This work proposes a novel quantum-assisted partial sorting algorithm, called multi-minima Dürr–Høyer (MMDH), designed to reduce query complexity in scenarios where only a small subset of elements must be sorted. Empirical results show that MMDH significantly outperforms classical algorithms in these settings, achieving over an order of magnitude reduction in query complexity. The algorithm is applied to signal detection in multiple-input multiple-output systems and is particularly effective when integrated into a newly introduced variable-complexity sphere decoder, called progressive tree expansion (PTE), which inherently benefits from partial sorting. Compared to fixed-complexity sphere decoders (FCSDs), the PTE algorithm substantially reduces the computational complexity, especially at high signal-to-noise ratios (SNRs). When augmented with MMDH, the quantum-assisted PTE decoder achieves near maximum-likelihood error performance while mitigating the query overhead commonly associated with tree-based decoders. In contrast, conventional FCSDs benefit less from MMDH, as they require selection of multiple minima rather than partial ordering, a task where classical methods such as heap-based selection remain competitive. Although MMDH introduces a small failure probability, the resulting error floor stays below practical thresholds in high-SNR regimes.

This paper proposes a novel index modulation-based orthogonal time frequency space (IM-OTFS) framework for terahertz integrated sensing and communication (THz-ISAC) systems. The framework enhances bit error rate (BER) performance in high-mobility scenarios while improving spectral efficiency relative to existing embedded pilot-based ISAC methods by conveying information through both constellation symbols and index bits in the delay-Doppler (DD) domain. A new sensing and estimation method is developed, employing an argmax-based interior point optimization to estimate integer and fractional DD shifts with low computational complexity. A simple detection strategy combining least-squares residuals and maximum like-lihood power detection accurately recovers data and index bits. The algorithm’s efficiency is analyzed in terms of computational complexity and compared with existing OTFS THz-ISAC systems. Simulations show that the proposed framework achieves improved BER performance, low computational complexity, range root-mean-square error (RMSE) below 10−3m and a velocity RMSE on the order of 10−1m/s. It also allows flexible trade-offs among communication metrics such as peak-to-average power ratio (PAPR) and BER and achieves a PAPR reduction of 3–5 dB compared to prior methods, demonstrating its effectiveness and adaptability for diverse THz-ISAC deployment scenarios.

Intelligent radios play a pivotal role in optimizing communication resources for both commercial and military applications. Automatic signal identification (ASI) serves as a crucial component for intelligent radios, with likelihood-based and feature-based ASI algorithms being conventional approaches. Recent studies have explored the integration of machine learning (ML) algorithms for ASI, revealing their enhanced resilience to channel distortions compared to traditional methods. This article proposes the application of an extreme learning machine (ELM), a type of the ML algorithm, for the identification of cellular signals based on over-the-air measurements of power spectral density (PSD). The proposed ELM undergoes evaluation using two distinct datasets of PSDs to assess identification accuracy, with the first dataset utilized for hyperparameter optimization and the second unseen dataset employed to evaluate robustness and generality. The experimental results showcase improved performance in both accuracy and training complexity compared to recent work in the literature.

Integrated sensing and communication (ISAC) technology has been explored as a potential advancement for future wireless networks, striving to effectively use spectral resources for both communication and sensing. The integration of reconfigurable intelligent surfaces (RIS) with ISAC further enhances this capability by optimizing the propagation environment, thereby improving both the sensing accuracy and communication quality. Within this domain, accurate channel estimation is crucial to ensure a reliable deployment. Traditional deep learning (DL) approaches, while effective, can impose performance limitations in modeling the complex dynamics of wireless channels. This paper proposes a novel application of conditional generative adversarial networks (CGANs) to solve the channel estimation problem of an RIS-assisted ISAC system. The CGAN framework adversarially trains two DL networks, enabling the generator network to not only learn the mapping relationship from observed data to real channel conditions but also to improve its output based on the discriminator network feedback, thus effectively optimizing the training process and estimation accuracy. The numerical simulations demonstrate that the proposed CGAN-based method improves the estimation performance effectively compared to conventional DL techniques. The results highlight the CGAN’s potential to revolutionize channel estimation, paving the way for more accurate and reliable ISAC deployments.

