The fundamental capacity limits of pinching-antenna systems (PASS)-based multi-user communication network are investigated. Two practical pinching-antenna configuration strategies are considered, namelymultiple-time discrete activationandone-off continuous sliding. For each strategy, the capacity and rate regions are characterized under the non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes, respectively. 1) For NOMA, the capacity region achieved by the discrete activation is first characterized. In particular, the ideal case with the infinite number of activation times is considered, where the optimal PA activation and resource allocation scheme is derived. It is shown that different user groups or decoding orders are served via time-sharing. Inspired by this result, an inner bound of capacity region is obtained for the practical case with a finite number of activation times. Then, for the continuous sliding case, the inner bound of capacity region is characterized by alternately optimizing the resource allocation and PAs’ continuous positions. 2) For OMA, the rate region is first obtained for the discrete activation case. It is unveiled that multiple users are successively served. Then, by alternately optimizing the resource allocation and PAs’ continuous positions, the inner bound of rate region is obtained for the continuous sliding case. Numerical results demonstrate that i) PASS can significantly improve the capacity performance compared with the conventional fixed-antenna systems; ii) the capacity gain can be further enhanced by using the proposed PA activation and sliding and resource allocation schemes; and iii) the continuous sliding has the potential to outperform the discrete activation in NOMA, whereas the latter performs better in OMA.

Users’ connection is pivotal for advancing meta-computing in Industrial Internet of Things (IIoT), where efficient data transmission can help overcome the barriers of computing resources distributed among billions of devices from diverse IIoT networks. Cooperative device-to-device (D2D) transmission, which is underlaid with cellular networks, plays an important role in enhancing the users’ connections for IIoT. However, since serving as a content provider in cooperative transmission needs consuming resources, IoT devices are typically willing to participate in cooperative transmission only when the content requesters are within the same IIoT networks. To encourage the cooperation across different IIoT networks, we in this article introduce social credit as a universal virtual concurrency to stimulate users’ willingness to assist content requesters via D2D transmission. Specifically, we first establish the relationship between social credit and data transmission, and model the process of credit acquisition and expenditure using a queue, which is initially unstable. Then, the inversely queuing technique is adopted to construct an equivalent stable queue, and a statistical credit guarantee mechanism is proposed to maintain cooperative transmission. Based on this mechanism, we explore the throughput maximization problem for cooperative D2D transmission in IIoT underlaying cellular networks, considering constraints on average and peak transmit power as well as interference to cellular communications, for obtaining the optimal credit-aware power control scheme. When multiple content providers are available, we prioritize the provider located at the shortest distance and examine the corresponding credit-aware power minimization problem, where the optimal power control scheme is obtained. Simulation results demonstrate that the proposal offers more stable credit guarantees than baseline methods, encouraging greater participation in cooperative transmission while achieving higher throughput and reducing transmit power consumption.

Age of information (AoI) is an effective measure to quantify the information freshness in wireless status update systems. It has been further validated that the peak AoI has the potential to capture the core characteristics of the aging process, and thus the average peak AoI is widely used to evaluate the long-term performance of information freshness. However, the average peak AoI is a risk-insensitive metric and therefore may not be well suited for evaluating critical status update services. Motivated by this concern, and following the spirit of entropic value-at-risk (EVaR) in the field of risk analysis, in this paper we present a concept, termed Statistical AoI, for providing a unified framework to guarantee various requirements of risk-sensitive status-update services with the demand on the violation probability of the peak age. In particular, as the constraint on the violation probability of the peak age varies from loose to strict, the statistical AoI evolves from the average peak AoI to the maximum peak AoI. We then investigate the statistical AoI minimization problem for status updates over wireless fading channels. It is interesting to note that the corresponding optimal sampling scheme varies from step to constant functions of the channel power gain with the peak age violation probability from one to zero. We also address the maximum statistical AoI minimization problem for multi-source status updates with time division multiple access (TDMA), where longer transmission time can improve reliability but may also cause the larger age. By solving this problem, we derive the optimal transmission time allocation scheme. Numerical results show that our proposals can better satisfy the diverse requirements of various risk-sensitive status update services, and demonstrate the great potential of improving information freshness compared to baseline approaches.

Status update applications are very common in the internet of drones (IoD), where some of status update information are privacy-sensitive. How these information can be efficiently and securely transmitted remains as an open challenging issue. Motivated by this concern, we in this paper consider using the transmit antenna selection (TAS) technique to guarantee secure status updates over IoD downlink networks, in which only the drones corresponding to the highest channel gain on each antenna can become the candidate receivers, and then the base station (BS) selects one antenna to transmit the associated status update information with wiretap coding to the corresponding candidate. The mobility of drones causes the channel gain is time-varying, and thus one natural question arises, i.e., what is the optimal antenna selection scheme in terms of the long-term network-wide freshness performance. To answer this question, the weighted sum of average age-of-information (AoI) minimization problem is formulated, and we propose a deep Q-learning-based antenna selection approach to solve it. It is worth mentioning that, in light of the try-error mechanism of Q-learning, no prior knowledge of the wireless environments is required for our proposal in contrast to the traditional schemes. Finally, the numerical results verify that our proposal can further reduce the weighted sum of AoI as compared with the state-of-the-art max-weight scheme.

