In congested Wi-Fi, sending less does not guarantee lower latency. Based on measurements on commodity Wi-Fi routers in the wild, sparse microflows can suffer bulk-like tail latency even at negligible load. This latency is driven by MAC-level contention dynamics rather than a flow's sending rate, rendering rate-based congestion control ineffective. We present Concord, a Wi-Fi MAC mechanism that makes burst airtime an explicit control signal and penalizes excessive medium holding. Concord operates entirely within the Wi-Fi driver, requires no flow classification, no client or protocol changes, and incurs only O(1) work per burst. Concord shows that controlling medium-holding time, rather than transmission rate, is key to tail latency in WLANs. With four saturated downlink contenders, Concord reduces the 99.9th-percentile enqueue-to-ACK latency of 100 B microflows from 298/461 ms (IEEE baseline / vendor bursting) to 42 ms without sacrificing bulk throughput. For interactive workloads (cloud gaming), it cuts 99.9th-percentile latency from 231/441 ms to 92 ms and reduces starvation by up to 10× versus the default IEEE stack.

Semantic communication (SemCom) has recently emerged as a transformative paradigm for next-generation wireless systems, aiming to enable task-oriented information transmission. However, existing SemCom networks rely on cell-centric architectures, where users, particularly those at the cell edge, suffer from severe inter-cell interference and poor channel conditions. These issues undermine semantic accuracy, induce intolerable delay, and ultimately hinder the fulfillment of quality-of-service (QoS) requirements. To overcome these limitations, we propose the user-centric SemCom (UCSC) network, a novel architecture that integrates user-centric networks with semantic-aware transmission. In UCSC, each user is served by a dedicated cooperative access point group, enabling effective interference mitigation and reliable semantic delivery, even at the cell edge. To fully exploit the potential of UCSC, we formulate a joint optimization problem for semantic information extraction and resource allocation, aiming to minimize total energy consumption under constraints on semantic similarity and delay. By decomposing the original problem into three tractable subproblems, semantic information extraction, computational capacity control, and power allocation, we propose an alternating optimization algorithm to efficiently solve it. Simulation results demonstrate that UCSC, combined with the proposed algorithm, achieves significant performance improvements in reducing energy consumption compared with cell-centric SemCom networks, especially under stringent QoS requirements.

The limited-capacity fronthaul link between access points (APs) and central processing unit (CPU), along with signal distortions caused by hardware impairments at users and APs, lead to unreliable transmission, hindering the achievement of ultra-reliable low-latency communication (URLLC) in cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems. In this paper, we investigate the performance of URLLC in CF mMIMO systems with limited fronthaul capacity and hardware impairments. First, a low-complexity fronthaul rate allocation scheme is proposed using rate-distortion theory in the considered system. Then, we derive the distribution of signal-to-interference-plus noise ratio (SINR) and analyze the delay violation probability (DVP) based on stochastic network calculus. Furthermore, a closed-form upper bound expression for DVP is also derived. To fulfill the requirements of next-generation URLLC, which demand additional key performance indicators, we further investigate effective energy efficiency and derive a closed-form lower bound, defined as the ratio of effective capacity to total power consumption. Extensive numerical results verify the accuracy of our derivations and indicate that a large deployment of APs can compensate for the reliability degradation caused by limited fronthaul capacity and hardware impairments. Additionally, the number of APs must be appropriately configured to ensure low latency and high reliability while avoiding energy waste.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) has been considered as a potential technology for Beyond 5G. However, the performance of CF mMIMO systems has not been thoroughly studied. Most existing analytical studies on CF mMIMO systems rely on deriving average performance metrics. The statistical characteristics of the signal-to-interference-plus-noise ratio (SINR), which capture the tail behavior of SINR, are crucial for metrics such as outage probability and for emerging mission-critical applications that emphasize extreme and rare events, but have not been thoroughly investigated. In this paper, we aim to obtain the distribution of SINR in CF mMIMO systems. Considering a downlink CF mMIMO system with pilot contamination, we first give the closed-form expression of the SINR. Based on our analytical work on the two components of the SINR, i.e., desired signal and interference-plus-noise, we then derive the probability density function and cumulative distribution function of the SINR under maximum ratio transmission (MRT) and full-pilot zero-forcing (FZF) precoding, respectively. Subsequently, the closed-form expressions for two more sophisticated performance metrics, i.e., ergodic rate and outage probability, are obtained. Finally, we perform Monte Carlo simulations to validate our analytical work. Numerous numerical results demonstrate the effectiveness of the derived SINR distribution, ergodic rate, and outage probability.

