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A novel M-algorithm-based detection method called M-algorithm to maximum likelihood (M–ML) is proposed for spatial modulation. The proposed algorithm has fixed complexity and reduces complexity by using a parallel structure to limit the search space of an optimum decoder. The simulation results show that by carefully choosing proper values of M, the new algorithm can reduce the computational complexity significantly while maintaining a near-optimum bit error rate performance.

In this study, the authors explore a generalised scheme for the synchronous code division multiple access (CDMA). In this scheme, unlike the standard CDMA systems, each user has different codewords for communicating different messages. Two main problems are investigated. The first problem concerns whether uniquely detectable overloaded matrices (an injective matrix, i.e. the inputs and outputs are in one-to-one correspondence depending on the input alphabets) exist in the absence of additive noise, and if so, whether there are any practical optimum detectors for such input codewords. The second problem is about finding tight bounds for the sum channel capacity. In response to the first problem, the authors have constructed uniquely detectable matrices for the generalised scheme and the authors have developed practical maximum likelihood detection algorithms for such codes. In response to the second problem, lower bounds and conjectured upper bounds are derived. The results of this study are superior to other standard overloaded CDMA codes since the generalisation can support more users than the previous schemes.

The authors propose two soft forwarding schemes for unitary space-time modulation, namely estimate-and-forward (EF) and mutual information-based forwarding (MIF), which are ideally suited for rapid piecewise-constant fading scenarios with no channel state information both at transmitters and receivers. In the EF scheme, the relay regenerates signal via the conditional expectation of the source unitary space-time (UST) signal given the received matrix at the relay node instead of hard decisions. The MIF scheme employs the instantaneous mutual information between the source UST signal and received matrix at the relay node as a measurement for maximum-likelihood detection. The authors provide the relay forwarding functions, the normalisation factors, and the detection methods at the destination firstly. Above all, the generalised signal-to-noise ratio (SNR) at the destination for our unitary space-time modulated relaying system is analysed and simulated firstly. Simulation results reveal that the proposed EF and MIF schemes can outperform the existing amplify-and-forward (AF) and detect-and-forward (DF) schemes in terms of bit-error-rate performances in most scenarios. For a parallel relay network with two relays, the proposed EF scheme achieves 2 and 3 dB SNR gain over AF and DF schemes, respectively. Moreover, the MIF scheme can further give about 1.5 dB SNR gain.

Reducing SIC operations and interference while maintaining the same radio resources for users in a cluster is a potential research direction in non‐orthogonal multiple access (NOMA) systems. In order to reach this goal, a streamlined user grouping is proposed for a four‐user cluster in downlink NOMA systems, which is carried out based on the in‐phase and quadrature components of a constellation. In terms of bit error rate performance, the proposed scheme is superior in comparison with the conventional NOMA without user grouping and phase rotation‐based user grouping. This improvement is achieved in light of the three positive points of pulse amplitude modulation, the Gray mapping rule, and the joint maximum‐likelihood detector. We have come up with a streamlined user grouping scheme for a four‐user cluster in downlink NOMA systems. This proposed scheme is designed based on PAM modulation, the Gray mapping rule, and the joint maximum‐likelihood detector. The streamlined user grouping gives outstanding BER performance in comparison to the conventional NOMA without user grouping and phase rotation‐based user grouping.

Signal space cooperative (SSC) system is bandwidth and energy efficient, which can achieve full-rate and full-diversity simultaneously. In this study, the authors first derive accurate bit error rate (BER) of the SSC system with maximum-likelihood detection (MLD), low-complexity linear detection (LD) and combined MLD and LD (MLD&LD). Specifically, closed-form BER expressions for the three detection schemes are derived. Also, asymptotic approximation analysis is performed to show clearly the diversity and coding gains of the three detection schemes. Through the analysis, the authors find out that MLD is optimal in diversity but has a high complexity, whereas LD is of low complexity but only achieves diversity order one. Interestingly, MLD&LD has almost the same BER as the BER-optimal MLD over the whole SNR range when equal power allocation (EPA) is adopted, whereas having lower complexity than MLD at low-to-medium SNR. Then, optimum power allocation for MLD&LD is developed, however, the largest power gain over EPA is proven to be only 0.37 dB. In view of the BER performance of MLD&LD and implementation simplicity of EPA, they conclude that MLD&LD with EPA is a wise choice for the SSC system. Finally, outage probability of MLD&LD with EPA is analysed to characterise the transmission reliability.

In this study, the authors investigate the expected complexity of increasing radii algorithm (IRA) in an independent and identified distributed Rayleigh fading multiple-input–multiple-output channel with additive Gaussian noise and then present its upper bound result. IRA employs several radii to yield significant complexity reduction over sphere decoding, whereas performing a near-maximum-likelihood detection. In contrast to the previous expected complexity presented by Gowaikar and Hassibi (2007), where the radius schedule was hypothetically fixed for analytic convenience, a new analytical result is obtained by considering the usage of multiple radius schedules. The authors analysis reflects the effect of the random variation in the radius schedule and thus provides a more reliable complexity estimation. The numerical results support their arguments, and the analytical results show good agreement with the simulation results.

