The integrity and availability of outsourced data are critical concerns in open and distributed cloud-native infrastructures such as the O-RAN architecture. Existing Proof of Retrievability (PoR) schemes, though effective in traditional settings, are insufficient to address the unique challenges of O-RAN, particularly its heterogeneous slice-specific service requirements, dynamic control loops, and auditability constraints. In this paper, we propose a novel framework that integrates PoR mechanisms into the operational fabric of O-RAN by leveraging its native control architecture. Specifically, we introduce ASAP-CD (Attested Slice-Aware PoR with Challenge Diversity), a dynamic and policy-driven auditing protocol tailored to per-slice service-level agreements, and CPT-PoR (Commitment-Packed Tags for PoR), a lightweight, commitment-based construction that supports efficient designated-verifier auditing without relying on expensive cryptographic primitives. Our design incorporates Trusted Execution Environment (TEE)-based attestation, entropy-bound challenge generation, and real-time PoR coordination via xApps and rApps. Through formal analysis and prototype implementation, we demonstrate that the proposed framework achieves strong correctness, soundness, and retrievability guarantees, while maintaining scalability and modest overhead, making it suitable for deployment in practical O-RAN environments.

ABSTRACT Open Radio Access Network (O‐RAN) fronthaul links face stringent bandwidth, latency, and computational constraints, which become particularly critical when transmitting high‐resolution medical images. This paper proposes an adaptive region‐aware image compression framework for healthcare imaging over O‐RAN that reduces fronthaul load while preserving diagnostically relevant information. Each image is partitioned into Region‐of‐Interest (ROI) and Non‐ROI areas and compressed using independent quantisation parameters. An optimisation model is formulated to minimise transmitted data size subject to ROI and Non‐ROI quality constraints, end‐to‐end latency bounds and computational limits at O‐RAN nodes. The framework is evaluated using two medical imaging datasets (chest X‐rays and bone fracture X‐rays), where empirical rate–distortion and quality models are derived and validated. Results demonstrate substantial fronthaul bandwidth reduction—achieving compression ratios up to 416:1—while maintaining ROI quality and diagnostic accuracy above 97%. These findings highlight the effectiveness of region‐aware optimisation for bandwidth‐efficient healthcare imaging in O‐RAN environments.

Advancements in high-frequency communication technologies, including millimeter-wave (mmWave), terahertz (THz), and optical wireless bands, play a crucial role in extending wireless connectivity beyond 5 G. These bands provide ultra-wide bandwidths that enable very high data rates, support dense device deployments, and precise positioning. However, their performance critically depends on maintaining clear Line-of-Sight (LoS) conditions, since Non-Line-of-Sight (NLoS) components suffer from strong attenuation and reflection losses. While mmWave links may provide limited connectivity through NLoS reflections, LoS propagation remains the main factor governing the link budget and reliability of the target applications. In contrast, THz and optical wireless links are almost completely blocked by opaque materials, making LoS assurance essential. This paper tackles the issue of LoS coverage by determining the minimum number and optimal placement of Access Points (APs) required to ensure LoS connectivity in stochastic environments with random obstacles. The environment is modeled as a visibility graph whose nodes represent sub-polygons and edges denote visibility overlaps. Using maximal-clique clustering and maximum-clique packing algorithms, the proposed deterministic framework ensures LoS coverage under all realizations within the modeled stochastic ensemble, achieving up to a 50% reduction in the number of required APs while maintaining over 90% LoS coverage for every realization of obstacle locations.

Every year, the automobile sector experiences significant technology breakthroughs, which drive innovations in connected and autonomous cars. However, these developments increase cybersecurity concerns such as information theft, eavesdropping, and impersonation attacks, which compromise the integrity and safety of vehicle networks. In this paper, we provide a group secret key (GSK) generation technique designed specifically for vehicular networks. Our technique uses physical layer security to provide a shared GSK for all vehicles in a specific fleet, allowing for safe mutual authentication. The GSK is generated using the Doppler shift values and the channel randomness between pairs of vehicles, therefore using the inherent dynamics and communication patterns of vehicular movement. In addition, we ran extensive machine learning experiments to determine the accuracy and reliability of estimating the generated common key. The experimental findings show that machine learning models can predict the GSK, lowering the Key Error Rate (KER) and improving the overall security and reliability of group authentication in automotive networks.

