Efficient power allocation and interference management are critical challenges in dynamic wireless communication systems. To address these challenges, graph neural networks (GNNs) have attracted significant attention, while knowledge graph further enhance this capability by representing structured interactions among entities. This article proposes the Power-focused Knowledge Graph Convolutional Network (PKGCN), a novel framework utilizing knowledge graph driven learning to model and optimize power allocation strategies. By integrating wireless-specific features such as channel conditions and interference metrics, PKGCN effectively captures the complex interactions and dependencies among network nodes. This model employs a message aggregation layer to extract local and global interactions and a power prediction layer to optimize resource allocation. Comprehensive evaluations reveal that PKGCN de-livers higher average user rates, lower interference levels, and greater robustness.

Multi-access edge computing (MEC) emerges as an effective computational paradigm. It meets the low latency and low energy consumption requirements of users by enabling user terminals (UE) to offload their computationally intensive applications to nearby access points (AP). However, the integration of Virtual Reality (VR) applications within MEC environments remains underexplored, primarily due to their structural complexity and high computational requirements for offloading and scheduling. To address these challenges, we use Directed Acyclic Graph (DAG) to model VR applications and 6G-oriented Rate-Splitting Multiple Access (RSMA) to enhance offloading and scheduling processes of VR applications. The objective is to jointly optimize the offloading strategy, transmit power, RSMA decoding order, and UE application scheduling to minimize the total system cost. Recognizing the limitations of traditional learning-based methods, which struggle with convergence in multi-user scenarios, we decompose the optimization problem into two subproblems: application offloading and application scheduling. We then propose the PPOCO algorithm, which integrates reinforcement learning with convex optimization to effectively solve these subproblems independently. Experimental results demonstrate that our proposed method consistently out-performs baseline algorithms in reducing the total system cost across various MEC network configurations.

Immersive communication, including emerging augmented reality, virtual reality, and holographic telepresence, has been identified as a key service for enabling next-generation wireless applications. To align with legacy wireless applications, such as enhanced mobile broadband or ultra-reliable low-latency communication, network slicing has been widely adopted. However, attempting to statistically isolate the above types of wireless applications through different network slices may lead to throughput degradation and increased queue backlog. To address these challenges, we establish a unified QoS-aware framework that supports immersive communication and legacy wireless applications simultaneously. Based on the Lyapunov drift theorem, we transform the original long-term throughput maximization problem into an equivalent short-term throughput maximization weighted by virtual queue length. Moreover, to cope with the challenges introduced by the interaction between large-timescale network slicing and short-timescale resource allocation, we propose an adaptive adversarial slicing (Ad2S) scheme for networks with invarying channel statistics. To track the network channel variations, we also propose a measurement extrapolation-Kalman filter (ME-KF)-based method and refine our scheme into Ad2S-non-stationary refinement (Ad2S-NR). Through extended numerical examples, we demonstrate that our proposed schemes achieve 3.86 Mbps throughput improvement and 63.96% latency reduction with 24.36% convergence time reduction. Within our framework, the trade-off between total throughput and user service experience can be achieved by tuning systematic parameters.

Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this article, we developed a distributed queue model for multitask deep neural network inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided proximal policy optimization algorithm, named LyaPPO. Through numerical results, we show that our LyaPPO algorithm outperforms the baseline algorithms. Specifically, under different maximum local computational capacities, the proposed algorithm decreases 24.29%–56.62% energy compared to the suboptimal baselines.

This paper presents a novel two-stage deep reinforcement learning (DRL) algorithm built on a Transformer Encoder-based Deep Deterministic Policy Gradient (TEDDPG) framework, named TS-TEDDPG, which jointly optimizes the learning latency, energy consumption and model accuracy of Asynchronous Federated Learning (AFL) systems with prescribed security. The CPU configuration of local training and the transmit power of model uploading are learnt by the TEDDPG in the first stage. A linear programming-based device scheduling and cooperative jamming strategy is designed to efficiently optimize the rest of the decisions in the second stage and evaluates the immediate reward to train the TEDDPG. Experimental results based on a CNN model and the MNIST dataset demonstrate that the proposed TS-TEDDPG can reduce the training latency and energy consumption by 68.6% compared to its benchmarks, when the required test accuracy is 0.9.

In Network Slicing (NS), the hard RAN slicing interactions with the physical transmission environment have been explored predominantly, with limited consideration given to the interaction between slicing and existing transmission protocols. To address this gap, we propose a novel cross-slice re-transmission protocol integrated with NS, aiming to maximize throughput while ensuring latency constraints. Our protocol introduces adaptive re-transmission parameters, cross-slicing retransmission, and flexible duplexing mode switching. We model the problem as a bi-level optimization framework and propose a nested Hungarian-based reinforcement learning algorithm for optimization. Extensive experiments demonstrate the proposed protocol and algorithm's superiority in throughput and latency performance. Additionally, we investigate the impact of user count on throughput, providing configuration recommendations for optimal performance.

