Maximum Likelihood Detection Research Articles

Quantum-Based Maximum Likelihood Detection in MIMO-NOMA Systems for 6G Networks

As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.

Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention

With the advent of advanced technological developments such as IoT, edge, and fog computing, cyber attacks have become increasingly sophisticated. IoT networks facilitate collaborative and intelligent tasks across various domains, including Industry 4.0, digital healthcare, and home automation. However, the proliferation of IoT devices has raised concerns about severe attacks, particularly those targeting resource constraints such as energy and memory. In response to these challenges, Tiny Machine Learning (TinyML) has emerged as a new research area, focusing on machine learning techniques tailored for embedded and IoT systems. This study proposes an ML detection mechanism designed to categorize and detect resource-constrained attacks in IoT devices. We consider IoT devices to be integral components within the continuum of edge and cloud computing, leveraging EdgeML and CloudML for detection purposes. Our paper conducts a comparative analysis of ML models, with a specific focus on energy consumption and memory usage in IoT applications. We compare various ML methodologies, including cloud-based, edge-based, and device-based strategies for both training and detection. The evaluation encompasses the application of these ML techniques to petite IoT devices, utilizing TinyML, as well as cloud and edge devices. Our findings reveal that the Decision Tree algorithm deployed on smart devices surpasses other approaches in terms of training efficiency, resource utilization, and the ability to detect resource-constrained attacks on IoT devices. We demonstrate a high level of accuracy, exceeding 96.9%, across all presented ML models in detecting resource constraint attacks within IoT systems. In summary, this research serves as a guide for implementing effective security measures in the dynamic landscape of IoT and associated technologies.

An advanced symbol detection approach for MIMO-FBMC/OQAM scheme based on migrating birds optimization algorithm

Application of multiple-input multiple-output (MIMO) technology to the filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) system provides great improvements on its robustness to channel fading effects. Nevertheless, the unique qualities of MIMO-FBMC/OQAM scheme do not make any sense without solving the symbol detection problem on its receiver part. To this end, an efficient symbol detecting strategy having the capability of recovering the transmitted symbol sequences with the highest accuracy is needed for the related system. Maximum likelihood (ML) detector is known with its excellent symbol detection performance in the literature. However, due to the usage of exhaustive search procedure, the computational complexity of the ML detector reaches extremely high levels with the increase of antenna size and modulation order. On the other hand, in the case that the exhaustive search procedure is substituted with the symbol optimization process, it becomes possible to achieve a large amount of complexity reduction in the conventional ML scheme without compromising too much on its symbol detection performance. In order to carry out an efficient symbol optimization in discrete space, we propose migrating birds optimization (MBO) algorithm based on cyclic bit flipping procedure in this paper. By virtue of employing the proposed MBO algorithm reinforced by the cyclic bit flipping mechanism for optimizing the symbol sequences, the computational complexity of the conventional ML is reduced to quite low levels. The percentages of complexity reduction achieved by the proposed scheme over the classical ML for 4 × 4, 6 × 6 and 8 × 8 MIMO configurations are equal to 29.688%, 87.188% and 98.299%, respectively. In addition to these huge complexity gains, an efficient symbol detection performance quite near to that of conventional ML is obtained thanks to the usage of aforementioned MBO-based symbol optimization procedure.

On the impact of interlayer misalignment for dual-layer data detection in three dimensional magnetic recording

Three-dimensional magnetic recording (3DMR) is a crucial technology for significantly increasing the storage capacity of hard disk drives (HDDs). However, the presence of inter-symbol interference (ISI), intertrack interference (ITI), and interlayer interference (ILI) poses significant challenges to the accurate detection of data stored on multiple layers. This study addresses the impact of interlayer misalignment on the bit error rate (BER) performance in 3DMR systems. We evaluate the performance based on a neural network estimator for reconstructing the top-layer read response signal using feedback from a Viterbi detector. This enables the separation of bottom-layer signals by subtraction from the mixed readback signal. We introduce a dual-layer partial response maximum likelihood (PRML) detector for simultaneous bit retrieval from both layers. Furthermore, we investigate methods of a per-layer binary classifier and a dual-layer four-class classifier based on neural networks. Our study demonstrates the BER performance of these detection schemes influenced by the interlayer misalignment, especially when the offset is 0, 10%, 50%, and 90% of the bit dimensions between the two layers. The results show that the neural network-based reconstruction and separation method achieves better bottom-layer BER performance under a slight interlayer misalignment. The BER performance benefits more from a mild downtrack offset than the crosstrack offset. The neural network-based separation detection and the dual-layer PRML achieve the lowest top-layer BER and the worst bottom-layer BER when the interlayer misalignment is half a bit.

