Massive connections of diverse mission-critical devices have sparked people’s envisioning for next-generation ultra-reliable and low-latency communications (xURLLC), prompting the design of customized next-generation advanced transceivers (NGAT). Rate-splitting multiple access (RSMA) has emerged as a pivotal technology for NGAT design, given its robustness to imperfect channel state information (CSI) and resilience to quality of service (QoS). Additionally, xURLLC urgently necessitates large-scale access techniques, thus massive multiple-input multiple-output (mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper, we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC) framework tailored to accommodate xURLLC’s critical QoS constraints in finite blocklength (FBL) regimes. Leveraging uplink pilot training under imperfect CSI at the transmitter, we estimate channel gains and customize linear precoders for efficient downlink short-packet data transmission. Subsequently, we formulate a joint rate-splitting, beamforming, and transmit antenna selection optimization problem to maximize the total effective transmission rate (ETR). Addressing this multi-variable coupled non-convex problem, we decompose it into three corresponding subproblems and propose a low-complexity alternating optimization algorithm for efficient optimization. Extensive simulations demonstrate that compared with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), the developed architecture accommodates larger-scale access and improves total ETR by 15.3% and 41.91%, respectively.

Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation and serious edge effect in traditional cellular-based edge service caching networks, in this paper, we proposed a novel user-centric edge service caching framework where each user is jointly provided with edge caching and wireless transmission services by a specific BS cluster instead of a single BS. To minimize the long-term average delay under the constraint of the caching cost, a mixed integer non-linear programming (MINLP) problem is formulated by jointly optimizing the BS clustering and service caching decisions. To tackle the problem, we propose JO-CDSD, an efficiently joint optimization algorithm based on Lyapunov optimization and generalized benders decomposition (GBD). In particular, the long-term optimization problem can be transformed into a primal problem and a master problem in each time slot that is much simpler to solve. The near-optimal clustering and caching strategy can be obtained through solving the primal and master problem alternately. Extensive simulations show that the proposed joint optimization algorithm outperforms other algorithms and can effectively reduce the long-term delay and caching cost.

In this paper, fundamentals and performance tradeoffs of next-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC). An xURLLC-enabled massive MU-MIMO system model has been developed to accommodate xURLLC features. By leveraging and promoting SNC, we provide a quantitative statistical quality of service (QoS) provisioning analysis and derive the closed-form expression of upper-bounded statistical delay violation probability (UB-SDVP). Based on the proposed theoretical framework, we formulate the UB-SDVP minimization problem, which is first degenerated into a one-dimensional integer-search problem by deriving the minimum error probability (EP) detector, and then efficiently solved by the integer-form Golden-Section search algorithm. Moreover, two novel concepts, EP-based effective capacity (EP-EC) and EP-based energy efficiency (EP-EE), have been defined to characterize the tail distributions and performance tradeoffs for xURLLC. Subsequently, we formulate the EP-EC and EP-EE maximization problems, and the EP-EC maximization problem is proven to be equivalent to the UB-SDVP minimization problem, while the EP-EE maximization problem is solved with a low-complexity outer-descent inner-search collaborative algorithm. Extensive simulations demonstrate that the proposed framework can reduce computational complexity compared to reference schemes and provide various tradeoffs and optimization performance of xURLLC concerning UB-SDVP, EP, EP-EC, and EP-EE.

Reconfigurable intelligent surface (RIS)-enhanced secure wireless system has captured widespread concern recently. However, there is still a lack of research on the evaluation of security threats from the illegal RIS (IRIS) deployed by malicious eavesdroppers. In this paper, we investigate the signal leakage brought by IRIS in typical RIS-enhanced millimeter wave (mmWave) multiple-input multiple-output (MIMO) wiretap system. We firstly illustrate the detriment and challenges of IRIS with respect to system secrecy rate (SR) competition. The existence of IRIS aggravates the difficulty in detecting behaviors of the eavesdropper, making it infeasible to globally solve the SR competition problem. Therefore, we propose an artificial noise (AN)-based interference scheme to moderate the security degradation caused by IRIS. Specifically, the transmit precoder, receiver combiner and RIS discrete phase shifts are jointly designed by exploiting the interpolation search method and sparsity of mmWave channels for the sake of generating more AN. Simulation results demonstrate the non-negligible SR degradation caused by IRIS and validate the effective security enhancement of the proposed scheme. Besides, we emphatically analyze specific system factors that should be weighed to better combat IRIS especially when IRIS is powerful.

