Delivering downlink ACKs to Class A LoRaWAN devices via LEO satellite constellations requires predicting end-to-end propagation delays of 10–500 ms to hit a 1-second receive window, a timing challenge absent from terrestrial schedulers. We present DORSAL, the first Mixed-Integer Linear Programming (MILP) scheduler for downlink delivery in direct-to-satellite LoRaWAN networks. It introduces theDownlink Arrival Receive-window Timing (DART)as the central constraint to jointly optimize gateway selection and packet injection timing across ISL-capable and bent-pipe architectures. Evaluation on a Walker 5×5 polar constellation (25 satellites, 650 km) and a commercial ground station network yields five results: (i) DART infeasibility blocks 73% of bent-pipe deliveries regardless of scheduler quality: DART-unaware approaches collapse from 93% nominal to only 20% real delivery; (ii) ISL routing raises %ACK by +67.4 pp over bent-pipe (91.6% vs. 24.2% atnGS=3 ground stations, ISM band) by eliminating the three-way simultaneous-visibility constraint, with 73% of ACKs delivered via ≥ 2-hop paths; (iii) ISL %ACK is nearly flat acrossnGS∈ {3, 6, 12} ground stations (91.6%–92.7%), showing that a minimal threestation polar network captures the full scheduling benefit of an ISL-equipped constellation; (iv) ISL hardware speed has negligible impact up to 300 ms/hop: %ACK is flat from 0 to 300 ms, then degrades in two discrete steps (−5.3 pp at 500 ms, −2 pp at 700 ms) as successive hop-count tiers lose causal injection feasibility; and (v) the MILP solves to proven optimality within seconds for up to 103devices, confirming that the sparse conflict-graph structure keeps the problem tractable at realistic IoT subscriber counts.

Deploying low-altitude unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is a promising approach to enhance cellular network coverage in dynamic and infrastructure-constrained environments. However, real-world UAV deployment involves complex challenges, including managing partially observable and mobile users, maintaining multi-hop backhaul connectivity to a fixed ground base station, mitigating co-channel interference via minimum UAV separation, and optimizing coverage efficiency, fairness, and energy consumption. This paper proposes a multi-agent deep reinforcement learning (MARL) framework that formulates the dynamic UAV deployment task as a centralized multi-input multi-output policy. Our method employs sequential action masking to guarantee multi-hop connectivity and enforce minimum distance constraints during UAV placement. The policy network integrates graph neural networks, residual convolutional layers, and feature-wise linear modulation to effectively capture spatial relations, inter-UAV interactions, and global and local network states. We validate our approach through extensive simulations across various user density scenarios, demonstrating significant improvements in coverage efficiency, fairness of coverage distribution, and flight energy reduction compared to strong baseline algorithms. Our proposed framework provides a comprehensive solution that simultaneously addresses dynamic user mobility, multi-hop connectivity, interference mitigation, and energy efficiency in UAV-assisted cellular networks.

This paper studies the performance of an automatic head-on vehicle collision warning system based on a decentralized sensing approach using radio frequency (RF) signals. To identify a vehicle moving toward another, a communication system is used in reception mode, and a continuous wave (CW) RF signal transmitted by a third vehicle driving behind as a probe signal. The gathered signal was classified using a long short-term memory neural network. A data set consisting of CW RF signals was collected in a series of experiments in a highway scenario. The signals were processed to find Doppler signatures of approaching vehicles. This information is used to detect events of interest. Our results demonstrate the system’s feasibility, obtaining a precision of 98.6% in a multi-class classification assessment.

In this work, we present a comparative study of two strategies for opportunistic vehicular access over TV White Spaces (TVWS) spectrum, in the presence of fixed secondary networks. When the vehicular network meets other opportunistic users in the same TVWS channel, the first strategy is searching for a TVWS channel for exclusive vehicular access, while the other approach explores the coexistence between the vehicular network and the fixed secondary network. To carry out the evaluation of the strategies, we use the NS-3 network simulator, including some of the cognitive radio modules developed. Results showed that, in the case of study, the strategy of coexistence between the vehicular network and other opportunistic users over the TVWS channel (i.e., sharing the same TV channel) reduces the average access channel delay up to 15% and the average packet loss up to 17%.

The coexistence of different types technologies supported by the same infrastructure introduces unwanted interference that affects negatively the communication system. In this manuscript, recently proposed Nyquist-I pulses are evaluated in terms of bit error rate (BER) considering first, the effect of cochannel interference (CCI) and later, inter-symbol interference (ISI) and CCI simultaneously, under the effects of time jitter. The results indicate that considering a fixed interference power, as the number of interfering signals increases, the effect of the CCI also increases. Additionally, when the effects of CCI and ISI are considered simultaneously, both the magnitude of the main lobe and the magnitudes of the lateral side-lobes of the impulse response are preponderant in the calculation of the BER.

The next generations of wireless communications are expected to have great demand for security and spectrum efficiency, and the current secrecy solutions may not be enough. In this paper we propose an optimization framework to address the physical layer security in cognitive radio networks when the secondary users employ improper Gaussian signaling. We resort to genetic algorithms to find optimal values of the secondary transmit power and the degree of impropriety, simultaneously. Then, two different problems regarding the system performance are solved: minimizing the secrecy outage probability and maximizing the secondary achievable rate. In both problems we evaluate, besides the secrecy outage probability, the effective secure throughput and the secure energy efficiency of the system as well. The results show that the secondary network using improper signaling outperforms conventional proper signaling in terms of secrecy outage probability and the effective secure throughput, while in terms of the secure energy efficiency, adopting proper signals attains better performance than improper ones.

Several techniques have been proposed to address the problem of high sensitivity to frequency offset in orthogonal frequency division multiplexing (OFDM) systems. Frequency offsets results in intercarrier interference (ICI) which eventually degrades the performance of the overall system. The most common approach being used to suppress the effects of ICI power in OFDM systems is a pulse-shaping technique. Two novel families of Nyquist-I pulses are proposed to minimize the ICI power and increase signal-to-interference ratio (SIR) power in OFDM-based systems. The proposed pulses are characterized by novel design parameters which provide extra degrees of freedom for a certain value of roll-off factor, α. The proposed pulses are examined with respect to ICI power and SIR in OFDM system. We evaluate and compare the performance of the proposed pulses with other recommended pulses for OFDM systems. Simulation results show that the proposed pulses performed better than other existing pulses in terms of ICI power and SIR for OFDM systems.