Inter-user interference and pilot contamination are the major obstacles limiting the performance of cell-free massive multiple-input multiple-output (CF mMIMO)-aided ultra-reliable low-latency communication (URLLC) systems. In this paper, user grouping is utilized to address these issues, by allocating users to groups based on frequency band division, preventing interference among users within different groups, and eliminating pilot contamination between different groups. We consider an uplink CF mMIMO-aided URLLC system with user grouping and derive the lower bound for the ergodic rate. Due to the limited blocklength of each group necessitating pilot reuse, a weight sum rate (WSR) maximum problem is formulated by jointly optimizing user grouping, pilot assignment, and power control. We propose a user grouping scheme based on graph theory, where iteratively searching for specific negative loops in the weighted directed graph can approach the optimal user grouping matrix. As the user grouping matrix updates at each iteration, we update the pilot assignment matrix based on graph theory and employ logarithmic function approximation and fractional programming for power control updates. Numerous numerical results demonstrate the effectiveness of user grouping, and the proposed algorithm improves WSR by 25% compared to the non-grouping algorithm, while outperforming other benchmark algorithms.

As a new development of Internet of Things (IoT), Internet of Video Things (IoVT) emerges to provide novel services based on video sensing, transmission, storage and analysis. However, IoVT also imposes massive computation and transmission on mobile edge computing (MEC) systems with a large amount of offloaded video data. To address this issue, in this paper, we propose a non-orthogonal multiple access (NOMA) assisted multi-MEC IoVT system. Although NOMA-assisted MEC systems have been proposed in existing studies, non-negligible inter-device interference caused by NOMA transmission has not been investigated, which is much more serious in multi-MEC IoVT systems. To combat them, we perform joint optimization on grouping and offloading in the proposed NOMA-assisted multi-MEC IoVT system. To achieve optimal performance, a utility minimization problem is formulated where the definition of utility leverages critical performance metrics including energy consumption and delay. We prove that this problem can be modeled as an exact potential game. Then, a selection algorithm is proposed to find the optimal strategies for each IoVT device by obtaining the Nash equilibrium. Specifically, power allocation for IoVT devices can be handled by a computation resource minimization problem. Simulation results demonstrate that the proposed algorithm achieves significant performance gains compared with existing schemes, i.e., at least 34.8% reduction in total utility and 14.5% less power consumption.

To relieve the stress on channel estimation and decoding complexity in cell-free massive multiple-input multiple-output (MIMO) systems, user grouping problem is investigated in this paper, where access points (APs) based on time-division duplex (TDD) are considered to serve users on different time resources and the same frequency resource. In addition, when quality of service (QoS) requirements are considered, widely-used max-min power control is no longer applicable. We derive the minimum power constraints under diverse QoS requirements considering user grouping. Based on the analysis, we formulate the joint power and user grouping problem under QoS constraints, aiming at minimizing the total transmit power. A generalized benders decomposition (GBD) based algorithm is proposed, where the primal problem and master problem are solved iteratively to approach the optimal solution. Simulation results demonstrate that by user grouping, the number of users served in cell-free MIMO systems can be as much as the number of APs without increasing the complexity of channel estimation and decoding. Furthermore, with the proposed user grouping strategy, the power consumption can be reduced by 2–3 dB compared with the reference user grouping strategy, and by 7 dB compared with the total transmit power without grouping.

In network function virtualization enabled networks with dynamic traffic, virtual network function (VNF) migration has been considered as an effective way to improve quality of service as well as resource utilization. However, due to time-varying network traffic, designing a fast and accurate VNF migration algorithm is still a great challenge. To address this issue, in this paper, we exploit the temporal convolutional network (TCN) to predict traffic flow for VNF migration decision in a fast and accurate manner. Based on the predicted results, we define a metric, i.e., migration index, to represent the load trend of each node in the network. A fast and efficient heuristic VNF migration algorithm is then proposed based on the migration index, with the goal to minimize the total migration cost in a time period. Extensive simulations are carried out to validate the effectiveness of TCN for traffic prediction. The results demonstrate that the proposed VNF migration algorithm can reduce the total migration cost up to 20% compared with existing algorithms.

