Search Results (260)

This work presents the design and experimental validation of a 2 × 2 MIMO communication system assisted by a directly modulated analog radio-over-fiber (A-RoF) fronthaul, targeting low-complexity connectivity solutions for underserved/remote regions. The study details the complete end-to-end architecture, including a wireless access segment to complement the 20-km optical fronthaul link. The system is implemented on an software defined radio (SDR) platform using GNU Radio 3.7.11, running on Ubuntu 18.04 with kernel 4.15.0-213-generic. It also employs adaptive modulation driven by real-time signal-to-noise ratio (SNR) estimation to keep bit error rate (BER) close to zero while maximizing throughput. Performance is characterized over 20 km of single-mode fiber (SMF) using coarse wavelength division multiplexing (WDM) and assessed through root mean square error vector magnitude (${\mathrm{EVM}}_{\mathrm{RMS}}$), throughput, and spectral integrity. The results identify an optimum radio-frequency drive region around $-16$ dBm enabling high-order modulation (e.g., 256-QAM), whereas RF input powers above approximately $-10$ dBm increase ${\mathrm{EVM}}_{\mathrm{RMS}}$ due to nonlinearity in the RF front-end/low-noise amplifier (LNA) and direct modulation stage, forcing the adaptive scheme to reduce modulation order and throughput. Over the optical-power sweep, when the incident optical power exceeds approximately $-8$ dBm, the system reaches ∼130 Mbps (24-MHz channel) with ${\mathrm{EVM}}_{\mathrm{RMS}}$ approaching ∼1%, highlighting the need for careful joint tuning of RF drive, optical launch power, and wavelength allocation across transceivers. Finally, the integrated access link employs diplexers for transmitter/receiver separation in a 2 × 2 configuration with 2.8 m antenna separation and low channel correlation, demonstrating a 10 m proof-of-concept range and enabling end-to-end spectrum/EVM/throughput observations across the full communication chain. Full article

Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article

The Global Positioning System (GPS) and Received Signal Strength Indicator (RSSI) usage for location provenance often fails in obstructed, noisy, or densely populated urban environments. This study proposes a passive location provenance method that uses the location’s acoustics and the device’s acoustic side channel to address these limitations. With the smartphone’s internal microphone, we can effectively capture the subtle vibrations produced by the capacitors within the voltage-regulating circuit during wireless transmissions. Subsequently, we extract key features from the resulting audio signals. Meanwhile, we record the RSSI values of the WiFi access points received by the smartphone in the exact location of the audio recordings. Our analysis reveals a strong correlation between acoustic features and RSSI values, indicating that passive acoustic emissions can effectively represent the strength of WiFi signals. Hence, the audio recordings can serve as proxies for Radio-Frequency (RF)-based location signals. We propose a location-provenance framework that utilizes sound features alone, particularly the Mel-Frequency Cepstral Coefficients (MFCCs), achieving coarse localization within approximately four kilometers. This method requires no specialized hardware, works in signal-degraded environments, and introduces a previously overlooked privacy concern: that internal device sounds can unintentionally leak spatial information. Our findings highlight a novel passive side-channel with implications for both privacy and security in mobile systems. Full article

Massive Internet of Things (IoT) deployments face critical spectrum crowding and energy scarcity challenges. Energy harvesting (EH) symbiotic radio (SR), where secondary devices share spectrum and harvest energy from non-orthogonal multiple access (NOMA)-based primary systems, offers a sustainable solution. We consider long-term throughput maximization in an EHSR network with a nonlinear EH model. To solve this non-convex problem, we designed a two-layered optimization algorithm combining convex optimization with a deep reinforcement learning (DRL) framework. The derived optimal power, time allocation factor, and the time-varying environment state are fed into the proposed long short-term memory (LSTM) attention mechanism combined Deep Deterministic Policy Gradient, named the LAMDDPG algorithm to achieve the optimal long-term throughput. Simulation results demonstrate that by equipping the Actor with LSTM to capture temporal state and enhancing the Critic with channel-wise attention mechanism, namely Squeeze-and-Excitation Block, for precise Q-evaluation, the LAMDDPG algorithm achieves a faster convergence rate and optimal long-term throughput compared to the baseline algorithms. Moreover, we find the optimal number of PDs to maintain efficient network performance under NLPM, which is highly significant for guiding practical EHSR applications. Full article