4 × 4 differential index modulation for optical orthogonal frequency division multiplexing.

In this Letter, we propose and demonstrate a 4 × 4 differential index modulation (DIM) scheme for optical orthogonal frequency division multiplexing (OOFDM) systems. The 4 × 4 DIM scheme avoids complex channel estimation by performing differential index modulation in the time-frequency domain, with the key aspect lying in the design of the time-frequency dispersion matrix that integrates the indices and constellation symbols. Moreover, we design a deep learning-based DIMFormer detector for the high decoding complexity problem of maximum likelihood (ML) detection. Experimental results show that the 4 × 4 DIM in OOFDM systems eliminates the need for complex channel estimation, and the loss of signal-to-noise ratio (SNR) is no more than 1 dB compared to conventional index modulation. The designed DIMFormer detector reduces the computational complexity by 38.98% as well as the time complexity by 99% compared to ML.

Maximum likelihood inference for a class of discrete-time Markov switching time series models with multiple delays

Autoregressive Markov switching (ARMS) time series models are used to represent real-world signals whose dynamics may change over time. They have found application in many areas of the natural and social sciences, as well as in engineering. In general, inference in this kind of systems involves two problems: (a) detecting the number of distinct dynamical models that the signal may adopt and (b) estimating any unknown parameters in these models. In this paper, we introduce a new class of nonlinear ARMS time series models with delays that includes, among others, many systems resulting from the discretisation of stochastic delay differential equations (DDEs). Remarkably, this class includes cases in which the discretisation time grid is not necessarily aligned with the delays of the DDE, resulting in discrete-time ARMS models with real (non-integer) delays. The incorporation of real, possibly long, delays is a key departure compared to typical ARMS models in the literature. We describe methods for the maximum likelihood detection of the number of dynamical modes and the estimation of unknown parameters (including the possibly non-integer delays) and illustrate their application with a nonlinear ARMS model of El Niño–southern oscillation (ENSO) phenomenon.

Orthogonal waveform-based backscattering interference suppression technique for underwater optical wireless communication

The backscattering effects degrade the detection performance in a full-duplex underwater optical wireless communication (UOWC) system when the power of received backscattering interference is comparable to that of a target signal. In this paper, the orthogonal waveforms with the Hermite–Gaussian function and prolate spheroidal wave function are first introduced for a pulse position modulation based dynamic UOWC system to alleviate the backscattering effects. Then, a joint maximum likelihood detection scheme is proposed accordingly. Experimental results verify the superiority of the proposed method with orthogonal waveforms over the scheme without orthogonal pulse shaping in a dynamic underwater bubbly channel.

Noise modelling and mitigation for broadband in‐door power line communication systems

AbstractCommunication systems are greatly hampered by many disruptive noises in powerline communication systems (PLC), which come with strong interference, resulting in the malfunction of PLC systems. Hence, there is a need to model noise and its effect on communication systems. This paper presents noise modelling and mitigation techniques for indoor broadband powerline communication systems. To model the PLC noise, frequency domain measurements employing the GSP‐930 spectrum analyser were carried out to determine the noise frequency response in the frequency range of 1–30 MHz. The results obtained were plotted. While the analytical model for the noise model is presented, furthermore, noise mitigation techniques for multiple input multiple output PLC (MIMO‐PLC) systems in the form of spatial modulation PLC systems have been proposed. The SM‐PLC system employs the indices of the individual transmit lines to increase the data rate, as opposed to the traditional MIMO‐PLC systems, where the symbol to be transmitted is transmitted by duplicating the symbol across all lines. The proposed system uses the maximum likelihood (ML) detector at the receiver to obtain estimates of the transmitted symbols. The simulation results of the SM‐PLC system are compared with the already existing MIMO‐PLC system and show a significant improvement of ≈6 dB and 5.2 dB in signal‐to‐noise ratio (SNR) at a bit error rate of 10(−5) for spectral efficiencies of 4 bits per channel use (bpcu) and 6 bpcu, respectively. On comparison of the SM‐PLC system having a combination of additive white Gaussian noise and impulse noise at the receiver, the SM‐PLC system outperformed the traditional MIMO‐PLC by 3.5 and 3.8 dB in SNR for 4 and 6 bpcu, respectively.