In multi-cell non-orthogonal multiple access (NOMA) systems, designing an appropriate user grouping strategy is an open problem due to diverse quality of service (QoS) requirements and inter-cell interference. In this paper, we exploit both game theory and graph theory to study QoS-aware user grouping strategies, aiming at minimizing power consumption in downlink multi-cell NOMA systems. Under different QoS requirements, we derive the optimal successive interference cancellation (SIC) decoding order with inter-cell interference, which is different from existing SIC decoding order of increasing channel gains, and obtain the corresponding power allocation strategy. Based on this, the exact potential game model of the user grouping strategies adopted by multiple cells is formulated. We prove that, in this game, the problem for each player to find a grouping strategy can be converted into the problem of searching for specific negative loops in the graph composed of users. Bellman-Ford algorithm is expanded to find these negative loops. Furthermore, we design a greedy based suboptimal strategy to approach the optimal solution with polynomial time. Extensive simulations confirm the effectiveness of grouping users with consideration of QoS and inter-cell interference, and show that the proposed strategies can considerably reduce total power consumption comparing with reference strategies.

To alleviate severe inter-user interference in Non-orthogonal multiple access (NOMA) systems, in practice, several users are grouped together for NOMA transmission and orthogonal resources are allocated among different groups. In this paper, we investigate uplink NOMA scenarios with multiple base stations, where user association is jointly considered with user grouping. It is worth noting that a user prefers to be associated with the base station where it can be assigned into a group with less inter-user interference. Furthermore, different users have different quality-of-service (QoS) requirements. Based on these observations, we firstly quantitatively analyze the extra transmit power that a user brings to the others in the same group under QoS constraints, i.e., externalities, and derive an externality function to indicate interference among users in uplink NOMA. Then, we obtain a game model for the joint user association, grouping and power allocation (JAGP) problem in uplink NOMA systems and prove that it is an exact potential game, with the goal to minimize total transmit power. Furthermore, we prove the existence of Nash Equilibrium (NE) as well as the finite improvement property (FIP) of this game. In addition, the NE of this game is proved to be Pareto-optimal. A heuristic algorithm is proposed to find the solution to the JAGP problem by achieving the NE of the proposed game, which convergence is guaranteed by FIP. Simulation results show that the proposed algorithm can considerably reduce the total transmit power compared with existing schemes.

To meet the performance and complexity requirements from practical deployment of non-orthogonal multiple access (NOMA) systems, several users are grouped together for NOMA transmission while orthogonal resources are allocated among groups. User grouping strategies have significant impact on the power consumption and system performance. However, existing related studies divide users into groups based on channel conditions, where diverse quality of service (QoS) and interference have not been considered. In this paper, we focus on the interference-aware user grouping strategy in NOMA systems, aiming at minimizing power consumption with QoS constraints. We define a power consumption and externality (PCE) function for each user to represent the power consumption involved by this user to satisfy its QoS requirement as well as interference that this user brings to others in the same group. Then, we extend the definition of PCE to multi-user scenarios and convert the user grouping problem into the problem of searching for specific negative loops in the graph. Bellman-Ford algorithm is extended to find these negative loops. Furthermore, a greedy suboptimal algorithm is proposed to approach the solution within polynomial time. Simulation results show that the proposed algorithms can considerably reduce the total power consumption compared with existing strategies.

With the explosive increment in mobile users and mobile devices, nowadays network infrastructures are foreseen to be much more struggle in handling the vast mobile video traffic in the network. To meet this challenge, small-cell base stations (SBSs) are introduced in heterogeneous networks (HetNets) to support high data rate services in the fronthaul. However, HetNets still suffer from backhaul bandwidth shortage. Caching is a promising way to relieve the bandwidth burden on the backhaul. However it may fail in improving user's quality of experience (QoE) in video services. The reason is that, without a corresponding power allocation strategy in the fronthaul, users are unlikely to be allocated sufficient power to receive the higher quality version of videos, which in return leads to the waste of the limited caching space. On the other hand, the distinct layered feature of scalable video encoding (SVC) videos allows us to do layer-wise caching to further promote the cache utilization efficiency. Thereupon, a nature idea is to combine the power allocation strategy and the layer-wise caching strategy to effectively utilize the limited power and cache space of SBSs, so that the limited bandwidth of backhaul can be saved while user's QoE can be improved. In this work, we take the layered feature of SVC videos into account to jointly optimize these two strategies, aiming to maximize user's QoE. A simulated annealing based algorithm is proposed to solve the formulated problem. Simulation results show that, by doing so, the cache-hit ratio is increased while the limited backhaul bandwidth can be saved. Moreover, user's QoE has been significantly improved compared to traditional caching strategies.