In order to provide design guidelines for energy efficient 6G networks, we propose a novel radial basis function (RBF) based optimization framework to maximize the integrated relative energy efficiency (IREE) metric. Different from the conventional energy efficient optimization schemes, we maximize the transformed utility for any given IREE using spectrum efficiency oriented RBF network and gradually update the IREE metric using proposed Dinkelbach’s algorithm. The existence and uniqueness properties of RBF networks are provided, and the convergence conditions of the entire framework are discussed as well. Through some numerical experiments, we show that the proposed IREE outperforms many existing SE or EE oriented designs and find a new Jensen-Shannon (JS) divergence constrained region, which behaves differently from the conventional EE-SE region. Meanwhile, by studying IREE-SE trade-offs under different traffic requirements, we suggest that network operators shall spend more efforts to balance the distributions of traffic demands and network capacities in order to improve the IREE performance, especially when the spatial variations of the traffic distribution are significant.

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling, especially when it comes to clients relying on energy harvesting to power their operations. This paper presents a new two-phase deep deterministic policy gradient (DDPG) framework, referred to as “TP-DDPG”, to balance online the learning delay and model accuracy of an FL process in an energy harvesting-powered HFL system. The key idea is that we divide optimization decisions into two groups, and employ DDPG to learn one group in the first phase, while interpreting the other group as part of the environment to provide rewards for training the DDPG in the second phase. Specifically, the DDPG learns the selection of participating clients, and their CPU configurations and the transmission powers. A new straggler-aware client association and bandwidth allocation (SCABA) algorithm efficiently optimizes the other decisions and evaluates the reward for the DDPG. Experiments demonstrate that with substantially reduced number of learnable parameters, the TP-DDPG can quickly converge to effective polices that can shorten the training time of HFL by 39.4% compared to its benchmarks, when the required test accuracy of HFL is 0.9.

Extended reality (XR) is one of the most important applications of beyond 5G and 6G networks. Real-time XR video transmission presents challenges in terms of data rate and delay. In particular, the frame-by-frame transmission mode of XR video makes real-time XR video very sensitive to dynamic network environments. To improve the users’ quality of experience (QoE), we design a cross-layer transmission framework for real-time XR video. The proposed framework allows the simple information exchange between the base station (BS) and the XR server, which assists in adaptive bitrate and wireless resource scheduling. We utilize the cross-layer information to formulate the problem of maximizing user QoE by finding the optimal scheduling and bitrate adjustment strategies. To address the issue of mismatched time scales between two strategies, we decouple the original problem and solve them individually using a multi-agent-based approach. Specifically, we propose the multi-step Deep Q-network (MS-DQN) algorithm to obtain a frame-priority-based wireless resource scheduling strategy and then propose the Transformer-based Proximal Policy Optimization (TPPO) algorithm for video bitrate adaptation. The experimental results show that the TPPO+MS-DQN algorithm proposed in this study can improve the QoE by 3.6% to 37.8%. More specifically, the proposed MS-DQN algorithm enhances the transmission quality by 49.9%-80.2%.

The advent of online video services has resulted in a remarkable surge in Internet traffic, prompting the need for mobile edge computing (MEC) as a crucial element in augmenting the quality of adaptive streaming media services amidst the time-varying wireless channels. MEC reduces network backhaul traffic by providing video transcoding and adaptive streaming services closer to users. Nonetheless, the process of video transcoding introduces additional latency and energy consumption. In order to effectively tackle this challenge and uphold the optimal quality of experience (QoE), we propose the Joint Bitrate Transcoding and Parallel Cooperative Transmission (JBTPCT) model, which operates at the edge of mobile networks and handles multiple video chunks simultaneously. Within the JBTPCT model, the Asynchronous Advantage Actor-Critic (A3C) algorithm framework is employed to jointly account for radio access network conditions and MEC resources, leveraging a parallel execution strategy for transmission and transcoding. This integrated approach aims to minimize both latency and energy consumption while enhancing the QoE of video streaming. We evaluate the average QoE of JBTPCT in different network scenarios, and the experimental results demonstrate that JBTPCT consistently achieves higher average QoE compared to competing algorithms.

Cooperative inference in Mobile Edge Computing (MEC), achieved by deploying partitioned Deep Neural Network (DNN) models between resource-constrained user equipments (UEs) and edge servers (ESs), has emerged as a promising paradigm. Firstly, we consider scenarios of continuous Artificial Intelligence (AI) task arrivals, like the object detection for video streams, and utilize a serial queuing model for the accurate evaluation of End-to-End (E2E) delay in cooperative edge inference. Secondly, to enhance the long-term performance of inference systems, we formulate a multi-slot stochastic E2E delay optimization problem that jointly considers model partitioning and multi-dimensional resource allocation. Finally, to solve this problem, we introduce a Lyapunov-guided Multi-Dimensional Optimization algorithm (LyMDO) that decouples the original problem into per-slot deterministic problems, where Deep Reinforcement Learning (DRL) and convex optimization are used for joint optimization of partitioning decisions and complementary resource allocation. Simulation results show that our approach effectively improves E2E delay while balancing long-term resource constraints.

