This demonstration presents a software-defined radio (SDR) testbed for experimentally evaluating air-to-ground (A2G) communication under dynamic flight conditions. By synchronizing UAV telemetry with orthogonal frequency-division multiplexing (OFDM) measurements, the system enables precise frame-to-telemetry alignment and frame-level bit error rate (BER) analysis. Results show that yaw angle is the dominant flight parameter affecting link reliability. Building on this finding, we demonstrate a yaw-aware adaptive modulation and coding (AMC) scheme that maps angular conditions to the optimum modulation selection. The proposed framework provides a reproducible and practical platform for flight-parameter–aware link adaptation, advancing the robustness and efficiency of future UAV communication systems.

Separation-based pipelines for wireless image transmission suffers a cliff effect at low signal-to-noise ratios. While deep joint source–channel coding may alleviate this drawback, most of the designs ignore readily available side information during decoding. To overcome these drawbacks, we introduce a semantic JSCC scheme in which a convolutional encoder maps an image to a spatial latent vector that traverses through an AWGN channel, and a decoder that is conditioned on class labels and instantaneous SNR values via learnable embeddings. To this end, we trained our model end-to-end with a mixed loss combining L1 fidelity and structural similarity. Our method offers a threefold contributions such as label- and SNR-conditioned decoding, preservation of spatial latent structure, and a simple, compute-efficient objective yielding robust reconstructions under AWGN. Evaluated on STL-10 across a range of SNRs, the proposed method consistently improves both perceptual and distortion metrics over a non-conditioned JSCC baseline. At 5 dB SNR, it improves PSNR value by 4.86 percent and improves LPIPS value by 28 percent. Qualitative reconstructions show reduced over-smoothing and sharper class-relevant details at low SNR. Overall, the results indicate that we provide practical gains especially for compute-constrained noisy wireless links which are used to transmit visual information.

Precise Ultra-Wideband (UWB) localization often relies on tightly synchronized clocks, which limits scalability and increases deployment complexity, especially for real-time, multi-user environments. This constraint is a critical obstacle for lightweight, real-time positioning systems. To address this, we propose three synchronization-free localization models: Centralized-Relay-Based TDoA (i) and Distributed-Relay-Based TDoA (ii), which employ controlled signal relaying and pairwise clock rate estimation to accurately compute the Time Difference of Arrival (TDoA) without the need for global clock synchronization, and Slotted TWR (iii), a collision-free, TDMA-based Two-Way Ranging (TWR) protocol that assigns deterministic retransmission slots at the MAC layer, enabling reliable and scalable localization for many users simultaneously. Experimental evaluation on DWM3001CDK development kits demonstrates that Slotted TWR, in particular, achieves sub-15 cm accuracy and maintains robust performance both within and beyond the anchor region while significantly reducing channel usage compared to conventional TWR.

In today's interconnected world, traditional communication methods have reached the maximum potential set by Shannon's limit. Semantic communication (SemCom) emerged as a response to this situation. It is a visionary strategy that surpasses Shannon's limit, grounded in well-established theoretical foundations, including definitions of semantic noise, semantic encoding/decoding, semantic entropy, semantic channel capacity, and semantic rate-distortion. However, translating these theoretical concepts into practical implementation presents challenges. In this regard, we propose a SemCom-based testbed featuring an encoder-decoder architecture for handling semantic information. This architecture turns theory into a practical system to enhance SemCom strategies. The testbed will aid researchers in developing new protocols, facilitating the adaptation and enhancement of SemCom systems.

UAV swarms have become increasingly valuable due to their adaptability in complex scenarios, yet managing these swarms centrally in harsh environments remains challenging. This paper presents an autonomous and distributed UAV swarm framework designed to improve operational resilience against network disruptions, specifically jamming attacks. The swarm structure utilizes a head UAV orbited by subordinate UAVs that maintain communication through periodic heartbeat signals, monitoring packet delivery ratios (PDR). Adaptive relay ranges, influenced by UAV orbit radius adjustments, significantly enhance communication robustness. The proposed system offers self-healing and jammer avoidance capabilities while maintaining Software Defined Networking (SDN) functionalities that are convenient for operators. Simulation results confirm substantial improvements in latency, jitter, packet delivery ratio, and throughput under jamming conditions.

Unmanned Aerial Vehicle (UAV) swarms enable the rapid deployment of IoT services in dynamic and challenging environments. While these swarms offer flexibility and close proximity to sensing and actuation points, efficiently deploying interdependent services at scale remains a core challenge. Traditional centralized methods struggle to handle the complexity of large UAV networks, leading to increased latency and limited reliability. In this paper, we propose a decentralized approach to service deployment in UAV swarms. Our method relies on local information at each node, allowing UAVs to make their own assignment decisions. Over time, these decisions are iteratively refined as nodes exchange status updates and adapt to network changes. This process avoids the bottlenecks of centralized coordination and enables more responsive resource allocation. Simulation results show that our approach supports the successful deployment of a high number of tasks while maintaining low latency. These findings indicate that decentralized methods with local resource knowledge, improves both scalability and responsiveness in UAV-based IoT systems.