This paper develops a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted symbiotic radio (SR) system, in which the STAR-RIS is deployed to transmit extra Internet of Things (IoT) data and simultaneously enhance the downlink transmission. A simple and efficient ON-OFF keying modulation scheme is applied by the STAR-RIS to modulate IoT data, which allows a low-complexity IoT transceiver and avoids the signal ambiguity. This work aims to maximize the weighted sum-rate (WSR) of downlink users, subject to the minimum received energy requirement of IoT transmission. Under the assumption of perfect channel state information (CSI), an efficient penalty dual decomposition (PDD)-based algorithm is proposed to solve the WSR maximization problem. By leveraging the PDD framework, the STAR-RIS’s coefficients are updated with close-form expressions. For the imperfect CSI case, the WSR maximization problem becomes a challenging stochastic optimization task. To address it, the constrained stochastic successive convex approximation framework is employed. Additionally, an efficient projection method is proposed to handle the STAR-RIS’s amplitude and coupled phase-shift constraints. Simulation results reveal the performance trade-off between the downlink transmission and the IoT transmission and validate the superiority of the proposed algorithms over the benchmarks.

This paper investigates a practical distributed reconfigurable intelligent surface (RIS)-assisted full-duplex wireless system. For the first time in the literature, the system resources minimization problem is considered by jointly optimizing the RIS phase shifts and their states (ON/OFF) subject to target sum rate constraints. The paper further considers a discrete phase shift model at the RISs. As the formulated problem is mixed-integer and non-convex, it is decoupled into two sub-problems: transmit beamforming and joint RIS phase shifts and RIS elements state optimization. The former problem is mathematically addressed using approximate solutions, while the latter problem is addressed using a novel reinforcement learning (RL) approach. Simulation results illustrate that the proposed RL algorithm is flexible for different target rate constraints. The results further show that the proposed framework efficiently saves a considerable number of reflecting elements by configuring their state.

Multi-user integrated sensing and communication (ISAC) assisted by intelligent reflecting surface (IRS) has been recently investigated to provide a high spectral and energy efficiency transmission. This paper proposes a practical channel estimation approach for the first time to an IRS-assisted multi-user ISAC system. The estimation problem in such a system is challenging since the sensing and communication (SAC) signals interfere with each other, and the passive IRS lacks signal processing ability. A two-stage approach is proposed to transfer the overall estimation problem into sub-ones, successively including the direct and reflected channels estimation. Based on this scheme, the ISAC base station (BS) estimates all the SAC channels associated with the target and uplink users, while each downlink user estimates the downlink communication channels individually. Considering a low-cost demand of the ISAC BS and downlink users, the proposed two-stage approach is realized by an efficient neural network (NN) framework that contains two different extreme learning machine (ELM) structures to estimate the above SAC channels. Moreover, two types of input-output pairs to train the ELMs are carefully devised, which impact the estimation accuracy and computational complexity under different system parameters. Simulation results reveal a substantial performance improvement achieved by the proposed ELM-based approach over the least-squares and NN-based benchmarks, with reduced training complexity and faster training speed.

Integrated sensing and communication (ISAC) and intelligent reflecting surface (IRS) are viewed as promising technologies for future generations of wireless networks. This paper investigates the channel estimation problem in an IRS-assisted ISAC system. A deep-learning framework is proposed to estimate the sensing and communication (S&C) channels in such a system. Considering different propagation environments of the S&C channels, two deep neural network (DNN) architectures are designed to realize this framework. The first DNN is devised at the ISAC base station for estimating the sensing channel, while the second DNN architecture is assigned to each downlink user equipment to estimate its communication channel. Moreover, the input-output pairs to train the DNNs are carefully designed. Simulation results show the superiority of the proposed estimation approach compared to the benchmark scheme under various signal-to-noise ratio conditions and system parameters.

This paper studies the sum-rate maximization problem of a distributed reconfigurable intelligent surface (RIS)-assisted full-duplex wireless system, where the availability of finite-resolution phase shifts at the RIS is considered. The aim is to optimize the transmit beamformers and RIS phase shifts, subject to the practical discrete phase shift and power constraints. The optimization problem is decoupled into two sub-problems; transmit beamforming and RIS phase shifts optimization. The transmit beamforming problem is mathematically addressed using approximate and closed-form solutions, while the discrete RIS phase shifts are optimized using a reinforcement learning (RL) approach. The existence and absence of a strong direct line-of-sight is investigated to show the effect of the phase shift optimization on the sum-rate. Simulation results illustrate that the proposed RL for the discrete phase shifts optimization provides a near-optimal performance with a small number of bits even for a large number of RIS elements, while improving the sum-rate compared to the random phase shift scenario and reducing the computational complexity compared to the state-of-the-art works.

