Maximum Likelihood Detection Research Articles

Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) has great potential for the implementation of high spectral-efficiency underwater acoustic (UWA) communications. However, general receivers consisting of the optimal maximum likelihood detection suffer from high computational load, which prohibits real-time data transmissions in underwater scenarios. In this paper, we propose a detection based on a vector approximate message passing (VAMP) algorithm for UWA OFDM-IM communications. Firstly, a VAMP framework with a non-loopy factor graph for index detection is formulated. Secondly, by utilizing the sparsity inherently existing in OFDM-IM symbols, a novel shrinkage function is derived based on the minimum mean square error criterion, which guarantees better posterior estimation. To reduce the errors from estimated non-existing indices, one trick is utilized to search the elements from the look-up table with the minimal Euclidean distance for the replacement of erroneously estimated indices. Experiments verify the advantages of the proposed detector in terms of low complexity, robustness and effectiveness compared with the state-of-art benchmarks.

Fast Transceiver Design for RIS-Assisted MIMO mmWave Wireless Communications

Due to high bandwidth and small antenna size, millimeter-wave (mmWave) integrated line-of-sight (LOS) multiple-input-multiple-output (MIMO) systems have attracted much attention. Reconfigurable intelligent surfaces (RISs), which have the potential to change the characteristics of incident electromagnetic waves with low power cost, can improve the performance for the MIMO mmWave wireless communications. Uniform circular array (UCA) is an effective antenna structure with low complexity transceiver. In this paper, UCA based RIS-assisted MIMO mmWave wireless communications with transmit UCA, the RIS UCAs, and receive UCA are investigated. Since the rotation angles between the transceiver make the channel matrix noncirculant, an algorithm is developed to derive the ranges of the rotation angles based on an acceptable error and reduce the impact of rotation angles on channel matrix. Then, we propose a low-complexity precoding scheme at the transmitter, phase designs at the RIS UCAs, and a phase compensation scheme at the receiver, which can convert the channel matrix into an equivalent circulant channel matrix with a small error. Then, a fast symbol-wise maximum likelihood (ML) detection scheme is proposed to recover the signals with low computational complexity. Simulation results are presented to illustrate the theory.

Relay‐assisted multiuser MIMO‐DQSM system for correlated fading channels

AbstractThis paper presents the performance evaluation of an amplify‐and‐forward (AF) relay‐assisted multiuser multiple input–multiple output (MU–MIMO) downlink transmission system for correlated fading channels. The overall system performance was improved by incorporating a double‐quadrature spatial modulation (DQSM) scheme. The bit error rate (BER) performance and detection complexity of the AF–MU–MIMO–DQSM system were analyzed and compared with those of a conventional AF–MU–MIMO system under the same conditions and parameters. The results showed that the correlated fading channel severely affected the performance of systems with higher spectral efficiency (SE). Considering an SE of 12 bpcu/user, the AF–MU–MIMO–DQSM system yielded a gain of up to 3 dB in BER performance compared with that of its conventional counterpart for the analyzed cases. In terms of detection complexity, the AF–MU–MIMO–DQSM system showed a reduction of up to 56 % compared with that of the conventional system for the optimal maximum likelihood detection criterion.

Reconfigurable-Intelligent-Surface-Aided Number Modulation for Symbiotic Active/Passive Transmission

Reconfigurable intelligent surface (RIS)-aided symbiotic active/passive transmission is a promising communication paradigm, which is able to improve the propagation environment while transmitting additional information. In this paper, a novel scheme, termed RIS-aided number modulation (RIS-NM), is proposed for symbiotic active/passive communications. In RIS-NM, the RIS elements are divided into in-phase (I-) and quadrature (Q-) subsets depending on their phase shift configurations, and the number of elements in the I-subset (or Q-subset, equivalently) is used to convey the RIS’s private information. A low-complexity yet near-optimal detector is designed for RIS-NM by shrinking the search space of constellation points. We then investigate a special case of RIS-NM, termed RIS-aided number shift keying (RIS-NSK), in which the radio-frequency source transmits unmodulated carrier signals. Statistic channel state information (CSI)-based maximum-likelihood (ML) detection is developed for RIS-NSK. We analyze the bit error rate (BER) performance of RIS-NM/NSK over Rician fading channels. BER upper bounds are derived in closed-form for RIS-NM by assuming instantaneous CSI-based ML detection, while an approximate BER expression is obtained for RIS-NSK by assuming statistic CSI-based ML detection. Furthermore, we extend RIS-NM to multiple-input multiple-output scenarios. Our simulation results in terms of BER corroborate the performance analysis and the superiority of RIS-NM over the state-of-the-art RIS-aided symbiotic active/passive transmission scheme.

