Network-assisted full-duplex (NAFD) cell-free (CF) network emerges as a promising solution for enabling massive ultra-reliable and low-latency communications (mURLLC). In the massive Internet-of-Things (mIoT) scenarios where users’ active statuses change, the existing NAFD resource scheduling schemes require frequent invocation of optimization algorithms, causing significant energy loss and operational delays. So they are not conducive to mURLLC. This paper proposes a dual time-scale resource scheduling scheme, which combines the improved long-term AP duplex mode optimization method with the short-term power allocation optimization method to further enhance the mURLLC ability of NAFD CF networks. In the proposed long-term AP duplex mode optimization method, we first derive the closed-form expressions of active users’ time overflow (TO) probability as the service latency indicator. Operating on a superframe as a large time-scale unit, the improved long-term AP duplex mode optimization method initially employs a long-term active user prediction algorithm to forecast active users in an upcoming superframe and then leverages the long-term AP duplex mode optimization algorithm based on multi-agent deep reinforcement learning to achieve optimal long-term AP mode selection which minimizes the TO probability. In the proposed short-term power allocation optimization method, we design a heuristic algorithm to ensure active users in each coherence time can receive high-reliable and low-latency service. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the short-term AP mode and power joint optimization methods, the dual time-scale resource scheduling scheme achieves similar spectral efficiency and a much lower TO probability, while also avoiding the frequent AP mode optimization and switching, making it more suitable for mURLLC.

Cellular-connected unmanned aerial vehicles (UAVs) is considered as integral components for sixth generation (6G) cellular networks. Cell-free radio access network (CF-RAN) with network-assisted fullduplex (NAFD) possesses global collaborative capabilities and enable uplink and downlink transmission simultaneously, which can be considered as a potential technology for supporting air-ground communication. In this paper, we investigate the performance of cellular-connected UAV in CF-RAN with NAFD. We propose a modified beamforming training scheme to mitigate cross-link interference (CLI) and a location-aware access point (AP) clustering strategy to reduce fronthaul overhead. We derive closed-form expressions for uplink and downlink achievable rates of GUEs and UAVs, respectively. Based on these expressions, we propose an efficient global spectral efficiency optimization scheme by solving a multi-objective optimization problem (MOOP) aiming to maximize the uplink sum rates and downlink sum rates simultaneously with deep Q-network (DQN). Numerical results verify the accuracy of the derived closed-form expressions. The effectiveness of the modified beamforming training scheme and location-aware AP clustering strategy are proved. In addition, the impact of system parameters and the advantages of NAFD system are analyzed. We also illustrate the convergence and benefits of DQN-based optimization scheme on different type of users.

This paper continues the research on deterministic end-to-end delay analysis of packet flows in connection-oriented networks. It extends the results presented by Chlamtac, Farago, Zhang, and Fumagalli (see IEEE/ACM Trans. Networking, vol.6, no.4, 1998) by obtaining a smaller upper bound of end-to-end delay in connection-oriented networks under the same source rate condition. It further investigates the tightness of this upper bound and proves that it is tight for multipoint-to-point trees.

A method is presented to find routes for UBR (unspecified bit rate) circuits in ATM networks to make UBR traffic well-balanced. It selects an optimal path for each UBR circuit, while avoiding the potential problem of selecting the same optimal path or subpath repeatedly for different circuits. This is achieved by choosing an optimal path uniformly at random from the set of all optimal paths, according to any user-selected metric. Since the set of potential paths is exponentially large and has a complex structure, a new tool is needed to select a path uniformly at random from the set of all optimal paths efficiently. We propose a solution whose complexity is only that of shortest path selection, and we prove that it selects any optimal path with equal probability, thus providing good load balancing among candidate routes. Given the efficiency of the proposed solution and the above properties of the resulting paths selection, we believe the solution can be a practical approach to load balancing of UBR circuits in ATM networks.

We analyze the worst-case behavior of general connection-oriented networks, with first-in-first-out (FIFO) queueing policy, forwarding packets along an arbitrary system of routes. A worst-case bound is proven for the end-to-end queueing delay and buffer size needed to guarantee loss-free packet delivery, given that sources satisfy a given source rate condition. The results are based on a novel deterministic approach and help in reconciling the discrepancy between the unstable worst-case behavior of FIFO-based networks and their good practical performance.

This paper introduces a novel technique called protocol threading, yielding a deterministic protocol that gives a guaranteed upper bound on the transmission delay of each packet at every node in a multihop mobile network. By eliminating the maximum degree constraint, the new method improves upon existing time-spread multiple-access (TSMA)-type protocols while preserving the advantages of the deterministic operation and topology transparency. We introduce the protocol threading solution, derive the maximum delay bound in a mobile topology, and analyze the performance of the protocol.