The Open Radio Access Network (O-RAN) architecture is key to enabling AI-driven dynamic network management. However, the complexity of this architecture introduces challenges, especially in managing conflicts between different AI-driven applications that operate concurrently within the network. These conflicts, if left unchecked, can lead to degraded network performance and service disruptions. To address this issue, we propose XAI4C (Explainable AI for Conflict Detection), a framework that leverages the SHAP (SHapley Additive exPlanations) explainable AI technique. XAI4C enhances transparency and interpretability in AI decision-making by helping network operators understand the factors driving AI decisions across different network components thereby allowing for early detection of conflicts between applications. In this paper, we first present the architecture and operation of the XAI4C framework. We then demonstrate its effectiveness in conflict detection through two case studies related to network slicing. Our results demonstrate that XAI4C outperforms the state-of-the-art PACIFISTA providing a detection accuracy increase up to 30%, while reducing the number of samples required for conflict detection by 41.17%.

ABSTRACT Live virtual machine (LVM) migration is pivotal in cloud computing for its ability to seamlessly transfer virtual machines (VMs) between physical hosts, optimise resource utilisation, and enable uninterrupted service. However, concerns persist regarding safeguarding sensitive data during migration, particularly in critical sectors like healthcare, banking and military operations. Existing migration methods often compromise between performance and data security, prompting the need for a balanced solution. To address this, we propose a novel framework merging machine learning with selective encryption to fortify the pre‐copy live migration process. Our approach intelligently predicts optimal migration times while selectively encrypting sensitive data, ensuring confidentiality and integrity without compromising performance. Rigorous experiments demonstrate its effectiveness, showcasing an average 51.82% reduction in downtime and an average 72.73% decrease in total migration time across diverse workloads. This integration of selective encryption not only bolsters security but also optimises migration metrics, presenting a robust solution for uninterrupted service delivery in critical cloud computing domains.

Efficiently managing ambulance deployment is crucial for the success of emergency medical services, ensuring timely responses and life-saving care delivery. The initial ambulance location problem poses a challenge, demanding optimal deployment strategies to minimize response times and maximize coverage. Traditional approaches rely on heuristics and predetermined rules, struggling to adapt to the dynamic nature of emergencies. In response, this study proposes a dynamic ambulance redeployment system to reduce ambulance response time, increasing the chances of saving lives. The system identifies available ambulances finishing patient transports and strategically redistributes them to designated spokes, enhancing readiness for future emergencies. Addressing the inherent complexity, the study introduces a deep score network, utilizing Deep Reinforcement Learning (DRL) to train the network effectively. Our proposed approach achieved approximately 75% reduction in delays in Average Response Times (AvRT), utilizing real data from Qatar in a realistic deployment scenario. The outcome is a dynamic ambulance redeployment algorithm for real-world application, supported by experimental results using real-world data.

THz technology is considered a key element in 6G wireless communication because it provides ultra-high bandwidths, considerable capacities, and significant gains. However, wireless systems operating at high frequencies are faced with uncertainty and highly dynamic channels. Reflecting intelligent surfaces (RISs) can increase the range of the THz communication links and boost the rate at the receiver. In contrast to the existing literature, we investigate the scenario of multiple access multi-hop (cascaded) RISs uplink THz networks in a correlated channel environment. We show that our inspected cascaded RIS system is over-determined and that the rate maximization optimization problem is non-convex. To this end, we derive a closed-form expression of the received power and derive an analytical solution based on pseudo-inverse to obtain optimum RISs’ phase shifts that maximize the received signal power and hence increase the rate. In addition, we utilize deep reinforcement learning (DRL), which is capable of solving non-convex optimization problems, to obtain the optimum cascaded RISs’ phase shifts at the receiver taking into account the situation of the spatially correlated channels. Simulation results demonstrate that the DRL algorithm achieves higher rates than the mathematical sub-optimal method and the case of randomized phases.

In the era of 5G and beyond, telecommunication networks tend to move Radio Access Network (RAN) from centralized architecture to a more distributed architecture for greater interoperability and flexibility. Open RAN (O-RAN) architecture is a paradigm shift that is proposed to enable disaggregation, virtualization, and cloudification of RAN components, possibly offered from multiple vendors, to be connected through open interfaces. Leveraging this O-RAN architecture, Deep Learning (DL) models may be running as a service close to the end users, rather than on the core network, to benefit from reduced latency and bandwidth consumption. If multiple DL models learn on the virtual edge, they will compete for the available communication and computation resources. In this paper, we introduce Optimized Resource and Model Allocation (ORMA), a framework that provides optimized resource allocation for multiple DL models learning at the edge, that aims to maximize the aggregate accuracy while respecting the limited physical resources. Distinguished from related works, ORMA optimizes the learning-related parameters, such as dataset size and number of epochs, as well as the amount of communication and computation resources allocated to each DL model to maximize the aggregate accuracy. Our results show that ORMA consistently outperforms a baseline approach that adopts a fixed, fair resource allocation (FRA) among different DL models, at different total bandwidths and CPU combinations.