Error analysis of OFDM‐IM systems for beyond 5G: The effect of IQI at transceiver

SummaryIt is well known that hardware impairments (HWIs) can worthy reduce the wireless system performance at high carrier frequencies by showing random effects. Most current researches for 5 GB systems assume that transmitters and receivers (transceivers) are perfectly equipped. But wireless transceivers ( ) are affected by HWIs in practice. Considering the previous studies in the literature, it is reported that HWIs have devastating effects on the performance of OFDM and OFDM‐index modulation (IM) systems with fading channels. In this paper, in‐phase and quadrature phase imbalance (IQI), which is the one of most HWIs between transmitter and receiver in wireless communication systems, is examined on OFDM‐IM system over Rayleigh and Nakagami‐ fading channels. Two well‐known detectors, the maximum likelihood (ML) detector and the log‐likelihood ratio (LLR) detector are used under the effect of the IQI at . Error performance analyzes over fading channels of the IQI effect on OFDM‐IM system are realized first theoretically and then by computer simulations. Results obtained for the presence of IQI at show that a performance evaluation based only on the presence of IQI in the receiver would be optimistic and misleading in terms of the performance of real‐life OFDM‐IM systems.

Performance enhancement of multiple-mode FBMC-IM VLC systems with group-interleaved precoding

In this paper, we introduce the application of multiple-mode index modulation (MMIM) to filter bank multi-carrier (FBMC) for the first time in visible light communication (VLC) systems. Additionally, we propose a group-interleaved precoding (GIP) technique to enhance the performance of MM-FBMC-IM-based VLC systems. The GIP technique reduces complexity in precoding by grouping and achieves equalization of the signal-to-noise ratio (SNR) through subcarrier interleaving. Furthermore, we develop a robust low-complexity maximum likelihood (LCML) detector, which can maintain the same computational complexity as a conventional LCML detector and achieve similar performance as an ML detector. The effectiveness and superiority of the proposed MM-FBMC-IM-based VLC system with GIP are demonstrated through comprehensive validation by both simulation and experimental results.

Symbol detection based on a novel discrete harmony search algorithm in MIMO-FBMC/OQAM system

Due to its high spectral efficiency and various other advantages, filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) has long been considered as a candidate waveform for the fifth generation (5G) and beyond telecommunication technologies. On the other hand, it is possible to both increase the data rate and alleviate the channel fading effects by using the multiple-input multiple-output (MIMO) antenna structure in the FBMC/OQAM transceiver. However, since the symbol detection is an indispensable task to be fulfilled in wireless communication, it is crucial to employ an efficient symbol detector at the MIMO-FBMC/OQAM receiver. Maximum likelihood (ML) detector, which always finds the optimal symbols by trying all of the possible symbol combinations likely to be transmitted, is known for its extremely high computational complexity making it impractical to be used in any system. On the other hand, it is possible to both considerably reduce the ML complexity and achieve the near-ML performance by optimizing the symbol vectors instead of implementing an exhaustive search. Since searching for the optimal symbol combination in discrete space is a combinatorial optimization problem, we developed a novel discrete harmony search (disHS) algorithm to perform this operation. According to the simulation results, the newly developed disHS algorithm not only achieves near-ML performance with lower computational complexity, but also clearly leaves behind the other symbol detectors considered in this paper.

