Industry 5.0 envisions a cyber-physical future where humans and robots collaborate harmoniously, empowered by 6G connectivity and intelligent automation. Central to this vision is the ability to autonomously configure complex production pipelines based on diverse and evolving human intents. Existing orchestration technologies exhibit critical shortcomings in terms of self-learning, validation, error diagnosis, and rectification capabilities. To this end, we propose an Agentic AI orchestration framework that interprets human intents and dynamically assembles optimal technology pipelines using a self-improving, retrieval-augmented Large Language Model (LLM) and a Bayesian contextual-bandit selector. This enables dynamic adaptation in unpredictable factory environments. Our solution is validated in a cyber-physical testbed integrating Digital Twins (DTs), distributed AI, robotics, and real-world network infrastructure. Compared to baseline LLMs, our system reduces orchestration iterations by over 94% for a given intent and by around 90% for an unseen intent, showing rapid convergence and strong generalization. Real-world deployments mirror DT results, confirming both the fidelity of the simulation and the practical value of intent-driven orchestration for human-centric manufacturing.

Multi-access edge computing (MEC) enhances users’ computing capabilities. However, it still faces the queue instability challenge due to the conflict between limited communication-computation resources and the massive access of users. In this paper, we invoke a multiple one-to-one queuing system in a non-orthogonal multiple access (NOMA)-MEC network, where NOMA enables massive access, and study its network stability and queuing delay control mechanisms. Meanwhile, we jointly consider the many-to-one queuing system. We formulate two network power minimization stochastic problems, each subject to the stability of the multiple one-to-one/many-to-one queuing system. We propose Lyapunov-based algorithms to solve these problems analytically and achieve a computational complexity of approximatelyO(N)withNusers. We further investigate the queuing-delay control mechanism in the multiple one-to-one queuing system-based NOMA-MEC network. Numerical results show that the proposed algorithms converge efficiently, effectively stabilize queuing systems, satisfy the queuing-delay requirement, and further demonstrate that: 1) the proposed multiple one-to-one queuing system-based NOMA-MEC exhibits a better balanced load distribution between users and the MEC server compared to orthogonal multiple access (OMA)-MEC; 2) we obtain a more efficient tradeoff between network power consumption and queue backlog compared to the many-to-one queuing system; and 3) we obtain superior queue stabilization capacity under bursty data and network power suppression conditions. This also validates NOMA’s high spectral efficiency and fine-grained remote execution control at the MEC server.

Efficient video transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality video transmission over a bandwidth-constrained noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented semantic communication (GSC) framework. In this framework, we first design a semantic encoder that effectively identify the keyframes from video and extract the relevant semantic information (SI) to reduce the transmission data size. We then develop a semantic decoder to reconstruct the keyframes from the received SI and further generate the full video from the reconstructed keyframes using frame interpolation to ensure high-quality reconstruction. Recognizing the impact of wireless channel noise on SI transmission, we also propose an SD-based denoiser for GSC (SD-GSC) condition on an instantaneous channel gain to remove the channel noise from the received noisy SI under a known channel. For scenarios with an unknown channel, we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn the distribution of channel gains and denoise the received SI. It is shown that, with the known channel, our proposed SD-GSC outperforms state-of-the-art ADJSCC, Latent-Diff DNSC, DeepWiVe and DVST, improving Peak Signal-to-Noise Ratio (PSNR) by 69%, 58%, 33% and 38%, reducing mean squared error (MSE) by 52%, 50%, 41% and 45%, and reducing Fréchet Video Distance (FVD) by 38%, 32%, 22% and 24%, respectively. With the unknown channel, our PSD-GSC achieves a 17% improvement in PSNR, a 29% reduction in MSE, and a 19% reduction in FVD compared to MMSE equalizer-enhanced SD-GSC. These significant performance improvements demonstrate the robustness and superiority of our proposed methods in enhancing video transmission quality and efficiency under various channel conditions.

