Low-complexity Detection Technique Research Articles

Distributed spatial media-based modulation for relay networks with RF mirrors

A novel distributed spatial media-based modulation scheme is proposed in this paper by cleverly utilizing distributed spatial modulation (DSM) and media-based modulation (MBM) principles. This proposed scheme is referred to as distributed channel modulation (DCM) for relay networks. In this scheme, decode-and-forward relaying protocol is adopted, and the channel states are exploited for transmitting extra information bits by using a number of radio frequency (RF) mirrors that are placed near each relay. To provide a fair comparison with the conventional state-of-the-art schemes, the symbol error rate (SER) performance of DSM scheme is evaluated. Besides, a low complexity detection technique known as iterative maximum ratio combining (i-MRC) is used in order to reduce the receiver complexity of the proposed scheme. Simulation results demonstrate that the proposed DCM scheme significantly outperforms DSM scheme for the same average rate. It is also shown that there is a negligible degradation in the SER performance of the proposed DCM scheme when i-MRC detection is used as compared to the performance with maximum likelihood (ML) detection. Furthermore, a significant reduction in the receiver complexity is achieved by using i-MRC detection technique in contrast to the results with ML detector. It has been also revealed that the proposed DCM scheme shows a performance drop of about 3 dB when the availability of an imperfect channel state information (CSI) is assumed with the presence of channel estimation errors (CEEs). Finally, simulation results have confirmed the analytical findings.

Compressive sensing‐based low‐complexity detector for differential spatial modulation

Detection of differential spatial modulation (DSM) needs only the received symbols, unlike spatial modulation that requires channel state information at the receiver. The maximum likelihood (ML)-based DSM receiver, though works well for a small number of transmit antennas, is computationally prohibitive for higher number of antennas. The hard-limiter based ML (HL-ML) receiver has low search complexity in the higher signal-to-noise regions only. The authors propose a low-complexity detection technique using compressive sensing for DSM. The detection involves a two-step process, by first finding the antenna index number and then the M-ary phase-shift keying (MPSK) symbol transmitted from that antenna. After performing a correlation between the past and the present received symbol blocks, the index location of the largest elements of this operation reveals the order of antennas that were activated in a symbol duration. The MPSK symbol is found by a pseudo-inverse operation on the columns of the past and the present received symbol blocks, corresponding to the time index and antenna number, which is same as the above indices. The proposed receiver achieves a substantial reduction in computational complexity close to 63%, when compared to the HL-ML receiver and the performance penalty narrows down with higher order MPSK constellations.

Low-Complexity Detection and Transmit Antenna Selection for Spatial Modulation

In this paper, we propose a novel low-complexity detection technique for conventional Spatial Modulation (SM). The proposed scheme is compared to SM with optimal detection (SM-OD), SM with signal vector based detection (SM-SVD) and another reduced complexity detection technique, presented in the literature. The numerical results show that the proposed scheme can match the error performance of SM-OD very closely, even at low bit-error rate (BER). The computational complexity at the receiver, is shown to be independent of the symbol constellation size, and hence, offers a much lower complexity compared to existing schemes. To further improve the error performance, we then propose two closed-loop transmit antenna selection (TAS) schemes for conventional SM. We assume the receiver has knowledge of the channel and a perfect low-bandwidth feedback path to the transmitter exists. From evaluation of the BER versus normalized signal-to-noise ratio (SNR) and the complexity analysis, the proposed schemes exhibit a significant improvement over SM-OD and other improved SM schemes in terms of error performance and/or complexity.

Achieving Full Diversity in Multi-Antenna Two-Way Relay Networks via Symbol-Based Physical-Layer Network Coding

This paper considers physical-layer network coding (PNC) with M-ary phase-shift keying (MPSK) modulation in two-way relay channel (TWRC). A low complexity detection technique, termed symbol-based PNC (SPNC), is proposed for the relay. In particular, attributing to the outer product operation imposed on the superposed MPSK signals at the relay, SPNC obtains the network-coded symbol (NCS) straightforwardly without having to detect individual symbols separately. Unlike the optimal multi-user detector (MUD) which searches over the combinations of all users' modulation constellations, SPNC searches over only one modulation constellation, thus simplifies the NCS detection. Despite the reduced complexity, SPNC achieves full diversity in multi-antenna relay as the optimal MUD does. Specifically, antenna selection based SPNC (AS-SPNC) scheme and signal combining based SPNC (SC-SPNC) scheme are proposed. Our analysis of these two schemes not only confirms their full diversity performance, but also implies when SPNC is applied in multi-antenna relay, TWRC can be viewed as an effective single-input multiple-output (SIMO) system, in which AS-PNC and SC-PNC are equivalent to the general AS scheme and the maximal-ratio combining (MRC) scheme. Moreover, an asymptotic analysis of symbol error rate (SER) is provided for SC-PNC considering the case that the number of relay antennas is sufficiently large.