Space-time block coded spatial modulation (STBC–SM) has been proposed to exploit the advantages of both spatial modulation and space-time block codes. The first objective of this study is to introduce a very simple near-maximum-likelihood (ML) low-complexity detection scheme for Nt × Nr M-ary quadrature amplitude modulation (M-QAM) STBC–SM. Simulation results validate that the proposed simple detection scheme achieves near-ML detection error performance. Furthermore, in comparison to existing schemes, the computational complexity of the proposed detector was demonstrated to be independent of the modulation size, hence exhibiting a very low computational complexity. The second objective of this study is to present an asymptotic bound to quantify the average bit-error rate performance of M-QAM STBC–SM over independent and identically distributed Rayleigh flat fading channels. The analytical frameworks are validated by Monte Carlo simulations.

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes a neural network architecture to learn the nonlinear relationship between quantized received signals and transmitted symbols directly from data. It achieves near-optimum performance, within a worst-case 12% margin of the maximum likelihood detector that assumes perfect channel knowledge. We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems.

During the process of sending a signal across a transmission channel that has a broad bandwidth, the Multiple-Input Multiple-Output, or MIMO, system will frequently require a larger quantity of energy and power than it would under normal circumstances. This is because the transmission channel will have a greater capacity to carry more data. When there are so many different sets of signals that have been found, it is necessary to be able to differentiate between them using one of the many different methods that have been developed. One of the methods that is now one of the most extensively employed in the process of discovery is called maximum likelihood detection. This method is also frequently referred to as machine learning (ML) detection. This is as a result of the very high throughput that ML detection has. On the other hand, the exponential growth in ideal throughput is far smaller than the complexity of the framework and the amount of energy that it consumes. In order to better simplify communication between the transmitter and the receiver, the major goal of this endeavor is to locate the shortest viable routing route for the physical data transmission layer and use that information to design the routing path. Because of this, both the quantity of energy that is used and the level of complexity that the system has will both drop. Improved Iterative Based Dijkstra Algorithm (IIBDA) to Gauge the Most Limited Course of the Channel with a Restricted Maximum Likelihood-Detection (RMLD) Design was a method that was presented to solve the problem. This was done in order to address the concerns that had been raised and resolved in the prior discussion. This was done in an effort to discover answers to problems such as these. The Execution Examination illustrates how the Complexity and Control Utilization in the Proposed Strategy may be Decreased by showing how these factors might be Addressed. An FPGA Virtex-6 was used for putting the suggested plan into action in order to accomplish this. When the recommended IIBDA-RMLD were put through their paces in terms of power consumption, area, time delay, and complexity, all of these characteristics exhibited improvements of up to 95.4%, 84.23%, 84.21%, 87.23%,90.14% respectively. These percentages represent the maximum levels of improvement that were observed. MIMO Transmission System is able to achieve a significantly higher Signal to Noise Ratio (Snr) demonstrating with reduced power usage of 0.538mw and range optimization accomplished 11093.13 m for Quadrature Phase Shift Keying (QPSK) balancing with the ML Location System using Feed Forward Neural Network (FFNN). This is in addition to the fact that the MIMO Transmission System can reduce the amount of power it uses by 0.538mw. This is accomplished with a reduced quantity of usage of electrical power. In conclusion, RMLD was employed on MIMO networks in order to enhance the transmission of information in military and other applications by making it more transportable and safer. This was done for a variety of reasons. This action was taken for a number of different reasons.

The design of an optimal non‐orthogonal multiple access (NOMA) transmission scheme with finite alphabet inputs for a typical two‐user uplink wireless communication system is investigated in which each terminal is equipped with a single antenna. Each of the two users utilises a four quadrature amplitude modulation (4‐QAM) constellation to transmit information data to a common base station, and the receiver employs a maximum likelihood (ML) detector to jointly estimate both transmitted signals. Assuming the availability of channel state information at both the transmitters and the receiver, it is aimed to design a pair of scalar beamformers for the two users such that the minimum Euclidean distance between elements of the received sum‐constellation is maximised subject to the power constraints on the users. A thorough consideration of all the different conditions results in the derivation of a closed‐form optimal beamformer design. As well, examination of the construction of sum‐constellation resulted from the optimum design directly leads to the unique decoding of the original transmitted signal of each user. To facilitate practical implementation, a fast decoding procedure of the optimum NOMA scheme is further developed. The corresponding theoretic probability of ML detection error is also derived. The theoretical development of an optimum sum‐constellation for the basic 2‐user 4‐QAM system provides a solid platform for the derivation of an optimum sum‐constellation for a K‐user and/or M‐QAM system. Indeed, a simple development of the basic sum‐constellation map facilitates such extensions. Numerical simulations not only demonstrate that the performance of the fast decoder agrees closely with the theoretical analysis, but also verify that it is superior in performance to other existing NOMA designs for the same system under high signal‐to‐noise ratio. We design a pair of scalar beamformers for the two users such that the minimum Euclidean distance between elements of the sum‐constellation is maximised subject to the power constraints. The structure of the resulted sum‐constellation leads to the unique decoding of the transmitted signal of each user, and a fast decoding procedure is developed. Numerical simulations show the performance of our proposed scheme is superior to the other NOMA designs.