Energy-Efficient Low-Complexity Detection of PPM Signals at the Presence of Monobit Quantisation

Large scale Multiple-input multiple-output (MIMO) has been widely used in the communication field for enabling high speed data transmission. Since it uses large number of transmitting antenna, the signal detection is complicated. When noticing, the performance of the system is affected by the system configurations and mechanism. In this work, generalized spatial modulation (GSM) based transmission is used in Rayleigh fading channel with known channel correlations and fading loss. The transmission mode selection based on energy efficiency is proposed based on BER requirement which reduces the complexity. The signal detection approach, Maximum likelihood QR decomposition algorithm (MLQR) is proposed with low computational complexity. The proposed approach decomposes the channel coefficients into two matrices and the likelihood detection is used for simplifying the process. The performance of the proposed approach is evaluated with the Bit error rate (BER), and Energy efficiency. The performance measure of the proposed approach is compared with the conventional approaches for showing the efficiency.

Signal detection algorithm based on the linear minimum mean square error (LMMSE) criteria can achieve quasi-optimal performance in uplink of massive MIMO systems where the base stations are equipped with hundreds of antennas. However, it introduces complicated matrix inversion operations, thus making it prohibitively difficult to implement rapidly and effectively. In this paper, we first propose a low complexity signal detection approach by exploiting the weighting symmetric successive over-relaxation (WSSOR) iterative method to circumvent the computations in the matrix inversion. We then present a proper initial solution, relaxation parameter, and scope of the weighting factor to accelerate the convergence speed. Simulation results prove that the proposed simplified method can reach its performance quite close to that of the LMMSE algorithm with no more than three iterations.

Recently, research on mobile molecular communication (MC) has become a trend and hotspot. The timevarying channel impulse response (IR) resulting from the mobile feature makes signal detection schemes for static MC no longer applicable. In this paper, a low complexity and non-coherent detection scheme based on the energy difference between two adjacent symbols is proposed for blind signal detection in mobile scenario providing high data rates. Specifically, we consider the non-coherent signal detection scheme with respect to both the moving transmitter and the moving receiver for the first time. In contrast to existing methods for low data rates and without considering inter-symbol interference (ISI), the proposed method utilizes the ISI to achieve quality detection results at high data rates without knowing the channel state information (CSI). The influences of the mobility parameters and data rates on the bit error rate (BER) are evaluated by simulations. The simulation results demonstrate that the BER performance of the proposed scheme is much lower at the same signal intensity compared with a non-coherent scheme, and the computational complexity can be significantly reduced as well. Consequently, the proposed scheme can reduce the effect of the mobility of the nanomachines in signal detection and has the potential to implement molecular communications, especially in mobile scenarios.

In recent years, there have been more and more research on molecular communication (MC). Because the deployment of mobile nanomachines may be required in some applications of MC, research on mobile MC has become a trend. The signal detection schemes for static MC are no longer applicable due to the time varying channel impulse response (IR), which is caused by the mobile characteristics of nanomachines. In this paper, a low complexity and non-coherent detection scheme is proposed for mobile scenario, which is based on the energy difference between two adjacent symbols. Most of the existing signal detection methods do not consider inter-symbol interference (ISI). Compared with those methods, the proposed scheme can achieve signal detection utilizing ISI without knowing channel state information (CSI). The bit error rate (BER) performance of the proposed method is investigated under different conditions through simulations. Besides, the influence of mobility features of nanomachines on the signal detection accuracy is also investigated in detail. The simulation results demonstrate that the BER performance of the proposed scheme outperforms the latest signal detection scheme for short-distance mobile MC system with high velocity. Consequently, the detection scheme proposed in this paper can reduce the influence of nanomachines' mobility and has the potential to be used in mobile MC systems.

The multiple-input multiple-output (MIMO) architecture with the layered space-time codes is a very promising solution for the high data rate underwater acoustic communications. The realization of this potential advantage, however, needs the essential layered space-time signal processing methods for canceling the interference resulting from the multipath propagation and the asynchronous arrivals of the sub-streams due to the different propagation delays, and the interference between the transmitted streams superposed in each receiving hydrophone. In this paper, the low-complex layered space-time signal detection scheme for the underwater acoustic communications is investigated. A propagation delay-based ordered successive interference cancellation (OSIC) algorithm is proposed at first. Sub-streams are sorted at the receiver according to the arrival orders resulting from the relative propagation delays inherent in the underwater acoustic channels from the transmitting transducers to the receiving hydrophones. The sub-stream with the first arrival is detected first. The proposed OSIC algorithm based on the "first-come first-go" principle has an advantage in the reduction of the interference from yet-to-be-detected sub-streams, therefore improving the detection performance at each step. The analysis manifests that the delay-based ordering is an optimal detection ordering to minimize the probability of overall block error for the asynchronous space multiplexing architectures. Then the ordering procedure is given which is performed by estimating the relative delays between the MIMO channels and requires only one ordering before the signal detection. This channel estimation-based method simplifies dramatically the ordering procedure and the calculations, therefore reducing substantially the calculation complexity of the layered signal detection. Finally, the single-carrier frequency domain equalization is employed to compensate for the multipath interference and the asynchronous arrival interference from the underwater acoustic propagation. Numerical results show that the performance gain can be obtained with the delay-based OSIC detection algorithm relative to the detection without ordering; moreover the gain increases substantially with the data rate. The investigation results demonstrates, on the other hand, that the inherent relative propagation delay in the underwater acoustic channels leading to asynchronous interference to the signal detection can be turned into an advantage to improve the performance with the efficient space-time signal processing algorithms.