Discrete Hartley transform-based dual-mode index modulation OFDM for visible light communications

Using discrete Hartley transform (DHT) instead of discrete Fourier transform (DFT) in the optical index modulation (IM) aided orthogonal frequency division multiplexing (OFDM) systems for visible light communications (VLC) results in improvement in terms of the spectral efficiency, energy efficiency, peak-to-average power ratio, and bit error rate (BER) performance with a lower computational complexity, benefiting from the removal of the Hermitian symmetry. In this paper, by employing dual-mode (DM) index modulation in the DHT-based OFDM systems, more spectral efficient schemes for VLC are proposed. A reduced-complexity maximum likelihood (ML) detector and a simple log-likelihood ratio (LLR) detector for the DM-OFDM-IM systems are presented, achieving a better performance with a lower complexity over the traditional detectors. The spectral efficiency and the average BER are analyzed. The minimum distance between different transmitted subblock vectors is calculated for characterizing the performance. The simulation results demonstrate that the DHT-based DM-OFDM-IM systems achieve significant performance gains over the DHT-based OFDM-IM counterparts and perform better than the DFT-based counterparts in terms of the spectral efficiency and BER performance.

Beamforming and data detection in intelligent reflecting surfaces‐assisted communication using deep learning

SummaryIn this article, we propose two deep neural network (DNN) architectures for an intelligent reflecting surface (IRS)‐assisted network with a single transmitter and multiple receivers. The first DNN is designed to obtain an optimal beamforming weight vector that maximizes the achievable sum rate over the IRS‐aided network. The beamforming vector is designed based on a pilot‐based channel estimation procedure, which requires only a small fraction of the available number of reflecting surfaces on the IRS to be active. The obtained vector is fed to the second DNN, which is designed for detecting information symbols from the transmitter. Our simulation study shows that the achievable rate obtained using the designed beamforming vector approaches the maximum achievable rate quickly, as the number of active elements increases. The impact of the number of active elements and the beamforming vector on the performance of the detector is also studied, in terms of the bit error rate (BER). We show that the BER performance of the second DNN is close to that of the optimal maximum likelihood detector. Further, we study the effect of the channel estimation errors on the performance of the DNNs. Moreover, we study the effect of the hyperparameters of the architectures on the training time and performance, in terms of root mean squared error and accuracy.

Image super resolution based channel estimation for future wireless communication

In this paper, a novel image-based Deep Learning (DL) approach for channel estimation for future wireless communications is proposed. The time–frequency response of the fast-fading wireless channel is represented as a 2D image to estimate the unknown values of the channel response using known values at the pilot locations. With given images, both image Super-Resolution (SR) and image Denoising Network (DnN), termed as Super-Resolution and Denoising Network (SRDnN), are combined to estimate the wireless channel. To show the effectiveness, the proposed SRDnN is applied to Massive Multiple-Input Multiple-Output (mMIMO) with Non-Orthogonal Multiple Access (NOMA). The enhanced performances of SRDnN are quantified in terms of Mean Square Error (MSE) and Symbol Error Rate (SER). In addition, the influence of pilot numbers on SRDnN performance for next-generation mMIMO-NOMA networks is presented. The simulation results demonstrate that SRDnN is comparable to the level of Maximum Likelihood (ML) detection for both with and without complete Channel State Information (CSI) at the receiver with less number of pilots.

Block-Wise Index Modulation and Receiver Design for High-Mobility OTFS Communications

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over the existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIM-OTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI).

