The sixth-generation (6G) wireless systems aim to achieve ultra-high data rates and enhanced connectivity, driven by the increasing demand for broadband services and interactive applications. However, achieving such high data rates introduces significant challenges, such as intersymbol interference (ISI), which degrades signal quality and system performance. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted single-carrier (SC) frequency domain equalization (FDE) to mitigate ISI and enhance broadband connectivity. The RIS reflection coefficients are configured to maximize the received signal power at the user equipment (UE) by compensating for the phase of the dominant-tap in the end-to-end channel of the proposed system. This configuration enables coherent signal combining at the receiver, thereby enhancing signal quality and overall system performance. The performance of the proposed system is analyzed over frequency-selective Rayleigh fading channels, and the pairwise error probability (PEP) expression and upper bound for the bit error rate (BER) are derived. Analytical and simulation results demonstrate that the proposed framework consistently outperforms the state-of-the-art cyclic prefix (CP) SC-RIS system, achieving up to two orders of magnitude improvement in terms of BER. Moreover, diversity analysis shows that while conventional CP transmission achieves a diversity order of 1, the proposed RIS-assisted framework attains a higher order equal tokλ, which corresponds to the shape parameter of the distribution of the weighted sum power of the end-to-end channel. This parameter scales linearly with both the number of RIS elements and the number of effective channel taps, thereby enabling enhanced diversity in the presence of richer multipath propagation and larger RIS arrays. Finally, performance analysis explicitly demonstrates that increasing the number of RIS elements and channel taps further improves system performance, highlighting the advantages of RIS-aided spatial configuration and multipath diversity exploitation.

This work investigates the performance of an Index Modulation-aided Non-Orthogonal Multiple Access (IM-NOMA) scheme in Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS)-assisted wireless networks. In the proposed system, information intended for a specific index-modulated (IM) user is conveyed via spatial modulation across STAR-RIS subsurfaces, where active indices are selected according to a predefined mapping. The IM user employs an energy-based maximum likelihood (EML) detector to infer the transmitted indices by exploiting the energy variations in the received signal. Alongside the IM user, additional users are multiplexed using conventional NOMA techniques, resulting in a hybrid IM-NOMA system. We focus on analyzing the conditional pairwise error probability (PEP) to characterize the system’s bit error rate (BER) performance. A union bound on the BER is derived using the PEP expression over Beaulieu-Xie (BX) fading channels. The resulting analysis provides tractable insights into the system behavior under practical propagation conditions. Extensive Monte Carlo simulations support the analytical findings, confirming the validity of the PEP-based union bound for evaluating the performance proposed IM-NOMA scheme in STAR-RIS environments.

The present contribution investigates Index Modulation Aided Non-Orthogonal Multiple Access (IM-NOMA) in simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-assisted networks. In the proposed IM-NOMA scheme, the information for a specific user (IM user) is spatially modulated across STAR-RIS subsurfaces according to a predefined pattern. Then, the IM user detects its signal using an energy-based maximum likelihood (EML) detection method, which technically leverages the energy of the received signals to identify the active subsurface indices. In this context, the performance of the proposed IM-NOMA scheme is quantified over Beaulieu-Xie (BX) fading channels in terms of pairwise error probability (PEP), bit error rate union bound, and achievable rate. The obtained PEP expressions are then used to derive a tight upper bound on the bit error rate (BER), which is subsequently utilized in quantifying the overall system performance in terms of upper-bound BER and the achievable rate. Finally, we validate the derived analytic expressions with respective results from extensive Monte Carlo simulations that provide valuable insights of the theoretical and practical importance on the achievable performance for all users in the system.

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.