Coordinated beamforming is one of the crucial improvements for WiFi7. However, to provide sufficient Quality of Service, the corresponding beams have to be aligned accurately. Conventional approaches search for optimal beams in the available codebook to align the beams. However, this introduces considerable delays because of high search space. To address this issue, we utilize Ultra-Wide-Band (UWB) assisted localization to swiftly adjust the beams for users to provide accurate beams. Then by using Received Signal Strength Indicator (RSSI) and state estimation error of the Kalman filter in the position system, we propose a task oriented beamforming alignment system that uses UWB assisted localization only when necessary. Our approach improves beamforming gain by 11.6% compared to conventional beam scanning approaches. On top of this, the proposed solution improves communication effectiveness by 55.6% against naive UWB localization-assisted beam alignment.

Despite the recent advanced safety systems utilizing expensive equipment such as Lidar or high resolution cameras, detecting vulnerable users in the blind-spots is a challenging task especially when LoS obstructed. To address this issue, we propose ultra-wide-band based outdoor localization to detect motorcyclists and cyclists in blind spots as well as a simulation architecture combining vehicle and traffic dynamics with wireless communication models. Our proposed approach offers an affordable solution without necessitating additional hardware by using cellphones of the vulnerable users as beacon sources. Finally, early simulation results show that UWB-based method locates motorcyclists with a similar accuracy when compared to LiDAR in LoS scenarios.

Smart environmental monitoring is crucial for public health and ecological balance as it enables us to monitor and react to environmental hazards. However, effective environmental monitoring can be hindered by the lack of infrastructure and high monetary costs. These challenges are even more pronounced in remote areas, where networking and energy sources are often limited or nonexistent. To address these challenges, we utilize UAVs to form a FANET which can provide effective communication infrastructure suitable for environment monitoring. Moreover, we utilize Edge Intelligence at these UAVs to increase the processing speed and reduce the data size that needs to be transmitted. Our results show that, compared to statically placed gateways, our solution is able to attain similar average age of information for monitoring results while also significantly increasing system capacity.

Application domain of Unmanned Aerial Vehicles(UAVs) expands every day. Multiple of these devices can simply be put together to form versatile and powerful Mobile ad hoc Networks (MANETs). Unfortunately, the existing protocols suffer to maintain efficient routes and channel assignments in such dynamic environments, where network topology and channel characteristic are prone to constant changes. In this paper, we propose a cross-layer protocol, utilizing well-known path finding algorithm, D* Lite, to achieve joint optimization of routing and dynamic channel assignment in software-defined aerial networks. Our protocol, CentAir, first uses graph centrality measures to adjust transmit power of the central nodes to both reduce interference rate and congestion around the central nodes. Then, it determines which channel to utilize along the path while taking the estimated collision probabilities into account. Our extensive simulation results show that CentAir provides 132% better channel capacity with the cost of 3% longer paths on e2e routes compared to traditional shortest path routing algorithms with random channel assignments.

Flying Ad Hoc Networks (FANETs) need reliable intra-communication to work effectively during the missions they participate in, especially in contested environments. Furthermore, choosing shortest paths for all e2e links causes over-utilized hot spot nodes in the network. Thus, these hot spots have increased traffic that leads to more collisions around them. Consequently, these central nodes become easy targets for malicious attackers whose aim is to harm the network’s performance as efficiently as possible. To address this, we propose a routing framework (CentAir) that utilizes eigenvector centrality measures of the network nodes to fine-tune their transmission power. Then, it employs D* Lite path-finding algorithm to determine optimal e2e routes under varying network load. As a result, CentAir achieves up to 132% more channel capacity of e2e links compared to shortest path routing algorithms. Additionally, it shows 41% less throughput degradation when jammers are present.

Existing off-the-shelf Wi-Fi technologies lack in terms of providing reliable multihop links for communication-intensive applications. This drawback severely limits the potential of tactical aerial networks, where reliable multihop communication is a must for successful operation. In this paper, we propose a new chain RTS/CTS scheme for multihop communication in Aerial Networks. Our proposed scheme uses the existing IEEE 802.11 Independent Basic Service Set (IBSS) as a stepping stone and extends the capabilities of collision avoidance (CA) mechanism to reserve channels dynamically throughout the multihop communication link. Extensive simulation results, gathered on OMNET++, show 37% throughput improvement in multihop links with 68% increased connection establishment time when compared to existing IEEE 802.11 IBSS with simple channel hopping enhancement.