Non-Orthogonal Multiple Access Based on Orthogonal Space-Time Block Codes for Mobile Communications

Non-orthogonal multiple access (NOMA), which combines multiple user signals and transmits the combined signal over one channel, can achieve high spectral efficiency for mobile communications. However, combining the multiple signals can lead to degradation of bit error rates (BERs) of NOMA under severe channel conditions. In order to improve the BER performance of NOMA, this paper proposes a new NOMA scheme based on orthogonal space-time block codes (OSTBCs). The proposed scheme transmits several multiplexed signals over their respective orthogonal time-frequency channels, and can gain diversity effects due to the orthogonality of OSTBC. Furthermore, the new scheme can detect the user signals using low-complexity linear detection in contrast with the conventional NOMA. The paper focuses on the Alamouti code, which can be considered the simplest OSTBC, and theoretically analyzes the performance of the linear detection. Computer simulations under the condition of the same bit rate per channel show that the Alamouti code based scheme using two channels is superior to the conventional NOMA using one channel in terms of BER performance. As shown by both the theoretical and simulation analyses, the linear detection for the proposed scheme can maintain the same BER performance as that of the maximum likelihood detection, when the two channels have the same frequency response and do not bring about any diversity effects, which can be regarded as the worst case.

ML and DL-based Phishing Website Detection: The Effects of Varied Size Datasets and Informative Feature Selection Techniques

One must interact with a specific webpage or website in order to use the Internet for communication, teamwork, and other productive activities. However, because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites, they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware. It is impossible to stop attackers from creating phishing websites, which is one of the core challenges in combating them. However, this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handover sensitive information. In this study, five ML and DL algorithms—CATB, GB, RF, MLP, and DNN—were tested with three different reputable datasets and two useful feature selection techniques, to assess the scalability and consistency of each classifier's performance on varied dataset sizes. The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets (DS-1, DS-2, and DS-3) with respective values of 97.9%, 95.73%, and 98.83%. The GB classifier achieved the second-best accuracy across all datasets (DS-1, DS-2, and DS-3) with respective values of 97.16%, 95.18%, and 98.58%. MLP achieved the best computational time across all datasets (DS-1, DS-2, and DS-3) with respective values of 2, 7, and 3 seconds despite scoring the lowest accuracy across all datasets.

Unscented Kalman Filter with Joint Decision Scheme for Phase Estimation in Probabilistically Shaped QAM Systems

A carrier phase estimation method based on the unscented Kalman filter (UKF) is proposed for probabilistically shaped (PS) quadrature amplitude modulation (QAM) systems. We further integrate a joint decision scheme into the proposed UKF−based algorithm to prevent the correlated erroneous decisions in the phase recovery scheme caused by the impact of PS. The proposed method achieves the performance benefit for PS constellations in optical transmissions by partitioning the constellation symbols suitably and utilizing both the maximum a posterior probability (MAP) and maximum likelihood (ML) detection. The results of numerical simulation and experimental verification reveal that the proposed method performs better than the conventional CPR algorithms in PS systems.

Performance Analysis of Multiple-Antenna Ambient Backscatter Systems at Finite Blocklengths

This paper analyzes the maximal achievable rate for a given blocklength and maximal error probability over a multiple-antenna ambient backscatter channel. The result consists of a finite blocklength channel coding achievability bound and a converse bound for the legacy system with finite alphabet constraints and multiple-input-multiple-output based on the Neyman-Pearson test, the Berry-Esseen theorem, and the Mellin transform. Then, we derive the closed-form expression of the mutual information and the information variance to reduce the complexity of the computation. By applying the low-complexity ML detection, the relation between the maximal error probability of the RF source signal and the average error probability of the tag symbol with respect to the blocklength is proposed. Finally, numerical evaluation of these bounds shows fast convergence to the maximal achievable rate as the blocklength increases and also proves that the information variance is an accurate measure of the backoff from the maximal achievable rate due to finite blocklength.

Probabilistic Constellation Shaping for Molecular Communications

Molecular communication (MC) is an emerging field aiming at realizing information exchange via chemical signals between nanomachines in nanonetworks. Information transmission that is energy-efficient and relies on relatively low-complexity transceiver techniques is of practical importance for MC systems. Based on the fact that constellation shaping can improve energy efficiency, in this paper, we propose a molecular shell mapping (MSM) scheme to implement the probabilistic constellation shaping for MC. The MSM method is designed to exploit the concentration sequences with the lowest sum sequence weight, which results in an energy-efficient signal constellation. Furthermore, we propose an algorithm for selecting and sorting the concentration sequences to mitigate inter-symbol interference. For information detection, we design a genie-aided maximum-likelihood (ML) detector and a realistic ML detector to leverage the constructive effect of intra-sequence interference, as well as derive their bit error rates and achievable rates. Additionally, for the applications using large blocklength sequences, a low-complexity ML detection method is proposed. Numerical simulation results confirm that the shaped signaling using the MSM method is more energy-efficient than the conventional equiprobable signaling, achieving shaping gains of up to 1.5 dB at an ultra-short blocklength of 4.

