Sensing and communication sharing the same hardware and frequency band has become a key technology for future 5G/6G systems to achieve high-performance integrated sensing and communication (ISAC), which has attracted a great deal of attention in both academia and industry. In this letter, a device-free ISAC system based on distributed unmanned aerial vehicles (UAVs) is designed to realize the sensing of vehicles on urban roads and transmit information to base stations through wireless communication. In order to realize autonomous decision-making of UAVs and solve the problem of stochastic optimization and multi-index tradeoffs caused by a time-varying environment, we propose an algorithm based on multi-agent reinforcement learning (MARL). The simulation results verify that the proposed scheme can significantly improve the sensing performance of the UAVs and ensure communication quality with base stations. Besides, we realize the sensing fairness for users, the flight safety of UAVs, and can save energy through hyperparameter tuning.

The emerging integrated sensing and communication paradigm has gained considerable recent attention due to its potential for supporting sensing and communication services simultaneously. To provide low-latency communication services for multiple communication users while sensing an unmanned aerial vehicle simultaneously, a non-orthogonal multiple access (NOMA) empowered integrated sensing and communication (ISAC) system is developed in this paper. To support the NOMA-empowered ISAC system efficiently, a beamforming-based cross-layer scheduling scheme is designed, which exploits the traffic dynamics information at the network layer and channel state information at the physical layer. Under the cross-layer scheduling scheme, an efficient joint beamforming, power allocation, and rate adaptation strategy is obtained via a developed Newton's method-based algorithm and the linear programming formulation. With the aid of the proposed strategy, the average delay in the communication task can be minimized while assuring sensing capability. Besides, an optimal tradeoff between the average delay and the average transmission power in the NOMA-empowered ISAC system is uncovered, which is further demonstrated by extensive numerical results.

In order to tackle the explosive growth of data traffic and the number of terminals, 5G heterogeneous network (HetNet) has become an important evolution direction of 5G networking architecture. Densely deployed micro base stations (gNBs) in 5G HetNets will dramatically increase the handover frequency of user equipment (UE), resulting in more handover failures, and seriously reducing the user experience of mobile UE. Aiming at tackling this problem, this paper proposes a beam enhancement handover scheme with an adaptive threshold. Firstly, different beamforming gains are configured for the mobile UE in the overlapping area of two gNBs to improve the signal strength received by the UE at the edge of gNB coverage. Secondly, for mobile UE with different speeds, adaptive handover decision parameters are configured, and reference signal receiving strength (RSRP) as well as reference signal receiving quality (RSRQ) are used for joint handover decisions to achieve reliable handover. The simulation results verify that the proposed scheme can effectively improve the signal strength of the edge area, and the adaptive joint handover decision algorithm based on UE speed can also effectively improve the handover success probability.

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.

With the exponential growth of user equipment (UE) access in future networks, service operators are deploying many low-cost and low-power heterogeneous base stations. However, when considering cooperative multi-point (CoMP) based soft handover schemes, there will be a trade-off between user quality of service (QoS), system power consumption, and handover frequency. Therefore, in this letter, we proposed a CoMP-based energy-efficient intelligent handover algorithm, named CEH-PPO in heterogeneous cellular networks. The proposed scheme is based on the Proximal Policy Optimization (PPO) algorithm in deep reinforcement learning. The optimization objective is to maximize the average user rates and minimize energy consumption and handover frequency in the CoMP-based heterogeneous network. Thus, we can dynamically select the transmission base station set for UEs to accomplish the handover procedure. Simulation results demonstrate that the proposed CEH-PPO algorithm simultaneously improves the average QoS of UEs in the system and reduces the overall handover frequency while reducing the overall power consumption.

The Internet of Things (IoT) is expected to foster the development of 5G wireless networks and requires the efficient support for a large number of simultaneous short message communications. To address these challenges, some existing works utilize new waveform and multiuser superposition transmission schemes to improve the capacity of IoT communication. In this paper, we will investigate the spatial degree of freedom of IoT devices based on their distribution, then extend the multiuser shared access (MUSA) which is one of the typical MUST schemes to spatial domain, and propose two novel schemes, that is, the preconfigured access scheme and the joint spatial and code domain scheduling scheme, to enhance IoT communication. The results indicate that the proposed schemes can reduce the collision rate dramatically during the IoT random access procedure and improve the performance of IoT communication obviously. Based on the simulation results, it is also shown that the proposed scheduling scheme can achieve the similar performance to the corresponding brute-force scheduling but with lower complexity.