Decoding scheme based on CNN for differential free space optical communication system

A high-speed and robust decoding algorithm based on Convolutional Neural Networks (CNN) is proposed for differential free space optical communication system. The system adopts bipolar complementary pulse width modulation (BCPWM) that can enhance the amplitude of the signal and avoid interference from common mode signals in a single channel system. In addition, BCPWM has amplitude and envelope characteristics. The deep learning-based detector proposed by the signal does not require channel state information. It directly feeds the received signal into a deep neural network, and CNN can effectively extract amplitude and envelope features from the signal. The experimental comparison was conducted with bipolar non-return to zero (BNRZ) signals with only amplitude features to verify the demodulation ability of CNN for signals with different features. With a bit error rate of 10−5, the required signal-to-noise ratio for BNRZ is 13 dB, and the required signal-to-noise ratio for BCPWM is 8 dB. The demodulation performance of CNN, maximum likelihood (ML), and Volterra equalizer detector were compared. For BCPWM signals, compared to ML and Volterra equalizer detectors, the proposed CNN-based detector reduces the required signal-to-noise ratio by 2 dB and 8 dB at a bit error rate of 10−5, respectively.

Detection and performance analysis for MIMO visible light communication system using joint optical spatial and pulse amplitude width modulation

Conventional optical spatial modulation (SM) scheme activates one of the light-emitting diodes (LEDs) to transmit an intensity-modulated optical signal, in which the index of the activated LED is determined by spatial symbol and the emitted intensity is controlled by temporal symbol. In order to enhance the spectral efficiency (bits per channel use), we propose a joint SM and pulse amplitude width modulation (PAWM) as a novel optical spatial–temporal signaling scheme. In this paper, the proposed SM-PAWM optical signaling scheme is applied in a multi-input multi-output (MIMO) visible light communication (VLC) system. Employing optimal maximum likelihood (ML) algorithm to extract the spatial and temporal symbols is computationally prohibitive; hence, we develop a novel low-complexity detection scheme that converts the joint optimization problem separately to decode the spatial and temporal symbols. Moreover, theoretical results in terms of the successful identification probability of activated LED as well as the overall symbol error rate are derived. Extensive computer simulations are performed to validate the analytical results. It is shown that the proposed detection scheme is a feasible alternative to the ML detector in the VLC-MIMO system employing SM-PAWM.

Bi-orthogonality recovery and MIMO transmission for FBMC systems based on non-sinusoidal orthogonal transformation

Filter Bank Multi-Carrier (FBMC) system based on offset Quadrature Amplitude Modulation (offset-QAM) combined with Multiple-Input-Multiple-Output (MIMO) technique faces great challenges. The inherent imaginary interference of FBMC seriously impacts the performance of the Maximum Likelihood (ML) detection technique in MIMO transmission. The application of the Alamouti code is also hindered. In this paper, we present a data-compressed transmission method based on non-sinusoidal orthogonal transformation, which can recover the bi-orthogonality of FBMC and eliminate the imaginary interference. Thereby, the combination of FBMC and MIMO as well as the application of ML technique and Alamouti code are realized. Specifically, we first consider the Walsh transform, which belongs to the non-sinusoidal orthogonal transform, and utilize its property that can reduce the transmission bandwidth to recover the orthogonality of the FBMC in the frequency domain. Secondly, we construct the discrete transmission model of the system and generate the orthogonal compression matrix utilizing the fast Walsh transform. Then, we analyze the multi-frame transmission, calculate the frames interference, and improve the signal-to-interference ratio again by adding guard time slots. Finally, we construct the MIMO transmission model and complete the theoretical analysis. Simulation results show that the proposed scheme has a robust MIMO transmission performance. ML and Alamouti code techniques perform the same as Orthogonal Frequency Division Multiplex in bi-orthogonal FBMC.