RIS-Based Opposite Phase Index Modulation and Its DNN-Assisted Joint Sequence Detection

Reconfigurable intelligent surface (RIS), a key technology for future wireless communications, allows intelligent reflection of signals and information transmission. The RIS-based modulation is considered a prospective information transmission mechanism. In this paper, a RIS-based opposite phase index modulation system is proposed, in which RIS can enhance the received power of user signals while transmitting additional data bits for near-end internet of things devices via opposite phase reflection pattern index without any additional radio frequency chain. To reduce the computational complexity of receiver detection, a deep neural network-assisted joint sequence detection method is proposed, in which the reflection pattern index and the symbol index are jointly detected. In addition, we derive the theoretical upper bound performance of the proposed modulation scheme in terms of the average bit error rate (BER) with the maximum likelihood detection. The theoretical and simulation results show that the proposed scheme has better BER performance and lower detection complexity than existing RIS-based modulation schemes.

Code Index Modulation-Aided Spatial Media-Based Modulation System for Future Wireless Networks

Future wireless networks, increment in mobile users, and Internet-connected devices have brought many needs, such as high data rate, low transmission energy, high error performance, and low latency. Index modulation-based systems have been considered as one of the strong candidates for meeting these demands and needs. In this article, a novel wireless communication system that combines code index modulation (CIM)-aided spatial modulation (SM) and media-based modulation (MBM) techniques, briefly called CIM-SMBM, is proposed for future wireless networks. In the CIM-SMBM system, source information bits are carried in transmitted symbols in addition to three indices: active antenna index selected by SM, indices of different channel states that vary depending on the propagation pattern with MBM, and selected spreading code indices determined by CIM. In order to further reduce the complexity of optimal maximum-likelihood (ML) and despreading-based ML detectors for the CIM-SMBM scheme, an enhanced low-complexity detector that can achieve near-ML error performance is proposed. The average bit error rate (ABER), throughput, energy efficiency, and complexity analyses of the CIM-SMBM system are derived over Rayleigh fading channels for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> -quadrature amplitude modulation technique. It has been observed that the ABER and simulation results are quite close to each other.

Deployment of Energy-Efficient Aerial Communication Platforms With Low-Complexity Detection

Due to their flexibility, mobility and autonomy, unmanned aerial vehicles (UAVs) are considered as a potential candidate to operate as flying base stations to provide a specific geographical area with air-to-ground wireless communications services. Besides, the limitation of the on-board energy motivates the design of a more energy-efficient way to transmit information. Moreover, the deployment of multiple UAVs can affect the quality of experience (QoE) of users. In this paper, we propose a method that combines the concept of index modulation (IM) with the UAV communications systems, which we refer to as IM-UAV, to attain an improved energy-efficiency (EE). Furthermore, based on the proposed IM-UAV communication system, a gradient descent based UAV deployment scheme is designed to maximize the downlink average rate of the ground users (GUs) in the target area. On the other hand, the maximum likelihood (ML) detection for the IM-UAV requires a high computational complexity for detection at the GUs, while providing the best possible performance. Hence, we propose a low-complexity detection scheme that can separately detect the index symbols and data symbols to reduce the computational complexity at the receiver side. The simulation results demonstrate that the proposed deployment method is capable of attaining the appropriate positions to deploy the UAVs, while the EE is also improved by combining IM with UAV communication system. In addition, the proposed low-complexity detection scheme reduces the computation complexity by slightly sacrificing the bit error rate (BER) performance.