Currently, fifth-generation (5G) communication has emerged as the most promising candidate for next-generation railway-dedicated communication systems. Condition monitoring of 5G networks is critical for ensuring the continuity and reliability of train–ground communications. In this paper, a real-time monitoring technology is proposed, which is based on generalized channel characteristics extracted from received Demodulation Reference Signals (DM-RSs). Furthermore, a corresponding monitoring system has been developed based on the Radio Frequency System on Chip (RFSoC). Experimental results demonstrate that the proposed condition monitoring system exhibits excellent performance: it can accurately measure key network metrics (including field strength, multipath components, and frequency offset) and enable real-time monitoring of the operational condition of 5G radio access networks (RAN) and on-board terminals. Future work will focus on integrating the monitoring system into on-board terminals. Full article

The proliferation of Internet of Things (IoT) devices due to remarkable developments in mobile connectivity has caused a tremendous increase in the consumption of broadband spectrums in fifth generation (5G) mobile access. In order to secure the continued growth of IoT, there is a need for efficient management of communication resources in the 5G wireless access. Cognitive radio (CR) is advanced to maximally utilize bandwidth spectrums in the radio communication network. The integration of CR into IoT networks is a promising technology that is aimed at productive utilization of the spectrum, with a view to making more spectral bands available to IoT devices for communication. An important function of CR is spectrum sensing (SS), which enables maximum utilization of the spectrum in the radio networks. Existing SS techniques demonstrate poor performance in noisy channel states and are not immune from the dynamic effects of wireless channels. This article presents a comprehensive review of various approaches commonly used for SS. Furthermore, multi-agent deep reinforcement learning (MADRL) is proposed for enhancing the accuracy of spectrum detection in erratic wireless channels. Finally, we highlight challenges that currently exist in SS in CRIoT networks and further state future research directions in this regard. Full article

Radio frequency fingerprint identification (RFFI) leverages the unique characteristics of radio signals resulting from inherent hardware imperfections for identification, making it essential for applications in telecommunications, cybersecurity, and surveillance. Despite the advancements brought by deep learning in enhancing RFFI accuracy, challenges persist in model deployment, particularly when transferring RFFI models across different receivers. Variations in receiver hardware can lead to significant performance declines due to shifts in data distribution. This paper introduces the source-free cross-receiver RFFI (SCRFFI) problem, which centers on adapting pre-trained RF fingerprinting models to new receivers without needing access to original training data from other devices, addressing concerns of data privacy and transmission limitations. We propose a novel approach called contrastive source-free cross-receiver network (CSCNet), which employs contrastive learning to facilitate model adaptation using only unlabeled data from the deployed receiver. By incorporating a three-pronged loss function strategy—minimizing information entropy loss, implementing pseudo-label self-supervised loss, and leveraging contrastive learning loss—CSCNet effectively captures the relationships between signal samples, enhancing recognition accuracy and robustness, thereby directly mitigating the impact of receiver variations and the absence of source data. Our theoretical analysis provides a solid foundation for the generalization performance of SCRFFI, which is corroborated by extensive experiments on real-world datasets, where under realistic noise and channel conditions, that CSCNet significantly improves recognition accuracy and robustness, achieving an average improvement of at least 13% over existing methods and, notably, a 47% increase in specific challenging cross-receiver adaptation tasks. Full article

With the vigorous development of Internet of Things technologies, Cell-Free Radio Access Network (CF-RAN), leveraging its distributed coverage and single/multi-antenna Access Point (AP) coordination advantages, has become a key technology for supporting massive Machine-Type Communication (mMTC). However, under the grant-free random access mechanism, this network architecture faces the problem of information freshness degradation due to channel congestion. To address this issue, a joint decoding model based on logical grouping architecture is introduced to analyze the correlation between the successful packet transmission probability and the Peak Age of Information (PAoI) in both single-AP and multi-AP scenarios. On this basis, a global Particle Swarm Optimization (PSO) algorithm is designed to dynamically adjust the channel access probability to minimize the average PAoI across the network. To reduce signaling overhead, a PSO algorithm based on local topology information is further proposed to achieve collaborative optimization among neighboring APs. Simulation results demonstrate that the global PSO algorithm can achieve performance closely approximating the optimum, while the local PSO algorithm maintains similar performance without the need for global information. It is especially suitable for large-scale access scenarios with wide area coverage, providing an efficient solution for optimizing information freshness in CF-RAN. Full article

