Multi-Cell, Multi-Channel Scheduling with Probabilistic Per-Packet Real-Time Guarantee

For mission-critical sensing and control applications such as those to be enabled by 5G Ultra-Reliable, Low-Latency Communications (URLLC), it is critical to ensure the communication quality of individual packets. Prior studies have considered Probabilistic Per-packet Real-time Communications (PPRC) guarantees for single-cell, single-channel networks with implicit deadline constraints, but they have not considered real-world complexities such as inter-cell interference and multiple communication channels. Towards ensuring PPRC in multi-cell, multi-channel wireless networks, we propose a real-time scheduling algorithm based on \emph{local-deadline-partition (LDP)}. The LDP algorithm is suitable for distributed implementation, and it ensures probabilistic per-packet real-time guarantee for multi-cell, multi-channel networks with general deadline constraints. We also address the associated challenge of the schedulability test of PPRC traffic. In particular, we propose the concept of \emph{feasible set} and identify a closed-form sufficient condition for the schedulability of PPRC traffic. We propose a distributed algorithm for the schedulability test, and the algorithm includes a procedure for finding the minimum sum work density of feasible sets which is of interest by itself. We also identify a necessary condition for the schedulability of PPRC traffic, and use numerical studies to understand a lower bound on the approximation ratio of the LDP algorithm. We experimentally study the properties of the LDP algorithm and observe that the PPRC traffic supportable by the LDP algorithm is significantly higher than that of a state-of-the-art algorithm

Power control for predictable communication reliability in wireless cyber-physical systems

Wireless networks are being applied in various cyber-physical systems and posed to support mission-critical cyber-physical systems applications. When those applications require reliable and low-latency wireless communication, ensuring predictable per-packet communication reliability is a basis. Due to co-channel interference and wireless channel dynamics (e.g. multi-path fading), however, wireless communication is inherently dynamic and subject to complex uncertainties. Power control and MAC-layer scheduling are two enablers. In this dissertation, cross-layer optimization of joint power control and scheduling for ensuring predictable reliability has been studied. With an emphasis on distributed approaches, we propose a general framework and additionally a distributed algorithm in static networks to address small channel variations and satisfy the requirements on receiver-side signal-to-interference-plus-noise-ratio (SINR). Moreover, toward addressing reliability in the settings of large-scale channel dynamics, we conduct an analysis of the strategy of joint scheduling and power control and demonstrate the challenges. First, a general framework for distributed power control is considered. Given a set of links subject to co-channel interference and channel dynamics, the goal is to adjust each link\u27s transmission power on-the-fly so that all the links\u27 instantaneous packet delivery ratio requirements can be satised. By adopting the SINR high-delity model, this problem can be formulated as a Linear Programming problem. Furthermore, Perron-Frobenius theory indicates the characteristic of infeasibility, which means that not all links can nd a transmission power to meet all the SINR requirements. This nding provides a theoretical foundation for the Physical-Ratio-K (PRK) model. We build our framework based on the PRK model and NAMA scheduling. In the proposed framework, we dene the optimal K as a measurement for feasibility. Transmission power and scheduling will be adjusted by K and achieve near-optimal performance in terms of reliability and concurrency. Second, we propose a distributed power control and scheduling algorithm for mission-critical Internet-of-Things (IoT) communications. Existing solutions are mostly based on heuristic algorithms or asymptotic analysis of network performance, and there lack eld-deployable algorithms for ensuring predictable communication reliability. When IoT systems are mostly static or low mobility, we model the wireless channel with small channel variations. For this setting, our approach adopts the framework mentioned above and employs feedback control for online K adaptation and transmission power update. At each time instant, each sender will run NAMA scheduling to determine if it can obtain channel access or not. When each sender gets the channel access and sends a packet, its receiver will measure the current SINR and calculate the scheduling K and transmission power for the next time slot according to current K, transmission power and SINR. This adaptive distributed approach has demonstrated a signicant improvement compared to state-of-the-art technique. The proposed algorithm is expected to serve as a foundation for distributed scheduling and power control as the penetration of IoT applications expands to levels at which both the network capacity and communication reliability become critical. Finally, we address the challenges of power control and scheduling in the presence of large-scale channel dynamics. Distributed approaches generally require time to converge, and this becomes a major issue in large-scale dynamics where channel may change faster than the convergence time of algorithms. We dene the cumulative interference factor as a measurement of impact of a single link\u27s interference. We examine the characteristic of the interference matrix and propose that scheduling with close-by links silent will be still an ecient way of constructing a set of links whose required reliability is feasible with proper transmission power control even in the situation of large-scale channel dynamics. Given that scheduling alone is unable to ensure predictable communication reliability while ensuring high throughput and addressing fast-varying channel dynamics, we demonstrate how power control can help improve both reliability at each time instant and throughput in the long-term. Collectively, these ndings provide insight into the cross-layer design of joint scheduling and power control for ensuring predictable per-packet reliability in the presence of wireless network dynamics and uncertainties