Spatial modulation (SM) is a promising multiple- input multiple-output system used to increase spectral efficiency. The maximum likelihood (ML) decoder jointly detects the transmitted SM symbol, which is of high complexity. In this paper, a novel reliable sphere decoder (RSD) algorithm based on tree-search is proposed for the SM system. The basic idea of the proposed RSD algorithm is to reduce the size of the tree- search, and then, a smart searching method inside the reduced tree-search is performed to find the solution. The proposed RSD algorithm provides a significant reduction in decoding complexity compared to the ML decoder and existent decoders as well. Moreover, the RSD algorithm provides a flexible tradeoff between the bit error rate (BER) performance and decoding complexity, so as to be reliable for a wide range of practical hardware implementations. The BER performance and decoding complexity analysis for the RSD algorithm are studied, and Monte Carlo simulations are then provided to demonstrate the findings.

Multi-user spatial modulation (SM) assisted by sparse code multiple access (SCMA) has been recently proposed to provide uplink high spectral efficiency transmission. The message passing algorithm (MPA) is employed to detect the transmitted signals, which suffers from high complexity. This paper proposes three low-complexity algorithms for the first time to the SM-SCMA. The first algorithm is referred to as successive user detection (SUD), while the second algorithm is the modified version of SUD, namely modified SUD (MSUD). Then, for the first time, the tree-search of the SM-SCMA is constructed. Based on that tree-search, another variant of the sphere decoder (SD) is proposed for the SM-SCMA, referred to as fixed-complexity SD (FCSD). SUD provides a benchmark for decoding complexity at the expense of bit-error-rate (BER) performance. Further, MSUD slightly increases the complexity of SUD with a significant improvement in BER performance. Finally, FCSD provides a near-optimum BER with a considerable reduction of the complexity compared to the MPA decoder and also supports parallel hardware implementation. The proposed algorithms provide flexible design choices for practical implementation based on system design demands. The complexity analysis and Monte-Carlo simulations of the BER are provided for the proposed algorithms.

In this paper, a novel low-complexity detection algorithm for spatial modulation (SM), referred to as the minimum-distance of maximum-length (m-M) algorithm, is proposed and analyzed. The proposed m-M algorithm is a smart searching method that is applied for the SM tree-search decoders. The behavior of the m-M algorithm is studied for three different scenarios: 1) perfect channel state information at the receiver side (CSIR); 2) imperfect CSIR of a fixed channel estimation error variance; and 3) imperfect CSIR of a variable channel estimation error variance. Moreover, the complexity of the m-M algorithm is considered as a random variable, which is carefully analyzed for all scenarios, using probabilistic tools. Based on a combination of the sphere decoder (SD) and ordering concepts, the m-M algorithm guarantees to find the maximum-likelihood (ML) solution with a significant reduction in the decoding complexity compared with SM-ML and existing SM-SD algorithms; it can reduce the complexity up to 94% and 85% in the perfect CSIR and the worst scenario of imperfect CSIR, respectively, compared with the SM-ML decoder. The Monte Carlo simulation results are provided to support our findings as well as the derived analytical complexity reduction expressions.

In spatial modulation (SM) and quadrature SM (QSM), the maximum-likelihood (ML) decoder provides the optimum solution with high decoding complexity at the receiver side. This paper presents a novel low-complexity algorithm for decoding the SM and QSM symbols, referred to as the min-max algorithm. This is an intelligent searching algorithm, particularly designed for the tree-search of the SM and QSM decoders. The proposed algorithm expands the minimum Euclidean distance (ED) by adding a single node at each step, without considering the order of the branches. The expanding process stops if the minimum ED occurs at the end of a fully expanded branch. It is shown that the proposed algorithm achieves the optimum ML bit error rate performance with a significant reduction in the decoding complexity comparing with SM-ML and QSM-ML, as well as other existing sphere decoding algorithms. Simulations and mathematical analysis are provided to assess the decoding performance and complexity of the proposed algorithm.

The traditional K-best sphere decoder retains the best K-nodes at each level of the search tree; these K-nodes, include irrelevant nodes which increase the complexity without improving the performance. A variant of the K-best sphere decoding algorithm for ill-conditioned MIMO channels is proposed, namely, the ill-conditioned reduced complexity K-best algorithm (ill-RCKB). The ill-RCKB provides lower complexity than the traditional K-best algorithm without sacrificing its performance; this is achieved by discarding irrelevant nodes that have distance metrics greater than a pruned radius value, which depends on the channel condition number. A hybrid-RCKB decoder is also proposed in order to balance the performance and complexity in both well and ill-conditioned channels. Complexity analysis for the proposed algorithms is provided as well. Simulation results show that the ill-RCKB provides significant complexity reduction without compromising the performance.