Mobile edge computing (MEC) offers an opportunity for devices relying on wireless power transfer (WPT), to accomplish computationally demanding tasks. Such WPT-powered MEC systems have yet to be optimized for long-term efficiency, due to random and changing task demands and wireless channel states of the devices. This paper presents an augmented two-staged deep Q-network (DQN), referred to as “TS-DQN,” for online optimization of WPT-powered MEC systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN for learning the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. Another important aspect is that a new action generation method is developed to expand and diversify the actions of the DQN, further accelerating its convergence. As validated by simulations, the proposed TS-DQN is much more energy efficient and converges much faster, than its potential alternative directly using the state-of-the-art Deep Deterministic Policy Gradient algorithm to learn all decision variables.

This paper presents a new two-stage deep Q-network (DQN), referred to as "TS-DQN", for online optimization of wireless power transfer (WPT)-powered mobile edge computing (MEC) systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN to learn the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. A new action generation method is developed to expand and diversify the actions of the DQN, hence further accelerating its convergence. Simulation shows that the gain of the TS-DQN in energy saving is nearly 60% compared to its potential alternatives.

As interference becomes one of the key factors restricting the performance of wireless network, many interference cancellation schemes are studied. However, most of these schemes have disadvantages in one way or another, such as high complexity and cost of the accurate feedback. In this case, blind interference cancellation schemes are proposed, which can eliminate the interference according to the received signal without any prior information, but with a very high searching complexity. To solve above issues, we propose a semi-blind interference parameter detection (Semi-BIPD) and signal restoration scheme in this paper. Firstly, a semi-blind interference detection module is investigated to detect the parameters related to strong inter-ference with the help of the received demodulation reference signal (DM-RS) sequence. Then, the channel parameters of both target user and interfering users are estimated. Finally, the signal restoration based on Semi-BIPD is conducted in the data sequence to eliminate the interference. Simulation results demonstrate that the proposed scheme can achieve better mean square error (MSE) with a low searching complexity in 5G uplink orthogonal frequency division multiplexing (OFDM) systems.

With the rapid development of indoor location-based services (LBSs), the demand for accurate localization keeps growing as well. To meet this demand, we propose an indoor localization algorithm based on graph convolutional network (GCN). We first model access points (APs) and the relationships between them as a graph, and utilize received signal strength indication (RSSI) to make up fingerprints. Then the graph and the fingerprint will be put into GCN for feature extraction, and get classification by multilayer perceptron (MLP). In the end, experiments are performed under a 2D scenario and 3D scenario with floor prediction. In the 2D scenario, the mean distance error of GCN-based method is 11m, which improves by 7m and 13m compare with DNN-based and CNN-based schemes respectively. In the 3D scenario, the accuracy of predicting buildings and floors are up to 99.73% and 93.43% respectively. Moreover, in the case of predicting floors and buildings correctly, the mean distance error is 13m, which outperforms DNN-based and CNN-based schemes, whose mean distance errors are 34m and 26m respectively.

Transmission control protocol (TCP) congestion control is one of the key techniques to improve network performance. TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency. Existing TCP identification methods can only be applied to limited number of TCP congestion control algorithms and focus on wired networks. In this paper, we proposed a machine learning based passive TCP identification method for wired and wireless networks. After comparing among three typical machine learning models, we concluded that the 4-layers Long Short Term Memory (LSTM) model achieves the best identification accuracy. Our approach achieves better than 98% accuracy in wired and wireless networks and works for newly proposed TCP congestion control algorithms.

Although almost blank subframes (ABS) proposed in heterogeneous networks (HetNet) can enhance the performance of user equipments (UEs) in Pico-cell range expansion (CRE) area, it also significantly degrades the Macro-cell throughput. To address this issue, further-enhanced inter-cell interference coordination (FeICIC) scheme is considered in 3GPP Release 11, where low power ABS (LP-ABS) are adopted for the Macro-cell center region users to improve the Macro-cell throughput. However, LP-ABS power, Pico CRE bias and Pico base station (PBS) density will jointly affect on the system performance, which eventually deteriorates the network energy efficiency (EE) without careful configuration. In this paper, we first deduce the closed-form expression of network EE as a function of PBS density, Pico CRE bias and LP-ABS power based on stochastic geometry model. Then we provide Monte Carlo simulations to verify the accuracy of theoretical derivation of the network EE and analyze the impacts of these parameters on the network EE. The simulation results show that the reasonable PBS density, Pico CRE bias and LP-ABS power can improve the network EE obviously.