Ultra-reliable and low-latency communications (URLLC) are one of main scenarios of the fifth-generation (5G) and beyond-5G wireless networks, where most existing work concentrated on the tailored resource allocation on the premise of known channel state information (CSI) to meet its strict requirements. However, sometimes due to the high mobility of transceivers, the instantaneous CSI is highly time-varying such that we cannot capture it in a timely manner. Motivated by this background, we in this paper investigate the provisioning of URLLC over unmanned-aerial-vehicle (UAV) networks in high-mobility environments, considering that only the distribution of CSI is available at the transmitter. The short-packet transmission is assumed to guarantee the low-latency requirement. The influences of channel outage probability and block error rate of short-packet transmissions are analyzed to evaluate and guarantee transmission reliability. Next, we delve into the transmit power minimization problem by firstly relaxing it to be convex and then devising the corresponding optimal solution with convex optimization techniques. The numerical results verify the superiority of our proposal.

Metaverse is envisioned to shape a virtual digital world accommodating people to live, work, and interact with each other, which requires massive information exchange for frequent updates of panoramic information in the digital world and imposes unprecedented pressure on future networks, so it is desirable to only sample the information updating process covering objects of user’s main attention. Yet under the always-limited wireless capacity compared to the persistently-growing information load, a critically important but unanswered question remains, i.e., how often shall we sample information updating process and deliver it to users, while keeping information at users’ side as fresh as possible. To answer this question, we investigate the statistical age-of-information (AoI) minimization problems to catch varying wireless channels and attention of users. Unlike conventional average or maximum AoI optimization technologies, we concentrate on statistical feature of AoI to more accurately characterize the capability of supporting metaverse applications. The formulated problems are solved by fractional programming. Specifically, using the Dinkelbach’s and quadratic transforms, we derive the adaptive sampling and transmission schemes for cases with single and multiple users, respectively. The interaction among multiple users is also considered. Analyses reveal that the optimized sampling rate shall decrease as the information updating process covers more varying objects or the channel gets poorer. Moreover, when the AoI requirement becomes extremely stringent, the sampling rate approaches a constant. Numerical results validate that our proposals can achieve lower statistical AoI than baseline schemes, thus offering better experiences for metaverse users.

With the ubiquitous connectivity in internet of vehicles, secure transmission is of vital importance for intelligent vehicle-to-everything (V2X) communications. However, in order to effectively guarantee the secrecy performance, once the short-term statistics of channel quality are low, which frequently happens because of the high mobility of vehicles, the throughput performance can be weakened by using the existing either cryptography techniques or physical-layer security (PLS) technique. To improve the throughput of secure data transmission, we propose the concept of statistical security, which utilizes the time-sensitive characteristics of information, such as the position of mobile vehicles, in V2X communications. Specifically, the time-sensitive information has its own outdated rate. As long as the information leakage rate is less than the information outdated rate, the eavesdropper still cannot obtain any useful information. Then, we use a queue system to characterize the eavesdropping process, where the queue-length violation probability indicates the level of secure transmission. Under this statistical security model, we investigate the throughput maximization problem and give the optimal power allocation scheme for the legitimate vehicular users. Furthermore, since the service’s security QoS requirements and wireless environments are both varying in V2X communications, we propose a unified cross-layer policy-adaption approach to guarantee the varying security QoS requirements meanwhile improving the throughput. Simulation results verify that our proposed approach significantly outperforms the existing baseline schemes.

With the very stringent demand for real-time transmission of wireless communication services, the requirement of sub-millisecond ultra-low end-to-end delay has been initially proposed in the sixth generation (6G) communication networks. Finite blocklength transmission is one of the potential technologies to meet such a low end-to-end delay demand for the next generation networks. However, as the finite blocklength decreases, the transmission delay decreases while the queuing delay increases, which results in the tradeoff between the transmission delay and the queuing delay. To achieve the optimal balance, in this paper we propose an adaptive blocklength transmission framework to minimize the important part of the end-to-end delay of wireless networks, where we focus on the transmission delay and queuing delay. A dynamic buffering model for variable transmission time interval (V-TTI) is introduced for the time-varying arrival of packets adaptation. Then, we propose the Flexible proximal Alternating direction method of multipliers based Blocklength Optimization (FaBo) scheme to minimize the important part of the end-to-end delay for the single user case. We also propose the Multiple deep Q-learning network based Resource Allocation (MuRa) scheme, which can efficiently balance the transmission delay and queuing delay, to minimize the important part of the end-to-end delay for the multi-user case. Numerical results show that the proposed adaptive blocklength framework can reduce the important part of the end-to-end delay compared with that of long-term evolution and the fifth generation (5G) new radio. We also show that our proposed schemes can quickly converge to the minimum end-to-end delay.