Low-Complexity Decoding via Reduced Dimension Maximum-Likelihood Search

In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable performance loss due to a reduction in the search space is compensated by 1) the use of a list tree search, which is an extension of a single best searching algorithm called sphere decoding, and 2) the recomputation of a set of weak symbols, i.e., those ignored in the reduced dimension search, for each strong symbol candidate found during the list tree search. Through simulations on M-quadrature amplitude modulation (QAM) transmission in frequency nonselective multi-input-multioutput (MIMO) channels, we demonstrate that the RD-MLS algorithm shows near constant complexity over a wide range of bit error rate (BER) (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ~ 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> ), while limiting performance loss to within 1 dB from ML detection.

Graph-Based Channel Detection for Multitrack Recording Channels

We propose a low complexity detection technique for multihead multitrack recording systems. By exploiting sparseness of two-dimensional partial response (PR) channels, we develop an algorithm which performs belief propagation (BP) over corresponding factor graphs. We consider the BP-based detector not only for partial response channels but also for more practical conventional media and bit-patterned media storage systems, with and without media noise. Compared to the maximum likelihood detector which has a prohibitively high complexity that is exponential with both the number of tracks and the number of intersymbol interference (ISI) taps, the proposed detector has a much lower complexity and a fast parallel structure. For the multitrack recording systems with PR equalization, the price is a small performance penalty (less than one dB if the intertrack interference (ITI) is not too high). Furthermore, since the algorithm is soft-input soft-output in nature, turbo equalization can be employed if there is an outer code. We show that a few turbo equalization iterations can provide significant performance improvement even when the ITI level is high.

A scalar MSDD with multiple antenna reception of Differential Space-Time π/2-Shifted BPSK Modulation

In this paper, the issue of blind detection of Alamouti-type differential space-time (ST) -shifted BPSK modulation in static Rayleigh fading channels is considered. We introduce a novel transformation to the received signal from each receiver antenna such that this binary ST modulation, which has a second-order transmit-diversity, is equivalent to QPSK modulation with second-order receive-diversity. The pre-detection combining of the result of transformation allows us to apply a low complexity detection technique specifically designed for receive-diversity, namely, scalar multiple-symbol differential detection (MSDD). With receiver complexity proportional to the observation window length, our receiver can achieve the performance 1.5dB better than that of conventional differential detection ST and 0.5dB worse than that qf a coherent maximum ratio combining receiver (with differential decoding) approximately.

A Receiver for Differential Space-Time-Shifted BPSK Modulation Based on Scalar-MSDD and the EM Algorithm

In this paper, we consider the issue of blind detection of Alamouti-type differential space-time (ST) modulation in static Rayleigh fading channels. We focus our attention on a-shifted BPSK constellation, introducing a novel transformation to the received signal such that this binary ST modulation, which has a second-order transmit diversity, is equivalent to QPSK modulation with second-order receive diversity. This equivalent representation allows us to apply a low-complexity detection technique specifically designed for receive diversity, namely, scalar multiple-symbol differential detection (MSDD). To further increase receiver performance, we apply an iterative expectation-maximization (EM) algorithm which performs joint channel estimation and sequence detection. This algorithm uses minimum mean square estimation to obtain channel estimates and the maximum-likelihood principle to detect the transmitted sequence, followed by differential decoding. With receiver complexity proportional to the observation window length, our receiver can achieve the performance of a coherent maximal ratio combining receiver (with differential decoding) in as few as a single EM receiver iteration, provided that the window size of the initial MSDD is sufficiently long. To further demonstrate that the MSDD is a vital part of this receiver setup, we show that an initial ST conventional differential detector would lead to strange convergence behavior in the EM algorithm.