The sixth-generation (6G) wireless network aims to deliver remarkable advancements in system throughput, energy efficiency, traffic capacity per area, spectral efficiency, and low latency. Achieving these goals requires a highly adaptable radio interface capable of efficiently managing limited frequency resources, necessitating the development of new multiple access techniques and waveforms. In large-bandwidth multi-user networks, intersymbol interference (ISI) and inter-user interference (IUI) pose significant design challenges. Time reversal (TR) has emerged as a promising waveform candidate for 6G, as it focuses signal energy in both the time and space domains within multipath environments. Meanwhile, non-orthogonal multiple access (NOMA) offers high spectral efficiency and improved connectivity by serving multiple users over the same time-frequency-code resources. This paper explores the integration of NOMA and TR to address these challenges and proposes, for the first time in the literature, a novel receiver architecture for downlink NOMA-based TR communications, which does not require precoding at the transmitter. Specifically, power-domain NOMA is employed at the transmitter, and TR filtering is applied at each receiver. We derive novel approximated expressions for the pairwise error probability (PEP), a key element in determining the union bound on the bit error rate (BER), to assess user performance. Extensive Monte Carlo simulations are carried out to validate these analytical expressions, providing critical insights into the error rate performance for each user. Additionally, we evaluate the performance gains of the proposed NOMA-based TR receiver over the orthogonal multiple access scheme, known as time-reversal multiple access (TRMA). Results show that our approach significantly outperforms TRMA in terms of BER, particularly in sparse multipath environments, with an average BER improvement of 73.5% to 98.31%. Furthermore, our findings reveal that at high signal-to-noise ratios, the diversity gain for a specific user is proportional to the product of the user’s order, determined by its channel strength, and the number of its channel taps.

Spatial Modulation (SM) has been proposed as a multiple-input-multiple-output (MIMO)-based technique to overcome the inter-channel interference experienced in conventional MIMO systems. It has been further shown that SM enhances energy efficiency and reduces the receiver’s complexity. Nevertheless, under high antenna correlation scenarios, the detection performance of the antenna indices degrades significantly. To address this critical concern, in this paper, we propose three autoencoder-based frameworks for spatial modulation. The first scenario, similar to conventional spatial modulation, trains the encoder for data modulation and the decoder for data demodulation as well as antenna index detection. The performance of this framework deteriorates in high antenna correlation scenarios. Therefore, two novel solutions are presented to embed the antenna index into the transmitted signal in order to reduce the receiver’s reliance on the channel conditions. The first framework adds a phase-shift keying-based antenna signature, while the other trains the encoder to learn an appropriate antenna index embedding. Simulation results show that the two enhanced frameworks result in a significantly enhanced performance, compared to conventional spatial modulation, in terms of block error rate and power efficiency under a high correlation setup (about 18 dB and 24 dB gain, respectively, at a Rician factor of 20 dB).

Automatic modulation classification (AMC) is an indispensable part of intelligent receivers in modern wireless communication systems. AMC enables blind identification of modulation without prior knowledge of the signal parameters, which is a challenging task, particularly in practical scenarios with severe multipath fading, frequency-selective and time-varying channels. Although deep learning techniques have been shown to be efficient in AMC tasks, traditional convolutional and recurrent neural networks may not be able to cope with complex-valued input signals and large-scale datasets. Motivated by this, in this paper, we propose a novel perceiver-based AMC architecture that leverages the recently introduced Perceiver, which combines cross-attention and latent transformer modules, to efficiently process and classify complex-valued in-phase and quadrature (IQ) samples of the received signal. The proposed model is trained and evaluated on the DeepSig 2018 RadioML dataset. Simulation results demonstrate a significant improvement in the classification accuracy compared to a ResNet-based AMC model, particularly for higher-order quadrature amplitude modulation (QAM) and under practical signal-to-noise ratio values. These findings indicate the potential of the perceiver architecture for robust and efficient AMC in wireless communication systems.