Massive multiple-input multiple-output (MIMO) systems can substantially improve the spectral efficiency and system capacity by equipping a large number of antennas at the base station and it is envisaged to be one of the critical technologies in the next generation of wireless communication systems. However, the computational complexity of the signal detection in massive MIMO systems presents a significant challenge for practical hardware implementations. This work proposed a novel minimum mean square error (MMSE) signal detection method based on the accelerated overrelaxation (AOR) iterative algorithm. The proposed AOR-based method can reduce the overall complexity of the classical MMSE signal detection by an order of magnitude from \( {\rm O}\left( {K^{3} } \right) \) to \( {\rm O}\left( {K^{2} } \right) \), where \( K \) is the number of users. Numerical results illustrate that the proposed AOR-based algorithm can outperform the performance of the recently proposed Neumann series approximation-based algorithm and approach the conventional MMSE signal detection involving exact matrix inversion with significantly reduced complexity.

Due to the extremely large path loss of non-line of sight optical wireless communication, the received signal exhibits the characteristics of discrete photons. In this work we investigate the signal detection and achievable rates of OOK modulation for discrete Poisson multiple access channel (MAC), for both code division multiple access (CDMA) and non-orthogonal multiple access (NOMA). For the signal detection, we adopt the maximum likelihood (ML) criterion for the optimal detection, whose complexity grows exponentially with the number of users. Low computational complexity detection is proposed with $\mathcal{M}$-order correction algorithm that can perform closely to ML detection. Moreover, we address the achievable rates and rationalize the power allocation for discrete Poisson MAC for both NOMA and CDMA. Numerical and simulation results are presented to show the performance of the proposed detection, the achievable rate region as well as the optimal power allocation of two-user discrete Poisson NOMA and CDMA.

The purpose of this paper is to analyze orthogonal frequency division multiplexing carrying offset QAM (OFDM/OQAM) signal detection using cyclostationary signatures, which are artificially embedded into digital modulated signals. The spectral correlation characterization of OFDM/OQAM signal can be described by a special linear periodic time-variant (LPTV) system. Using this description we have derived the explicit theoretical formulas of cyclic autocorrelation function (CAF) and spectral correlation function (SCF) for OQAM signal. A low-complexity cyclostationary signature detector can be utilized for OQAM signal detection, and experimental results show the advantages of this signature detector.

Massive multiple-input multiple-output (M-MIMO) can significantly enhance the spectrum efficiency of cellular networks by deploying hundreds of active elements at the base stations and is envisaged to become the key technology in 5th generation (5G) cellular networks. However, the large number of antennas required brings about tremendous challenges for practical implementation, especially for separation of the multiplexed data. Iterative approaches, such as Jacobi, Richardson, Gauss-Seidel (GS), successive overrelaxation (SOR), and symmetric successive overrelaxation (SSOR) have received great attention recently due to their low-complexity and high performance for signal detection. In this work, we provide a comprehensive review of recent progress in iterative based signal detection for massive MIMO systems. The system model of an iterative method based minimum mean square error (MMSE) signal detection is provided. The convergence behavior and complexity of the iterative approach based detectors are analyzed. Numerical results show that the iterative algorithm-based detectors can achieve a performance close to the classical MMSE detector with significantly less computational complexity.

In order to reduce the complexity of Massive multiple-input multiple-output (MIMO) signal detection, the iterative method is utilized for signal detection. Based on the implementation and analysis of the successive over relaxation (SOR) iterative algorithm, it can achieve near-optimal performance and can reduce an order of magnitude for the computational complexity. The simulation results that employing optimized relaxation factor can achieve the low bit error rate with less iteration and an efficient relaxation range is obtained to guide the relaxation factor selection.