This paper presents a novel, four-port, rectangular microstrip, inset-feed multiple-input and multiple-output (MIMO) antenna array, enhanced with metamaterials for improved gain and isolation, specifically designed for multi-operator 5G open radio access network (ORAN)-based indoor software-defined radio (SDR) applications. ORAN is an open-source interoperable framework for radio access networks (RANs), while SDR refers to a radio communication system where functions are implemented via software on a programmable platform. A 3 × 3 metamaterial (MTM) superstrate is placed above the MIMO antenna array to improve gain and reduce the mutual coupling of MIMO. The proposed MIMO antenna operates over a 300 MHz bandwidth (3.5–3.8 GHz), enabling shared infrastructure for multiple operators. The antenna’s dimensions are 75 × 75 × 18.2 mm³. The antenna possesses a reduced mutual coupling less than −30 dB and a 3.5 dB enhancement in gain with the help of a novel 3 × 3 MTM superstrate 15 mm above the radiating MIMO elements. A performance evaluation based on simulated results and lab measurements demonstrates the promising value of key MIMO metrics such as a low envelope correlation coefficient (ECC) < 0.002, diversity gain (DG) ~10 dB, total active reflection coefficient (TARC) < −10 dB, and channel capacity loss (CCL) < 0.2 bits/sec/Hz. Real-world testing of the proposed antenna for ORAN-based sub-6 GHz indoor wireless systems demonstrates a downlink throughput of approximately 200 Mbps, uplink throughput of 80 Mbps, and transmission delays below 80 ms. Additionally, a walk test in an indoor environment with a corresponding floor plan and reference signal received power (RSRP) measurements indicates that most of the coverage area achieves RSRP values exceeding −75 dBm, confirming its suitability for indoor applications. Full article

The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and low energy consumption. The low data rate of low-power wide-area network (LPWAN) technologies, particularly SigFox, makes them appropriate for Internet of Things applications since the longer the radio link’s useable distance, the lower the data rate. Network reliability is the primary goal of SigFox technology, which aims to deliver data messages successfully through redundancy. This raises concerns about SigFox’s scalability and leads to one of its flaws, namely the high collision rate. In this paper, the goal is to prevent collisions by switching to time division multiple access (TDMA) from SigFox’s Aloha-based medium access protocol, utilizing only orthogonal channels, and eliminating redundancy. Consequently, during a designated time slot, each node transmits a single copy of the data message over a particular orthogonal channel. To achieve this, a multi-agent, off-policy reinforcement learning (RL) Q-Learning technique will be used on top of SigFox. In other words, the objective is to increase SigFox’s scalability through the use of Reinforcement Learning based time slot allocation (RL-SCAP). The findings show that, especially in situations with high node densities or constrained communication slots, the proposed protocol performs better than the basic SCAP (Slot and Channel Allocation Protocol) by obtaining a higher Packet Delivery Ratio (PDR) in average of 60.58%, greater throughput in average of 60.90%, and a notable decrease in collisions up to 79.37%. Full article

Complexity of non-orthogonal multiple access (NOMA) digital signal processing schemes is particularly relevant in mobile environments because of the varying channel conditions of every single user. In contrast to legacy modulation and coding schemes (MCSs), NOMA MCSs typically have irregular symbol constellations with asymmetric symbol decision regions affecting synchronization at the receiver. Research papers investigating signal processing in this emerging field usually lack sufficient details for facilitating software-defined radio (SDR) implementation. This work presents a new symbolic framework approach for simulating signal processing functions in SDR transmit–receive paths in a dynamic NOMA downlink use case. The proposed framework facilitates simple and intuitive implementation and testing of NOMA schemes and can be easily expanded and implemented on commercially available SDR hardware. We explicitly address several important design and measurement parameters and their relationship to different tasks, including variable constellation processing, carrier and symbol synchronization, and pulse shaping, focusing on quadrature amplitude modulation (QAM). The advantages of the proposed approach include intuitive symbolic modeling in a dynamic framework for NOMA signals; efficient, more accurate, and less time-consuming design flow; and generation of synthetic training data for machine-learning models that could be used for system optimization in real-world use cases. Full article

