, it remains lower than that of the MSER-JIO reduced-rank algorithms [40] for a large number of antenna elements.…”

“…By using (35) for the reduced-rank receive filter or (36) for the preprocessing filter, we have equivalently ω l,R 0,0 (i) > 0 and ω l,I 0,0 (i) = 0, where ω l,R 0,0 (i) and ω l,I 0,0 (i) represent the real and imaginary parts of ω l 0,0 (i) after phase rotation, respectively. Hence, the symbol decisionb 0 (i) =b R 0 (i) + jb I 0 (i) can be made as (37) and (38), whereb R 0 (i) andb I 0 (i) represent the real and imaginary parts of the estimated M -QAM symbol, respectively.…”

“…After the optimum filter lengths are determined for all the branches, we select the optimum branch of the JPDF scheme based on the following criterion Full-Rank-LMS 2LP + 1 2LP Full-Rank-MSER [35] 3LP + 1 2LP MSER-JIO [40] 8LP D + 7D + 2LP + 9 7LP D + 2LP − 1 EIG [13] O((LP ) 3 ) O((LP ) 3 ) MSER-MSWF [38] D Full-Rank-LMS 2LP + 1 2LP Full-Rank-MSER [7] 6LP + 5 5LP MSER-JIO [40] 10LP D + 7D + 4LP + 17 9LP D + 4LP + 3 EIG [13] O((LP ) 3 ) O((LP ) 3 ) MSER-MSWF [38] D(LP ) 2 + 5LP D + 5D + 2LP + 11…”

“…2 and Fig. 3, we show the computational complexity against the number of received antenna elements for the recently reported MSER-based reduced-rank algorithms [38], [40] and the proposed MSER-JPDF reduced-rank algorithms. In particular, we consider a commonly used configuration with P = 3, I = 12 and D = 10.…”

“…8 and Fig. 9 show the SER performance versus the number of received symbols for the proposed adaptive MSER-JPDF reduced-rank algorithms with fixed and automatic parameter selection schemes and for the existing adaptive fullrank and reduced-rank algorithms, namely: the MSER-SG full-rank adaptive algorithm [7], [35], the MMSE-based LMS full-rank adaptive algorithm [11], the MSER-based MSWF adaptive reduced-rank algorithm [38] and the MSER-based adaptive JIO reduced-rank algorithm [40]. In Fig.…”