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Compact ultra massive array (CUMA) is a new form of the emerging fluid antenna system where a huge number of flexible‐position antennas are selected to produce the output signal. By making sure that the in‐phase channels (similarly for the quadrature channels) of the desired signal at the selected antenna ports align, it builds an advantage of the desired signal over the interference. It is known that CUMA as a multiple access scheme is able to deal with hundreds of users on the same channel use, in the case of rich scattering, if binary phase shift keying is considered. It is nevertheless unclear if higher‐level modulation can bring even greater network rate in this extreme massive connectivity scenario. This letter investigated this situation by presenting the average data rate expression of CUMA when M‐ary amplitude shift keying is used, assuming a binary symmetric channel. Numerical results reveal that M‐ary amplitude shift keying can indeed raise the rate performance considerably. This letter studies the network rate performance of a new concept, termed CUMA, for massive connectivity. In particular, it is investigated if a higher‐order M‐ary amplitude shift keying can boost the capacity performance.

The emergence of terahertz (THz) communication in ultra‐massive multiple‐input multiple‐output (UM‐MIMO) systems presents new challenges for accurate and efficient channel estimation, particularly under hybrid‐field propagation conditions. Conventional estimation techniques struggle to meet the demands of such high‐dimensional systems, especially in the presence of limited radio frequency (RF) chains and mixed near‐ and far‐field effects. To address these limitations, this paper proposes a deep learning‐based framework that combines a fully connected neural network (FCNN) for linear channel estimation with a convolutional neural network (CNN) for non‐linear refinement. The architecture is designed to adapt to diverse propagation environments while maintaining computational efficiency. Simulation studies based on realistic THz scenarios demonstrate that the proposed approach significantly improves estimation accuracy, achieving up to 90% reduction in normalized mean squared error (NMSE) compared to traditional and advanced estimation techniques. The robustness of the model under varying signal‐to‐noise ratios and noise power levels underscores its potential for deployment in future 6G THz communication networks. This paper introduces a deep learning‐based framework for channel estimation in terahertz (THz) ultra‐massive multiple‐input multiple‐output systems, combining fully connected neural network for linear estimation and connected neural network for non‐linear refinement. The proposed method significantly reduces normalized mean squared error by up to 90% compared to traditional techniques, showcasing its potential for 6G THz communication. Simulation results confirm its robustness across varying signal‐to‐noise ratios.

Channel estimation is a critical component of modern wireless communication systems, especially in massive multiple‐input multiple‐output (MIMO) architectures, where the accuracy of received signal decoding heavily depends on the quality of channel state information. As wireless networks evolve into fifth‐generation (5G) and beyond, they face increasingly complex propagation environments with rapid mobility, dense connectivity, and hardware constraints. Accurate and timely channel estimation is therefore essential for maintaining system performance, enabling reliable data transmission, and supporting techniques such as beamforming and interference management. Traditional estimation methods like least squares and minimum mean square error offer baseline performance but are often limited by their computational complexity, sensitivity to noise, and inefficiency in quantised systems—particularly those employing one‐bit analogue‐to‐digital converters. These limitations hinder their applicability in real‐time, low‐power, and bandwidth‐constrained scenarios. To address these challenges, this paper proposes a novel channel estimation framework based on conditional generative adversarial networks. The approach incorporates a U‐Net‐based generator and a sequential convolutional neural network discriminator to learn complex channel mappings from highly quantised received signals. Unlike existing methods, the proposed architecture dynamically adapts to various noise levels and system configurations, offering improved robustness and generalisation. Comprehensive experiments conducted on realistic indoor massive MIMO datasets demonstrate that the proposed method achieves substantial performance gains. The model improves estimation accuracy from 93% to 95.5% and significantly enhances normalised mean square error, consistently outperforming conventional and deep learning‐based techniques across diverse training conditions. These results confirm the effectiveness of the proposed scheme in delivering high‐accuracy channel estimation under extreme quantisation conditions, making it suitable for next‐generation wireless systems. This paper proposes a novel channel estimation framework for massive multiple‐input multiple‐output (MIMO) systems using conditional generative adversarial networks with a U‐Net‐based generator and sequential convolutional neural network discriminator. The method improves accuracy and robustness under severe quantisation conditions, outperforming traditional and deep learning‐based techniques, achieving significant performance gains in terms of normalised mean square error and estimation accuracy. Experiments on realistic massive MIMO datasets demonstrate its suitability for next‐generation wireless communication systems.