This paper presents an optimization model for wireless channel allocation in cellular networks, specifically designed for the transmission of smart meter (SM) data through a mobile virtual network operator (MVNO). The model efficiently allocates transmission channels, minimizing smart grid (SG) costs. The MVNO manages fixed and random channels through a shared access scheme, optimizing meter connectivity. Channel allocation is based on a Markovian approach and optimized through the Hungarian algorithm that minimizes the weight in a bipartite network between meters and channels. In addition, cumulative tokens are introduced that weight transmissions according to channel availability and network congestion. Simulations show that dynamic allocation in virtual networks improves transmission performance, contributing to sustainability and cost reduction in cellular networks. This study highlights the importance of inefficient resource management by cognitive mobile virtual network and cognitive radio virtual network operators (C-MVNOs), laying a solid foundation for future applications in intelligent networks. This work is motivated by the increasing demand for efficient and scalable data transmission in smart metering systems. The novelty lies in integrating cumulative tokens and a Markovian-based bipartite graph matching algorithm, which jointly optimize channel allocation and transmission reliability under heterogeneous wireless conditions. Full article

Aiming to address the issue of multi-user dynamic spectrum access in an opportunistic mode in cognitive radio networks leading to low sum throughput, we propose a multi-user opportunistic spectrum access method based on multi-head self-attention and multi-agent deep reinforcement learning. First, an optimization model for joint channel selection and power control in multi-user systems is constructed based on centralized training with a decentralized execution framework. In the training phase, the decision-making policy is optimized using global information, while in the execution phase, each agent makes decisions according to its observations. Meanwhile, a multi-constraint dynamic proportional reward function is designed to guide the agent in selecting more rational actions by refining the constraints and dynamically adjusting the reward proportion. Furthermore, a multi-head self-attention mechanism is incorporated into the critic network to dynamically allocate attention weights to different users, thereby enhancing the ability of the network to estimate the joint action value. Finally, the proposed method is evaluated in terms of convergence, throughput, and dynamic performance. Simulation results demonstrate that the proposed method significantly improves the sum throughput of secondary users in opportunistic spectrum access. Full article

The emergence of the Internet of Things (IoT) technologies has greatly enhanced the lives of individuals with disabilities by leveraging radio frequency identification (RFID) systems to improve autonomy and access to essential services. However, these advancements also pose significant security risks, particularly through side-channel attacks that exploit weaknesses in the design and operation of RFID tags and readers, potentially jeopardizing sensitive information. To combat these threats, several solutions have been proposed, including advanced cryptographic protocols built on cryptographic algorithms such as elliptic curve cryptography. While these protocols offer strong protection and help minimize data leakage, they often require substantial computational resources, making them impractical for low-cost RFID tags. Therefore, it is essential to focus on the efficient implementation of cryptographic algorithms, which are fundamental to most encryption systems. Cryptographic algorithms primarily depend on various finite field operations, including field multiplication, field inversion, and field division. Among these operations, field multiplication is especially crucial, as it forms the foundation for executing other field operations, making it vital for the overall performance and security of the cryptographic framework. The method of implementing field multiplication operation significantly influences the system’s resilience against side-channel attacks; for instance, implementation using unidirectional systolic array structures can provide enhanced error detection capabilities, improving resistance to side-channel attacks compared to traditional bidirectional multipliers. Therefore, this research aims to develop a novel unidirectional systolic array structure for the Dickson basis multiplier, which is anticipated to achieve lower space and power consumption, facilitating the efficient and secure implementation of computationally intensive cryptographic algorithms in RFID systems with limited resources. This advancement is crucial as RFID technology becomes increasingly integrated into various IoT applications for individuals with disabilities, including secure identification and access control. Full article

Spoofing events are increasingly affecting the performance of devices and operations relying on Global Navigation Satellite Systems (GNSSs). Developing powerful and robust GNSS spoofing detection and mitigation algorithms is an important endeavor in the GNSS community nowadays; some of the challenges in this field are limited access to spoofing measurement data, as spoofing over wireless channels is not legally allowed and in-lab spoofing emulators are not necessarily able to precisely capture the effects of radio channels, and the fact that classical Receiver Autonomous Integrity Monitoring approaches are typically quite complex, especially when dealing with complex or targeted spoofers. Our paper addresses these two challenges, first, by proposing a targeted spoofing model with a variable number of spoofed satellites, starting from Android raw pseudorange measurements, and second, by introducing a consistency-check-based iterative approach for spoofing detection and mitigation. We test our solution in various dynamic scenarios (bus, walk, ferry, car, flight, and bike), and we show that the positioning error correction rates depend on the number of spoofing pseudorandom (PRN) codes, as well as on the spoofing error introduced by our model. We also show that a large part of the spoofing errors can be mitigated with the proposed algorithms if the number of spoofed satellites (or pseudoranges) is sufficiently low with respect to the total number of visible satellites. Full article