To improve the spectrum efficiency of high-order modulation polar codes, a novel modulation strategy, namely displacement of balanced modulation (DBM) is proposed in this paper. This algorithm can balance the performance between bit levels under the universal encoder and decoder by adding a shift mapping matrix. Simulation results show that the presented DBM algorithm can achieve almost the same performance as the multilevel coding (MLC) technique with more flexibility. To further reduce the computational complexity of the proposed DBM algorithm, a simplified algorithm called reduced-complexity DBM (RC-DBM) is also presented, where location-based reliability sorting construction with complexity O(n) instead of Gaussian approximation (GA) construction with complexity O(N logN) is used, and the computing process of DBM is replaced with the structure characteristics of polar codes. Simulation results show that the proposed RC-DBM algorithm has a tolerable performance loss compared to that of DBM.

This paper studies the energy efficiency (EE) maximization for an orthogonal frequency division multiple access (OFDMA) downlink network aided by a relay station (RS) with subcarrier pairing. A highly flexible transmission protocol is considered, where each transmission is executed in two time slots. Every subcarrier in each slot can either be used in direct mode or be paired with a subcarrier in another slot to operate in relay mode. The resource allocation (RA) in such a network is highly complicated, because it has to determine the operation mode of subcarriers, the assignment of subcarriers to users, and the power allocation of the base station and RS. We first propose a mathematical description of the RA strategy. Then, a RA algorithm is derived to find the globally optimum RA to maximize the EE. Finally, we present extensive numerical results to show the impact of minimum required rate of the network, the user number, and the relay position on the maximum EE of the network.

Time allocation optimisation for a multi‐antenna wireless energy and information transfer system is studied in this study. The terminal first harvests the wireless energy from the base station (BS) during phase I, and then uses the harvested energy to power its information transmission during phase II. Realistic channel state information (CSI) assumptions are made where the CSI at the BS is obtained via training and feedback and is subject to channel estimation error and quantisation error. Therefore, in addition to the tradeoff between information and power transfer, there is also a tradeoff in determining the overheads of training and feedback. To gain insight into this problem, the authors characterise the asymptotic behaviour of the parameters in the large system limit where the number of transmit antennas N t and transmission block length L tot tend to infinity with fixed ratio L tot / N t . The optimal overheads of training and feedback within each phase are first derived to maximise the harvested power and the information transmission rate, respectively. The optimised time allocation between phase I and phase II is then obtained by maximising the bounds of the effective average information transmission rate. Finally, numerical results are presented to verify the proposed theorems.

Time domain enhanced intercell interference coordination with Almost Blank Subframe (ABS) configuration for Macrocell is proposed in 3GPP to mitigate the inter-tier interference for heterogeneous networks (HetNets) consisting of Macrocells and overlaying Picos. The existing schemes for ABS power optimization are mainly base on the same level muted power of ABS, which will either cause time-frequency resource waste for Macrocell with zero power ABS or reduce the Pico cell range expansion capability with low power ABS. In this paper, we propose a multilevel power ABS scheme with resource management to conquer these problems. The simulation results show that our proposed scheme can improve the network throughput and protect the cell range expansion (CRE) area user effectively.

A new beamforming codebook design is proposed to reduce the feedback rate of quantized beamforming MIMO systems over temporally correlated channels. Based on a first-order Gauss-Markov fading model, we develop an adaptive cone codebook that can fold or expand to adapt to the interframe temporal correlations of the channel and rotate to track the time-varying channel. Through Monte Carlo simulations, we demonstrate that the proposed cone codebook based limited feedback scheme outperforms the conventional scheme with fixed codebook and compresses the feedback bits without degrading the system performance.

Femtocell networks, consisting of a conventional macro cellular deployment and overlaying femtocells, forming a hierarchical cell structure, constitute an attractive solution to improving the macrocell capacity and coverage. However, the inter- and intra-tier interferences in such systems can significantly reduce the capacity and cause an unacceptably high level of outage. This paper treats the uplink interference problem in orthogonal frequency-division multiple-access (OFDMA)-based femtocell networks with partial cochannel deployment. We first propose an inter-tier interference mitigation strategy without the femtocell users power control by forcing the femto-interfering macrocell users to use only some dedicated subcarriers. The non-interfering macrocell users, on the other hand, can use either the dedicated subcarriers, or the shared subcarriers which are also used by the femtocell users. We then propose subcarrier allocation schemes based on the auction algorithm for macrocell users and femtocell users, respectively, to independently mitigate the intra-tier interference. The proposed interference mitigation scheme for femtocell networks offers significant performance improvement over the existing methods by substantially reducing the inter- and intra-tier inferences in the system.