Design and Performance Analysis of the Dynamic Reduction of Intrinsic Interference Suppression and BER using QAM-based FBMC for MU-MIMO Communications

The present research work is focused on the study of co-channel interface with its minimization techniques without influencing its performance, in turn, which is desired to achieve the minimized complexity of Quadrature Amplitude Modulation (QAM)-based Filter Bank Multi-Carrier (FBMC) to minimize the interference and increase the spectral features with consideration of intrinsic features extractions for the ML (Maximum Likelihood) synthesis systems. The valid measures are given various concerns under consideration, to start with the consideration of the evaluation of the Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFD performance metrics along with the FBMC/QAM in signal transmission in a dedicated fading channel for the evaluation of the modulation order and BER as a required trade-off for quality assessments. From the results, it can be noted that the proposed FBMC QAM has performed better when compared with conventional FBMC systems. The present research also includes considering and calculating the efficiency of nonlinear channels with the Multi-User Multiple Input Multiple Output (MU-MIMO) and FBMC/QAM techniques. In continuation, the obtained results are dominating significantly to access the possible solution to meet the efficiency of the proposed system. In the next part of the research, it is considered with implementation of the sub-detector during the downlink of the system with the technique of threshold-driven strategy for better accuracy and minimization of the complexity in terms of ML detection in terms of order of its modulation. The calculations of the proposed technique with better BER are done on the recent MATLAB platform with its simulation demonstration for its detailed observation.

Prefiltering-based reduced rank multiuser detection in uplink generalized spatial modulation massive MIMO system

Exploiting generalized spatial modulation in massive MIMO system, we develop an efficient uplink multiuser detection scheme. Different from the maximum likelihood detection algorithm, the spatial and temporal data in the proposed detection scheme are decoded in a sequential manner: At the front end of base station receiver, a bank of spatial filters is first employed to mitigate the multiuser interference using the benefits of large-scale antenna array. There then, a reduced rank generalized Eigen-value decomposition-based algorithm is proposed to identify the antennas activated at each user terminal. In the second stage, a least-squares-based multiuser detector is employed to decode the multiplexed temporal symbols. Both theoretical analysis and computer simulations are conducted to evaluate system performance in terms of correct identification probability and overall symbol error rate. Simulation results demonstrate that the proposed algorithm outperforms most of the existing algorithms yet the complexity is extensively reduced.

On the BER Performance of Constant Envelope OFDM in Frequency Selective MIMO Channels With ML Detection

In this contribution, we investigate the issue of constant envelope orthogonal frequency-division multiplexing (CE-OFDM) transmission in multipath fading channels, by quantifying its bit-error rate (BER) performance with the aid of maximum likelihood (ML) detection in multiple-input multipleoutput (MIMO) configurations. Through theoretical derivation, we demonstrate the advantage of CE-OFDM over its traditional counterparts as original OFDM and discrete Fourier transformspread-OFDM (DFT-s-OFDM) in terms of BER performance, which is ultimately supported by the simulation results.

Interference Mitigation-Enabled Signal Detection in Diffusive Molecular Communications Systems With Molecular-Type Spreading

To accomplish complex tasks, several nano-machines may need to communicate via multiple-access channel with one access point, where information fusion is carried out. However, multiple-access Diffusive Molecular Communications (DMC) systems suffer from severe Multiple-Access Interference (MAI) and Inter-Symbol Interference (ISI), which should be effectively mitigated at receiver in order to achieve acceptable performance. Built on two fundamental single-user detection schemes, namely Threshold assisted Majority Vote Detection (TMVD) and Equal Gain Combination Detection (EGCD), we first propose three low-complexity interference cancellation schemes, which are the TMVD-assisted Iterative Interference Cancellation (TMVD-IIC), TMVD-based Minimum-Distance Decoding assisted Interference Cancellation (TMVD-MDDIC) and the EGCD-assisted N-order Iterative Interference Cancellation (EGCD-NIIC), for operation in the Molecular Type Spread assisted Molecular Shift Keying (MTS-MoSK) DMC systems. Then, following the principle of maximum likelihood detection, we propose a Simplified Approximate Maximum Likelihood (SAML) detection scheme. The error performance of the MTS-MoSK DMC systems employing respectively the considered detection schemes is comprehensively investigated and compared. Furthermore, the complexities of the detection schemes are analyzed and discussed in terms of the complexity-performance trade-off. Our studies and results show that, compared with the single-user TMVD and EGCD schemes, the proposed interference cancellation schemes are capable of mitigating efficiently the effect of MAI and enabling significant performance improvement at the slightly increased complexity.