The latency gap between wired and wireless networks poses a challenge in the adoption of wireless technologies in latency-sensitive scenarios. The gap is especially notable in multi-hop communication typical for industrial sensor networks and robotic swarms. The main reason behind it is that commonly used wireless protocols rely on store-and-forward routing and costly overhead procedures to avoid interference. This article introduces RF-Zero-Wire, an RF-based symbol-synchronous communication protocol. Instead of relaying the whole frame per hop in a store-and-forward manner, nodes concurrently relay the frame symbol by symbol, without the need for tight time synchronization. Based on data collected in real-world experiments, we reveal that the inevitable carrier frequency offsets (CFOs) introduced by imperfect crystal oscillators cause a beating effect under concurrent symbol transmissions. This is characterized by periodic constructive and destructive interference, which significantly affects reliability. Subsequently, a thorough simulation study shows how the beating problem can be overcome with error correction codes. RF-Zero-Wire allows achieving an end-to-end latency of less than 1 ms for a small 4-byte frame transmitted across 5 hops. Moreover, latency is shown to increase only by 0.16% per extra hop for 16-byte frames, which is negligible compared to the over 100% per-hop latency increase observed in store-and-forward protocols. The trade-offs between network reliability and CFO range, communication distance, node density, and achievable data rate are studied in large-scale experiments based on simulation.

Over the years, advancements such as increased bandwidth, new modulation and coding schemes, frame aggregation, and the use of multiple antennas have been employed to enhance Wi-Fi performance. Nonetheless, as network density and the demand for low-latency applications increases, contention delays and retransmissions have become obstacles to efficient Wi-Fi deployment in modern scenarios. The introduction of Orthogonal Frequency-Division Multiple Access (OFDMA) in the IEEE 802.11 standard allows simultaneous transmissions to and from multiple users within the same transmission opportunity, thereby reducing the contention. However, the AP must efficiently manage the resource allocation, particularly in uplink scenarios where it lacks prior knowledge of users' buffer statuses, thus making polling a critical bottleneck in networks with a high number of users with sporadic traffic pattern. This paper addresses the polling problem and introduces the A2P algorithm, designed to enable scalable and efficient polling in high-density OFDMA-based time sensitive Wi-Fi networks. Simulation results show that A2P outperforms the alternative schemes by maintaining significantly lower delay and packet loss in dense time-sensitive teleconferencing scenario.

Joint Communication and Sensing (JCAS) is expected to play a critical role in next-generation wireless networks such as 6G. For complex sensing tasks, such as 3D pose estimation for virtual reality (VR) applications, accurate channel impulse response (CIR) or I/Q samples as well as processing using a neural network is required. Due to the higher bandwidth and antenna array sizes of future wireless networks, it is expected that offloading this data to a remote server for processing would require data rates in the order of 100s of Megabits per second, which is an unreasonable amount of overhead. Therefore it is necessary to preprocess the sensing data locally, and reduce the raw data to useful intermediary features, to mimimize the sensing data transmission overhead, especially when using multiple sensing devices. This paper proposes a method leveraging split inference to distribute neural networks across multiple devices, which achieves high accuracy while addressing the sensing data transfer bottleneck. We evaluate the performance of the proposed method in a VR gaming scenario, where mmWave Wi-Fi signals are used for 3D pose estimation. We show that split inference allows for reducing the communication overhead by three orders of magnitude compared to the centralised approach, while only losing 10% of accuracy. These results pave the way for future work, exploring highly distributed multi-static JCAS as a practical and efficient method of sensing.

Real-time applications (RTA) tend to play a crucial role in people's everyday life. Such applications are among the key use cases for the next generations of wireless technologies. RTA applications are characterized by strict guaranteed delay requirements (in the order of a few milliseconds). One of the pillars of enabling RTA in next-generation Wi-Fi standards is Restricted Target Wake Time (R-TWT), which provides Wi-Fi stations exclusive channel access within negotiated service periods (SPs). If each RTA data flow uses dedicated SPs for data transmission, they are completely isolated from each other and do not experience any contention. To ensure the satisfaction of RTA QoS requirements while minimizing the channel airtime consumption, it is important to properly select the R-TWT pa-rameters, namely the duration of SPs and the period between SPs. In this paper, we develop a mathematical model that estimates the delay probability distribution and packet loss probability for a given set of network, traffic and R-TWT parameters. Using this model, the access point can select the optimal R-TWT parameters for the given QoS requirements. The high accuracy of the model is proven by means of simulation.

Various networking protocols currently used in wireless networks disseminate much control information. In particular, the deterministic channel access protocol (called MCCA) specified in the IEEE 802.11 standard sends information about channel reservations. The standard proposes a novel approach to reduce this overhead, which is based on dividing various pieces of control information into several groups. When the content of some group changes, the station sends a differential update related to this group. However, a group management algorithm (GMA) - which directly affects the performance of such an approach - is not specified in the standard. Previous studies propose a simple GMA which is based on the idea to use as low number of groups as possible. However, when the content of groups changes very fast, the overhead is high. In this paper, we propose and study an enhanced GMA which manyfold reduces the overhead. We develop an analytical model to evaluate the proposed GMA performance and to find its optimal parameters. We also obtain an explicit formula for the optimal parameters in the asymptotic case. We show that such asymptotically optimal parameters, which do not depend on traffic parameters, provide low overhead in non-asymptotic case as well. Thus, the proposed GMA can be easily implemented without any complex method for parameters adjustment.1