Intrinsic degrees of freedom (IDoF) which are the measure about the relative transmission ability of a channel from a transmitter to a receiver in the current transmit power at finite signal-to-noise power ratio are proposed. According to the IDoF of the subchannel in every dimension of channel space, the number of spatial dimensions allocated to the interferer by the interference avoiding technique, which is no more than the maximum number of subchannels associated with the decreasing IDoF of the transmission channel due to interference in the multiuser multi-antennas interference channel, is given from an information-theory perspective.

For cooperative spectrum sensing, multiple secondary users are required to deliver their local decisions to a fusion centre through a report channel. A resource-efficient transmission technique for the report channel is presented, which can significantly reduce the time or frequency resource needed for transmission by simultaneously transmitting the local decisions.

In this paper a novel optimization technique i.e. Social Impact based Wireless Communication Algorithm (SIWCA) has been applied on multi-carrier code division multiple access (MC-CDMA) communication systems to mitigate multiple access interference (MAI). MC-CDMA is being researched as an alternate technology for fourth generation (4G) as well as fifth generation (5G) mobile systems. MAI has been a major concern for the CDMA based systems. MAI increases the bit error rate in a MC-CDMA system, which in turn degrades the system performance. The SIWCA is based on the social impact theory of human behavior in the society. The proposed approach combines the social sciences with the communication technology. The simulation results show that SIWCA based MC-CDMA detector is capable of significantly reducing the MAI and gives a near-optimum performance. Further SIWCA is compared with popular optimization techniques like genetic algorithm (GA) and binary particle swarm optimization (PSO) for different parameters. Simulation results show that SIWCA converges fast and works with lesser number of control parameters as compared to GA and PSO.

Multiuser multiple‐input–multiple‐output (MIMO) has great potential to substantially improve throughput of wireless networks. Unfortunately, it requires a significant amount of feed back for user selection, which prevents practical implementation. The authors propose a feed back reduced downlink multiuser MIMO system, where a signal‐to‐interference‐plus‐noise ratio (SINR) threshold is carefully designed. Only the users whose SINRs are above the threshold feed back their SINRs and channel direction information (CDI) to the base station. They establish a monotonic relation between the threshold and the average feed back overhead. Based on the established relation, they design the threshold that balances the tradeoff between the feed back load and the sum rate. Simulation results show that the proposed approach can achieve the same performance as the conventional full‐feed back scheme (where every user feeds back its SINR and CDI), while the proposed approach is able to reduce the system overhead by more than 40%.

The performance of a dual-hop system is studied and evaluated, where m single-antenna source nodes communicate with a destination equipped with n ≥ m antennas via m single-antenna relays. The decode-and-forward protocol is implemented at the relays whereas the classical ordered V-BLAST scheme is performed at the destination. The analysis involves spatially independent non-identically distributed Rayleigh channel fading channels, reflecting on distinct average received powers among the different source and/or relay nodes with the destination, suitable for practical applications. The identically distributed scenario is also included as a special case. Novel union closed-form system bound expressions are derived in terms of the outage probability and the average symbol error probability. These bounds correspond to the cases of both the perfect and the imperfect channel state information at the relay and the destination nodes. The numerical results, accompanied with the equivalent simulation ones, reveal the accuracy of the proposed approach.

This study focuses on the design of downlink transmission protocols in multi-cell multi-user mobile networks, where co-channel interference has been recognised as a challenging issue particularly for the users close to the boundary of cells. The key idea of this study is to jointly apply interference alignment (IA) and pre-cancellation to the addressed scenario, where the former technique can effectively increase the overall system throughput and the latter can significantly boost the diversity gains and reception reliability. To ensure the diversity gains can be achieved with zero-forcing IA, a precoder optimisation scheme is proposed based on the well-known iterative interference leakage minimisation scheme. Both analytic and numerical results have been developed to show the capacity and diversity gains obtained by using the proposed scheme. Besides, the computational complexity of the proposed scheme and the effects of imperfect channel state information on the performance are studied as well.

Proposed is an adaptive fully transmitter-based block-diagonalisation scheme for multiple input multiple output (MIMO) multiuser downlink systems. A relaxation to the beamforming optimisation constraints is proposed, which introduces interference diversity and attains a more efficient performance optimisation. The trade-off to the performance improvement is an increase in the precoding complexity imposed by the adaptive nature of the proposed beamforming. A sub-optimal adaptive-decomposition beamforming scheme is also proposed with a reduced complexity overhead. Comparative analytical and simulation results to conventional beamforming demonstrate the significant diversity gains offered by the proposed scheme.