We propose a framework that enables the cluster head (CH) to harvest energy from uplink transmission by Internet of Things (IoT) nodes employing data compression under nonorthogonal multiple access (NOMA) scheme. Our framework enables the CH to maximize the harvested energy while meeting constraints on outage probability, consumed energies by the transmitting IoT nodes and compression and distortion ratios. We provide necessary analysis for our framework and derive an expression for the outage probability and average harvested energy under the NOMA scheme. We formulate an optimization problem with NOMA factors, simultaneous wireless information and power transfer (SWIPT) factors, and NOMA user distances as optimization parameters. We first solve the optimization problem and find the optimized values using a grid-based search. Then, we exploit a deep reinforcement learning algorithm to solve the optimization problem more efficiently. Throughout this work, we prove the feasibility of such framework and deliver key observation that will help the CH scheduling different IoT nodes such that the harvested energy is maximized while the constraints are met.

In this paper, we investigate the effect of a relay on the Degrees of Freedom (DoF) of a single input single output (SISO) X-channel with no channel state information at transmitters (CSIT). In contrast to previous work, which focused on two antennas at the relay to achieve the optimal 4/3 DoF, we focus on the case of a single antenna half duplex relay. We show that with a single antenna relay and delayed output feedback, the upper bound of 4/3 DoF for the X-channel is achievable and we propose the achievability scheme. Moreover, we study the alternating CSIT availability distribution for the SISO X-channel and provide few remarks on the achievability of the optimal DoF.

We propose a practical, simple and hardware friendly, yet novel and very efficient, angle of arrival (AoA) estimation system. Our intuitive, two-phases cross-correlation based system requires a switched beam antenna array with a single radio frequency chain. Our system cross correlates a reference omni-directional signal with a set of received directed signals to determine the AoA. Practicality and high efficiency of our system are demonstrated through performance and complexity comparisons with multiple signal classification algorithm.

Estimating the number of sources is a key task in many array signal processing applications. Conventional algorithms such as Akaike's information criterion (AIC) and minimum description length (MDL) suffer from underestimation and overestimation errors. In this study, the authors propose four algorithms to estimate the number of sources in white Gaussian noise. The authors’ proposed algorithms are categorised into two main categories; namely, sample correlation matrix (CorrM) based and correlation coefficient matrix (CoefM) based. Their proposed algorithms are applied on the CorrM and CoefM eigenvalues. They propose to use two decision statistics, which are the moving increment and the moving standard deviation of the estimated eigenvalues as metrics to estimate the number of sources. For their two CorrM based algorithms, the decision statistics are compared to thresholds to decide on the number of sources. They show that the conventional process to estimate the threshold is mathematically tedious with high computational complexity. Alternatively, they define two threshold formulas through linear regression fitting. For their two CoefM based algorithms, they re‐define the problem as a simple maximum value search problem. Results show that the proposed algorithms perform on par or better than AIC and MDL as well as recently modified algorithms at medium and high signal‐to‐noise ratio (SNR) levels and better at low SNR levels and low number of samples, while using a lower complexity criterion function.

In cognitive radio networks, secondary users (SUs) utilize the unused spectrum slots in the assigned band for the primary users (PUs). Conventional cognitive radio networks operate in half-duplex (HD) mode. Recently, full-duplex (FD) communication has become feasible. SUs with full-duplex capabilities can sense the spectrum and transmit simultaneously, which improves the efficiency of cognitive radio networks. In this paper, we study the performance of spectrum sensing based on general likelihood ratio (GLR) when the SU is operating in FD mode. We compare our results to the HD GLR case. We present the effect of residual self interference on the performance of the spectrum sensing technique. Moreover, we consider uncertainty in estimating the variance of the combined residual self interference and noise and show its effect on the performance of the FD GLR.

Abstract The vast majority of existing secret key generation protocols exploit the inherent randomness of the wireless channel as a common source of randomness. However, independent noise added at the receivers of the legitimate nodes affects the reciprocity of the channel. In this paper, we propose a new simple technique to generate the secret key that mitigates the effect of noise. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We study the properties of our generated SRP and derive a closed form expression for the probability mass function of the realizations of our SRP. We simulate an orthogonal frequency division multiplexing system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate between the legitimate nodes when compared with the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques. Moreover, we compute the conditional probabilities used to estimate the secret key capacity. Copyright © 2016 John Wiley & Sons, Ltd.