In view of expense of extra redundancy and reduced spectral efficiency, prefix based multicarrier transmission can find its major applications in 5G and beyond 5G wireless communication networks with properly designed low complexity signal detection technique. Hybrid prefixing aided multicarrier modulated system is capable of providing reasonably acceptable bit-error rate (BER). In this paper, a comprehensive study has been made on performance evaluation of hybrid prefixing scheme implemented multicarrier cmWave wireless communication system on transmission of encrypted audio signal in a hostile flat fading channel. The 4×4 multi antenna configured simulated system under investigation incorporates LDPC and Repeat and Accumulate channel coding and various types of digital modulations (16-PSK, 16-DPSK, and 16-QAM) and signal detection (MMSE, and Cholesky Decomposition based) techniques. The implementation of pulse shaping filter in the simulated system is very much convenient to improve system performance in terms of BER through reduction of intersymbol interference (ISI). Additionally, utilization of hybrid prefix scheme enhances spectrum efficiency of the simulated system. It is noticeable from MATLAB based simulation study that the Repeat and Accumulate channel encoded simulated system is very much robust and effective in retrieving audio signal under utilization of Cholesky Decomposition based signal detection and 16-QAM digital modulation techniques.

Molecular communication (MC), which allows nanomachines to communicate with each other by using chemical molecules, is considered to be a promising method for communications in liquid environment. Available works on MC mainly focus on modulation and signal detection schemes for MC systems with fixed nanomachines, i.e., fixed molecular communication (FMC) systems. However, the more complex systems with mobile nanomachines (i.e., mobile molecular communication (MMC) systems) have been largely unexplored. This paper considers a MMC system with a fixed transmitter and a mobile receiver communicating over diffusive-drift channels of a limited boundary. We first propose a new modulation scheme to address the issue of misalignment in the signal detection of MMC systems by adopting three types of molecules in the signal modulation and modulating the transmitted signals into blocks with equal length to avoid the transferring of a signal error in the current block on the signal detection in other blocks. We then propose a new signal detection scheme of the MMC systems by calculating the distance between the transmitter and the receiver based on a distance prediction method and detecting signals at the receiver based on the decided adaptive concentration threshold in each time interval. To verify the efficiency of our proposed scheme, we then conducted extensive simulations by the Monte Carlo simulation, and comparisons are also made among our proposed schemes, a well-known fixed threshold signal detection scheme, the CATD scheme, the PAD scheme, and a low complexity signal detection scheme for MMC systems in terms of the BER (bit error rate). Results show that our proposed schemes can outperform these schemes regarding the BER.

Massive multiple-input multiple-output (MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error (MMSE) signal detection using the accelerated overrelaxation (AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.

Molecular communications convey information via diffusion propagation. The inherent long-tail channel response causes severe inter-symbol interference, which may seriously degrade signal detection performances. Traditional linear signal detection techniques, unfortunately, require both high complexity and a high signal-to-noise (SNR) ratio to operate. In this paper, we proposed a new non-linear signal processing paradigm inspired by the biological systems that achieves low-complexity signal detection even in low SNR regimes. First, we introduce a stochastic resonance inspired non-linear filtering scheme for molecular communications, and show that it significantly improves the output SNR by transforming the noise energy into useful signals. Second, we design a novel non-coherent detector by exploiting the transient features of molecular signaling, which are independent of channel response and involves only lowcomplexity linear summation operations. Numerical simulations show that this new scheme can improve the detection performance remarkably (approx. 7dB gain), even when compared against linearly optimal coherent methods. This is one of the first attempts to demodulate molecular signals from an entirely biological point of view, and the designed non-linear noncoherent paradigm will provide significant potential to the design and future implementation of nano-systems in noisy biological environments.
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Currently, most of the researches in molecular communication (MC) domain focus on the static MC scenarios. However, some envisioned important MC applications require mobile MC system. The investigation on mobile MC, especially the signal detection of mobile MC is limited. This work considers the problem of signal detection for mobile MC scenarios where the receiver nano-machine performs random movement. Due to the random movement of the receiver, the channel impulse response (CIR) changes over time which makes the received signal stochastic and complicated. This further complicates the signal detection in mobile MC and leads to that the state-of-the-art signal detection schemes for static MC scenarios fail for the mobile MC scenarios. To solve this issue, an adaptive detection scheme has been proposed by our group previously, based on dynamic estimation of the stochastically varying distance between the transmitter and receiver and the reconstruction of CIR in each interval. However, its computational complexity is high. Limited capability of current nano-machines desire low-complexity detection algorithm. In this work, we further propose an adaptive detection scheme for mobile MC with a low computational complexity by utilizing the local convex property of the CIR. With on-off keying (OOK) modulation, the signal of symbol "1" presents local convex property while that of symbol "0" presents local concave property. The convexity extent varies with the stochastic distance. A simple indicator, local maximum convexity is proposed which adapts to the stochastic distance. By comparing the adaptive indicator with an adaptive threshold within each symbol interval, the signal is detected without the need to estimate the stochastically changing distance or to reconstruct the CIR. Therefore, the computational load is effectively reduced. Numerical simulations are performed to evaluate the proposed scheme. The results show that the proposed scheme achieves good detection accuracy with low computational complexity and it could be a promising detection scheme for mobile MC scenarios.