The upcoming sixth generation (6G) is expected to support a wide range of applications that require efficient sensing, accurate localization, and reliable communication capabilities. Furthermore, 6G is expected to catalyze the development of new use cases that will require working in extreme environmental and hazardous conditions and have ultra-small size and low-cost wire-less devices. Thus, developing sustainable multi-functional wireless networks that are capable of incorporating billions of low-power devices and supporting their sensing and communication requirements on top of energy harvesting capability is of paramount importance. Motivated by this, we consider in this work a rate-splitting multiple access (RSMA)-based multifunctional wireless network with sensing, energy harvesting, and communication capabilities. We employ trust region policy optimization (TRPO), a deep reinforcement learning (DRL) algorithm, to efficiently allocate the available resources and manage the interference between the three functionalities. TRPO/DRL is capable to learn a near-optimal policy for the resource allocation problem in a complex and dynamic environment. This enables us to obtain near-optimal transmit precoders, power splitting ratios, and rate-splitting among the common and private rates in a multiple access setting. Simulation results demonstrate the effectiveness of RSMA in mitigating the interference in such multi-functional networks and its capability to accommodate the rate and energy harvesting requirements of the devices while still capable of sensing multiple targets.

The emergence of new technologies for the fifth/sixth generation (5G/6G) wireless networks has led to the development of new services, resulting in an increase in malicious activities and cyber-attacks targeting various networklayers. Edge computing, a crucial technology enabler for 6G, is expected to facilitate traffic optimisation and support new ultra-low latency services. By integrating computing power from supercomputing servers into devices at the network edge in a distributed manner, edge computing can provide consistent quality-of-service, even in remote areas, which will drive the growth of associated applications. However, the complex environment created by edge computing also poses challenges for detecting malware. Therefore, this paper proposes a novel approach to malware detection using explain ability via visualization and a multi-labelling technique. An object detection algorithm is used to identify malware families within the dataset which is created by emphasizing key regions. Using features from different malware categories in an image, this model displays a thorough malware recipe. Our experiments using real malware data demonstrate that identifying malware by its visible characteristics can significantly improve the interpretability of the detection process, enhancing transparency and trustworthiness.

Traditional anti-jamming techniques like spread spectrum, adaptive power/rate control, and cognitive radio, have demonstrated effectiveness in mitigating jamming attacks. However, their robustness against the growing complexity of internet-of-thing (IoT) networks and diverse jamming attacks is still limited. To address these challenges, machine learning (ML)-based techniques have emerged as promising solutions. By offering adaptive and intelligent anti-jamming capabilities, ML-based approaches can effectively adapt to dynamic attack scenarios and overcome the limitations of traditional methods. In this paper, we propose a deep reinforcement learning (DRL)-based approach that utilizes state input from realistic wireless network interface cards. We train five different variants of deep Q-network (DQN) agents to mitigate the effects of jamming with the aim of identifying the most sample-efficient, lightweight, robust, and least complex agent that is tailored for power-constrained devices. The simulation results demonstrate the effectiveness of the proposed DRL-based anti-jamming approach against proactive jammers, regardless of their jamming strategy which eliminates the need for a pattern recognition or jamming strategy detection step. Our findings present a promising solution for securing IoT networks against jamming attacks and highlights substantial opportunities for continued investigation and advancement within this field.

This paper investigates different precoding strategies for rate splitting multiple access (RSMA) in the downlink of multi-cell visible light communication (VLC) networks. Since classical Shannon capacity formula does not hold for VLC, we first provide a lower bound on the channel capacity for RSMA in such interfering networks. Then, we formulate a spectral efficiency maximization problem to jointly find the optimal rate-splitting and transmit precoding. Beside that, since cell-edge users suffer from additional inter-cell interference, we propose to design the precoders of different RSMA signals utilizing coordinated beamforming (CB). Subsequently, aiming to improve the performance of the CB design for RSMA, while maintain a reduced complexity, we introduce two enhanced precoding strategies for RSMA. To the best of the authors’ knowledge such a contribution has not been considered before in the open literature. It is shown in the paper that the formulated optimization problem is non-convex and a sub-optimal, yet, a low complexity solution can be obtained efficiently using semi-definite relaxation combined with successive convex approximation. Through analytical results, we illustrate the flexibility and superiority of the proposed precoding strategies for RSMA over conventional coordinated space division multiple access and non-orthogonal multiple access for different scenarios and network loads.