The broadcast nature of wireless communications imposes the risk of information leakage to adversarial users or unauthorized receivers. Therefore, information security between intended users remains a challenging issue. Most of the current physical layer security techniques exploit channel randomness as a common source between two legitimate nodes to extract a secret key. In this paper, we propose a new simple technique to generate the secret key. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We simulate an orthogonal frequency division multiplexing (OFDM) system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate (BMR) between the legitimate nodes when compared to the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques.

Estimating the number of sources received by an antenna array have been well known and investigated since the starting of array signal processing. Accurate estimation of such parameter is critical in many applications that involve prior knowledge of the number of received signals. Information theoretic approaches such as Akaikes information criterion (AIC) and minimum description length (MDL) have been used extensively even though they are complex and show bad performance at some stages. In this paper, a new algorithm for estimating the number of sources is presented. This algorithm exploits the estimated eigenvalues of the auto correlation coefficient matrix rather than the auto covariance matrix, which is conventionally used, to estimate the number of sources. We propose to use either of a two simply estimated decision statistics, which are the moving increment and moving standard deviation as metric to estimate the number of sources. Then process a simple calculation of the increment or standard deviation of eigenvalues to find the number of sources at the location of the maximum value. Results showed that our proposed algorithms have a better performance in comparison to the popular and more computationally expensive AIC and MDL at low SNR values and low number of collected samples.

In the context of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. Therefore an accurate estimation of this characteristic is the core for reliable secret key generation. Estimation of almost all the existing physical layer characteristic suffer dramatically at low signal to noise (SNR) levels. In this paper, we propose a novel secret key generation algorithm that is based on the estimated angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an outstanding performance at very low SNR levels. Our algorithm can exploit either the Azimuth AoA to generate the secret key or both the Azimuth and Elevation angles to generate the secret key. Exploiting a second common source of randomness adds an extra degree of freedom to the performance of our algorithm. We compare the performance of our algorithm to the algorithm that uses the most commonly used characteristics of the physical layer which are channel amplitude and phase. We show that our algorithm has a very low bit mismatch rate (BMR) at very low SNR when both channel amplitude and phase based algorithm fail to achieve an acceptable BMR.

Spectrum sensing is a key task in any cognitive radio network. On the other hand, a literature survey in this topic shows a lack of implementation and testbeds for spectrum sensing techniques. In this paper, we plot the ROC curves for the cyclostationary detection through an extensive Monte Carlo simulation for different detection times. Then we implement the cyclostationary feature detection algorithm on an FPGA based WARP kit. We compare its implementation complexity to the conventional energy detection technique as well as our newly developed and implemented quickest detection algorithm. We then propose a peak search based FAM algorithm that speeds up the detection time.

Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or communication systems. It can be computed either by a pilot signal data-aided approach in which the transmitted signal would be known to the receiver, or without any knowledge of the transmitted signal, which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR estimation algorithm for QPSK signal is proposed. The proposed algorithm modifies the existing Signal-to-Variation Ratio (SVR) SNR estimation algorithm in the aim to reduce its bias and mean square error in case of negative SNR values at low number of samples of it. We first present the existing SVR algorithm and then show the mathematical derivation of the new NDA algorithm. In addition, we compare our algorithm to two baselines estimation methods, namely the M2M4 and SVR algorithms, using different test cases. Those test cases include low SNR values, extremely high SNR values and low number of samples. Results showed that our algorithm had a better performance compared to second and fourth moment estimation (M2M4) and original SVR algorithms in terms of normalized mean square error (NMSE) and bias estimation while keeping almost the same complexity as the original algorithms.

Spectrum sensing is the core for any cognitive radio network. Quick detection of the primary user signal allows for higher spectrum efficiency. In this paper, we introduce and implement a new hybrid spectrum sensing technique that is based on combining GLRT with energy detection and utilizing multiple antennas. Our system introduces a compromise between speed and complexity. When the SNR goes below the SNR wall for the low complexity energy detection, our system switches to the more expensive GLRT algorithm. A practical prototype of our system is implemented on the WARP FPGA-based nodes to study its efficiency and complexity. In addition to practical experimental results, we derive theoretical closed form expressions for the probability of false alarm and the probability of detection for our new approach when using multiple antennas at the secondary users. We also compare the multiple antenna approach to the collaborative detection approach.