With the emerging Internet of Things paradigm, massive Machine-Type Communication (mMTC) has been identified as one of the prominent services that enables a broad range of applications with various Quality of Service (QoS) requirements for 5G-and-beyond networks. However, it is very difficult to employ a monolithic physical network to support various mMTC applications with differentiated QoS requirements. Moreover, in ultra-dense mobile networks, the scarcity of the preamble and Physical Downlink Control CHannel (PDCCH) resources may easily lead to resource collisions when a large number of devices access the network simultaneously. To tackle these issues, in this paper, we propose a network slicing-enabled intelligent random access framework for mMTC. First, by tailoring a gigantic physical network into multiple lightweight network slices, fine-grained QoS provisioning can be accomplished, and the collision domain of Random Access (RA) can be effectively reduced. In addition, we propose a novel concept of sliced preambles (sPreambles), based on which the transitional RA procedure is optimized, and the issue of preamble shortage is effectively relieved. Furthermore, with the aim of alleviating PDCCH resource shortage and improving transmission efficiency, we propose a learning-based resource-sharing scheme that can intelligently multiplex the PDCCH resources in the naturally dynamic environment. Simulation results show that the proposed framework can efficiently allocate resources to individual mMTC devices while guaranteeing their QoS requirements in random access processes.

This paper explores sensing coverage in integrated sensing and communication (ISAC) networks, which are gaining traction in 6G due to enhanced sensing capabilities. We address the optimization of sensing performance as an unconstrained binary quadratic programming problem, taking into account the mutual interference among base stations. Optimization solutions are then developed for homogeneous and heterogeneous networks. By leveraging numerical results, we ascertain the sensing coverage for a given network topology, guiding the decision of whether to employ cooperative sensing and which base stations should be involved in sensing specific areas. Additionally, the results demonstrate the near optimality of collaborations involving only a few base stations.

The emerging paradigm of perceptive networks has attracted much recent attention to the integration of communication and sensing functionalities. In this paper, we mainly consider an integrated sensing and communication (ISAC) system comprised of a target and multiple energy harvesting (EH) enabled low-power sensors. To support a monitoring task by charging the sensors with radiated radio-frequency power while keeping sensing the target, a transmit beamforming-based scheme is designed for focusing energy in the directions of sensors and the target. Given a quality-of-service (QoS) constraint on the sensing task, a unified stochastic scheduling scheme is presented to assure the freshness of the sensor information at a monitor. Under the stochastic scheduling scheme, we minimize the average Age of Information (AoI) of all sensors by dynamically adjusting transmission power and beamforming weights in use. Given a power constraint, the optimal scheduling policy is obtained by a constrained Markov Decision Process (CMDP) approach. More specifically, an optimal tradeoff between the average power and average AoI is revealed through linear programming (LP) formulation, which is further demonstrated by extensive numerical results.

In this article, we propose a cell-free network architecture for an unmanned aerial vehicle (UAV) base station (BS), i.e., UBS, incorporating high-altitude platform stations (HAPSs) as central processing units (CPUs). The goal is to guarantee the quality of service (QoS) of user equipment (UE), reduce energy consumption, extend communication time, and facilitate rescue operations. The millimeter-wave (mmWave) frequency band is deployed in access and backhaul links to satisfy UE QoS requirements and high backhaul demands. The proposed framework jointly optimizes user association, backhaul bandwidth allocation, and power allocation to maximize energy efficiency while meeting QoS requirements. The optimization problem, modeled as non-convex mixed-integer nonlinear fractional programming, is solved through a three-stage iterative algorithm. This includes (1) optimizing power allocation based on Dinkelbach transformation and a successive convex approximation (SCA) method, (2) clustering UBSs using the Lagrangian method, and (3) deriving a closed-form bandwidth allocation factor. The proposed algorithm significantly outperforms many traditional algorithms in performance while maintaining low computational complexity.

This paper presents a cognitive radio (CR)-enabled symbiotic ambient backscatter communication (AmBC) system with the help of a zero-energy reconfigurable intelligent surface (zeRIS). An adaptive transmission (AT) strategy for the zeRIS is devised based on the amount of harvested energy. Specifically, when energy reserve is insufficient, the zeRIS merely reflects signals without any phase adjustments (PAs), whereas under sufficient energy conditions, it reflects signals following precise PAs. Moreover, a segmented zeRIS is adopted by taking primary transmission (PT) and backscatter transmission (BT) into account. Following this, the coexistence outage probability and ergodic capacity are derived to assess the reliability and effectiveness of the proposed model, respectively. Their asymptotic performance is analyzed to gain insightful observations. Finally, simulation results are provided to verify the accuracy of the theoretical analysis, confirming that AT offers improved reliability, system rate, and energy efficiency over non-adaptive transmission. Furthermore, CR-aided AT demonstrates superior energy efficiency compared to non-CR-assisted AT. It is also crucial to note that the allocation of reflective elements between PT and BT must be reasonably managed to satisfy specific system requirements.