Federated fine-tuning of pre-trained Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving privacy. However, challenges such as high computational and memory demands, heterogeneous client resources, bandwidth constraints, and ineffective global aggregation hinder its efficiency. To address these issues, we propose HAFLQ (Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization), featuring four technical innovations. First, a salience-driven adaptive quantization scheme ranks transformer blocks by Hessian-based importance scores, applying block-wise quantization (e.g., INT8 for low-salience blocks, FP32 for critical ones) tailored to each client’s GPU capacity. Second, an importance-based parameter management mechanism enables clients with limited resources to selectively freeze low-importance LoRA rank-1 matrices (identified via gradient-sensitivity metrics) while training only critical ones, avoiding truncation-induced performance loss. Third, bandwidth-adaptive communication quantization allocates higher precision (32/16-bit) to important parameters and lower precision (8/4-bit) to less critical ones based on wireless channel conditions, prioritizing transmission of high-impact rank-1 matrices. Fourth, adaptive rank-1 matrix-level aggregation weights client contributions by Frobenius norm and aggregates only updated matrices, preventing information dilution from zero-padding approaches. Experiments on text classification tasks show that HAFLQ outperforms existing heterogeneous federated learning baselines in convergence speed and accuracy under bandwidth-sufficient conditions. Under bandwidth-constrained scenarios, HAFLQ reduces memory usage by 31% via adaptive LLM quantization and communication cost by 49% via adaptive communication quantization, while the complete framework achieves 50% relative accuracy improvement over the zero-padding aggregation baseline (from 59% to 89%).

The emergence of the Metaverse brings new opportunities for enhancing productivity and creativity through real time updates and personalized content. However, it also leads to the generation of massive volumes of dynamic information, placing unprecedented demands on existing communication networks. Current bit-oriented communication systems are not designed to cope with such high levels of semantic complexity and data volume, ultimately limiting the responsiveness and interactivity of Metaverse applications. To address this research gap, we propose a goal-oriented semantic communication framework enabled by generative AI and optimal transport for Metaverse construction (GSC). The proposed GSC framework includes an hourglass network-based (HgNet) encoder to extract semantic information of objects in the Metaverse and a semantic decoder to construct the Metaverse content after wireless transmission,enabling efficient communication and real-time object behaviour updates to the scenery for the Metaverse construction task. To overcome the wireless channel noise at the receiver, we design an optimal transport (OT)-enabled semantic denoiser, which enhances the accuracy of the Metaverse scenery through wireless communication. The results of our computer experiments demonstrate that compared to the conventional Metaverse construction, our proposed GSC framework significantly reduces wireless Metaverse construction latency by 92.6%, while improving the Metaverse object status accuracy and viewing experience by45.6% and 44.7%, respectively.

To provide Uplink Centric Broadband Communication (UCBC), New Radio Unlicensed (NR-U) network has been standardized to exploit the unlicensed spectrum using Listen Before Talk (LBT) scheme to fairly coexist with the incumbent Wireless Fidelity (WiFi) network. Existing access schemes over unlicensed spectrum are required to perform Clear Channel Assessment (CCA) before transmissions, where fixed Energy Detection (ED) thresholds are adopted to identify the channel as idle or busy. However, fixed ED thresholds setting prevents devices from accessing the channel effectively and efficiently, which leads to the hidden node (HN) and exposed node (EN) problems. In this paper, we first develop a centralized double Deep Q-Network (DDQN) algorithm to optimize the uplink system throughput, where the agent is deployed at the central server to dynamically adjust the ED thresholds for NR-U and WiFi networks. Considering that heterogeneous NR-U and WiFi networks, in practice, may not be able to share the raw data with the central server directly due to data privacy, we then develop a vertical federated DDQN algorithm, where two agents are deployed in the NR-U and WiFi networks, respectively. Our results have shown that the uplink system throughput increases by over 100%, where cell throughput of NR-U network rises by 150%, and cell throughput of WiFi network decreases by 30%. To guarantee the cell throughput of WiFi network, we redesign the reward function to punish the agent when the cell throughput of WiFi network is below the threshold, and our revised design can still provide 70% uplink system throughput gain, where cell throughput of NR-U network rises by 100%, and cell throughput of WiFi network rises by 35%.