A Simple Design of Reconfigurable Intelligent Surface-Assisted Index Modulation: Generalized Reflected Phase Modulation

As a potential member of next generation wireless communications, the reconfigurable intelligent surface (RIS) can control the reflected elements to adjust the phase of the transmitted signal with less energy consumption. A novel RIS-assisted index modulation scheme is proposed in this paper, which is named the generalized reflected phase modulation (GRPM). In the GRPM, the transmitted bits are mapped into the reflected phase combination which is conveyed through the reflected elements on the RIS, and detected by the maximum likelihood (ML) detector. The performance analysis of the GRPM with the ML detector is presented, in which the closed form expression of pairwise error probability is derived. The simulation results show the bit error rate (BER) performance of GRPM by comparing with various RIS-assisted index modulation schemes in the conditions of various spectral efficiency and number of antennas.

Enhancing the Accuracy of 6T SRAM-Based In-Memory Architecture via Maximum Likelihood Detection

Enhancing the Accuracy of 6T SRAM-Based In-Memory Architecture via Maximum Likelihood Detection

Maximum-Likelihood Detection with QAOA for Massive MIMO and Sherrington-Kirkpatrick Model with Local Field at Infinite Size

Maximum-Likelihood Detection with QAOA for Massive MIMO and Sherrington-Kirkpatrick Model with Local Field at Infinite Size

Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems: Dithering-Aided Adaptive Approach

Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems: Dithering-Aided Adaptive Approach

Bağlantı Seviyesi Simülasyon Üzerinde LLRnet'in 5G-NR'ye Uygulanması

Modern receivers apply smooth demodulation and demapping processes to received symbols, using bit log-likelihood ratios (LLRs). Known as “LLRnet" demodulator architecture is offered that is a global educatable neural network attributed in this paper. The calculation of the optimal LLR algorithm includes the calculation of each bit in the LLR value and requires the assessment of whole lattice points as high dimensional which is unpractical for the QAM modulation. Known the most in the literature the Maximum Likelihood (ML) detector shows very high computational complexity that is used in the QAM scheme. Besides LLRnet developed achievement importantly, all calculational complexity is also reduced. Via estimating exact log-likelihood, how to create symbols, and channel corruptions for training a neural network LLRNet is shown in this paper. New and contemporary radio communication systems, such as 5G- NR (New Radio) and DVB (Digital Video Broadcasting) for satellite, DVB (S.2 Second Generation) utilize the LLR approach that calculates soft bit values with FEC (Forward Error Correction) algorithms and utilizes demodulated smooth bit values. This article aims a link-level simulation study to implement of LLRnet to DVB S2 and 5G-NR. The motivation of this study is seen that performing machine learning techniques on physical layer scheme, makes LLRNet a powerful example for practicability. This paper offers to compare Max-Log Approximate LLR, Exact LLR, and LLRNet methods for 16, 32 and 128 QAM.

Space–time labeling diversity for distributed Goppa-coded cooperative networks over Nakagami-[formula omitted] channels

This paper proposes the novel space–time labeling diversity for the distributed Goppa-coded cooperation (STLD-DGCC) scheme over the Nakagami-q fading channel to improve the performance of the short-to-medium information block transmission. Two different Goppa codes with the same information length are deployed in the source and the relay, respectively, with the Goppa code at the relay providing additional redundancy. Meanwhile, two distinct labeling mappers of M-ary quadrature amplitude modulation (M-QAM) are used in the novel space–time labeling diversity (STLD) to achieve the extra diversity gain and decrease the error floor (EF) region in the Goppa coding scheme. To lower the computational complexity of the maximum likelihood (ML) detection, a near-ML reduced-complexity (RC) detection approach that compromises the system performance and the computation complexity is proposed. In addition, two novel joint decoding algorithms based on the critical signal-to-noise ratio (SNR) threshold are presented at the destination, namely the parallel algorithm and serial algorithm, to enhance the overall performance of the proposed STLD-DGCC scheme. In the case of the Nakagami-q fading model, the analytical union bound of the average bit error probability is provided, which tightly matches the Monte Carlo simulated results at high SNR. The simulation results further indicate that the proposed STLD-DGCC scheme significantly outperforms both the corresponding non-cooperative Goppa coding scheme and the DGCC scheme without the STLD technique under identical conditions. Furthermore, the performance of the STLD-DGCC scheme surpasses that of the existing transmission scheme, offering a promising candidate for further small information block communications.