In view of the stringent requirements for delay in 5G usage scenarios, the existing congestion control schemes based on terminal measurement and evaluation cannot meet the requirements due to their reactive nature and blindness to links. To this end, we propose a buffer state-driven rate control mechanism in 5G networks, namely active queue management (AQM) based buffer delay guarantee (AQM-BDG) to explicitly adjust the sending rate of the source. Specifically, the rate mismatch and buffer state in Radio Link Control (RLC) layer are comprehensively considered, which timely grasp the dynamics of the buffer to obtain the direction and urgency of rate adjustment. Then the incoming packets are marked proportionally as "increase" or "decrease" to adjust the sending rate. Meanwhile, the adjustment quantum is reallocated among the different Service Data Flows (SDFs) that are mapped to the same Data Radio Bearer (DRB), so that every SDF can obtain the throughput share according to the preset priority, no matter what rate it starts at. Experimental results demonstrate that AQM-BDG significantly outperforms classical congestion control schemes on network utility by achieving relatively high throughput while bounding queuing delay within the threshold.

User-centric network (UCN) has been considered as a promising technology to improve the throughput of users, especially users at the cell edge, where each user is connected to and served by a group of access points. With the rapid growth of user’s uplink traffic demands, energy consumption has become an important issue in uplink UCN as user’s battery capacity is limited. To address this issue, in this research, we develop a reconfigurable intelligent surface (RIS)-enhanced uplink UCN, where RIS is exploited to improve the signal quality of uplink transmission for users with virtually no extra energy consumption. To approach an optimal energy efficiency, we jointly perform reflect beamforming at RISs and uplink power control at users. This is formulated into a non-convex and intractable energy efficiency maximization problem. To solve it effectively, we decompose it into two subproblems and carry out the optimization alternately. That is, we design the reflect beamforming matrices at RISs through fractional programming and an uplink power control at users through constructing a surrogate function via majorization-minimization algorithm. Numerical results demonstrate that the proposed algorithm could achieve significant gain both on energy efficiency and spectral efficiency compared to the benchmark algorithm.

In millimeter wave heterogeneous networks with integrated access and backhaul (mABHetNets), a considerable part of spectrum resources are occupied by the backhaul link, which limits the performance of the access link. In order to overcome such backhaul “ spectrum occupancy ”, we introduce cache into mABHetNets. Caching popular files at small base stations (SBSs) can offload the backhaul traffic and transfer spectrum from the backhaul link to the access link. To approach the optimal performance of cache-enabled mABHetNets, we first analyze the signal-to-interference-plus-noise ratio distribution and derive the average potential throughput (APT) expression by stochastic geometric tools. Then, based on our analytical work, we formulate a joint optimization problem of cache decision and spectrum partition to maximize the APT. Inspired by the block coordinate descent (BCD) method, we propose a joint cache decision, spectrum partition and power allocation (JCSPA) algorithm to approach the optimal solution. Numerical results show the effectiveness and enhancement of the proposed algorithm. Besides, we verify the APT under different parameters and find that the introduction of cache facilitates the transfer of backhaul spectrum to access link. Jointly deploying appropriate caching capacity at SBSs and performing specified spectrum partition can bring up about 90% APT gain in mABHetNets.

Recently, a mmWave-based access and backhaul integration heterogeneous networks (HetNets) architecture (mABHetNets) has been envisioned to provide high wireless capacity. Since the access link and the backhaul link share the same mm-wave spectral resource, a large spectrum bandwidth is occupied by the backhaul link, which hinders the wireless access capacity improvement. To overcome the backhaul spectrum occupation problem and improve the network throughput in the existing mABHetNets, we introduce the cache at base stations (BSs). In detail, by caching popular files at small base stations (SBSs), mABHetNets can offload the backhaul link traffic and transfer the redundant backhaul spectrum to the access link to increase the network throughput. However, introducing cache in SBSs will also incur additional power consumption and reduce the transmission power, which can lower the network throughput. In this paper, we investigate spectrum partition between the access link and the backhaul link as well as cache allocation to improve the network throughput in mABHetNets. With the stochastic geometry tool, we develop an analytical framework to characterize cache-enabled mABHetNets and obtain the signal-to-interference-plus-noise ratio (SINR) distributions in line-of-sight (LoS) and non-line-of-sight (NLoS) paths. Then we utilize the SINR distribution to derive the average potential throughput (APT). Extensive numerical results show that introducing cache can bring up to 80% APT gain to the existing mABHetNets.