Autonomous robotic systems are widely deployed in smart factories and operate in dynamic, uncertain, and human-involved environments that require low-latency and robust fault detection and recovery (FDR). To address this, we propose a novel Goal-oriented Semantic Communication (GSC) framework that minimises the FDR time while maximising the robotic task success rate. Our GSC framework defines and extracts the 3D scene graph (3D-SG) as the semantic information for FDR via a semantic extractor, and detects faults by monitoring spatial relationship changes in the 3D-SG. For fault recovery, we fine-tune a small language model (SLM) and enhance its reasoning and generalization capabilities via knowledge distillation to generate recovery motions for robots with low latency. Extensive simulations demonstrate that our GSC framework reduces the average FDR time by 86.1% while achieving a higher task success rate, compared to the State-of-the-Art (SOTA) frameworks that rely on vision-language models (VLMs) for fault detection and large language models (LLMs) for fault recovery. Project website: https://sites.google.com/view/gscfdr.

Federated learning (FL) for large language models (LLMs) offers a privacy-preserving scheme, enabling clients to collaboratively fine-tune locally deployed LLMs or smaller language models (SLMs) without exchanging raw data. While parameter-sharing methods in traditional FL models solves number of technical challenges, they still incur high communication overhead and struggle with adapting to heterogeneous model architectures. Federated distillation, a framework for mutual knowledge transfer via shared logits, typically offers lower communication overhead than parameter-sharing methods. However, transmitting logits from LLMs remains challenging for bandwidth-limited clients due to their high dimensionality. In this work, we focus on a federated LLM distillation with efficient communication overhead. To achieve this, we first propose an adaptive Top-k logit selection mechanism, dynamically sparsifying logits according to real-time communication conditions. Then to tackle the dimensional inconsistency introduced by the adaptive sparsification, we design an adaptive logits aggregation scheme, effectively alleviating the artificial and uninformative inputs introduced by conventional zero-padding methods. Finally, to enhance the distillation effect, we incorporate LoRA-adapted hidden-layer projection from LLM into the distillation loss, reducing the communication overhead further while providing richer representation. Experimental results demonstrate that our scheme achieves superior performance compared to baseline methods while effectively reducing communication overhead by approximately 50%.

Diabetes mellitus is a global health crisis characterized by poor blood sugar regulation, impacting millions of people worldwide and leading to severe complications and mortality. Although Type 1 Diabetes Mellitus (T1DM) has a lower number of cases compared to other forms of diabetes, it is often diagnosed at a young age and requires lifelong exogenous insulin administration. In this paper, we focus on understanding the interaction of insulin and glucose molecules within the subcutaneous layer, which is crucial for blood sugar control in T1DM patients. Specifically, we propose a comprehensive model to characterize the insulin-glucose system within the subcutaneous layer, incorporating a multicellular molecular communication system. We then divide the T1DM system into insulin and glucose subsystems and derive the end-to-end expression for insulin-glucose interaction in the subcutaneous layer. We further validate and demonstrate the correctness of our insulin-glucose interaction analysis via an agent-based simulator.

Efficient image transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality image reconstruction over a bandwidth-constrained noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented semantic communication (GSC) framework. In this framework, we design a semantic autoencoder that effectively extracts semantic information (SI) from images to reduce the transmission data size while ensuring high-quality reconstruction. Recognizing the impact of wireless channel noise on SI transmission, we propose an SD-based denoiser for GSC (SD-GSC) conditional on an instantaneous channel gain to remove the channel noise from the received noisy SI under known channel. For scenarios with unknown channel, we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn the distribution of channel gains and denoise the received SI. It is shown that, with the known channel, our SD-GSC outperforms state-of-the-art ADJSCC and Latent-Diff DNSC, improving Peak Signal-to-Noise Ratio (PSNR) by 32 % and 21 %, and reducing Fréchet Inception Distance (FID) by 40 % and 35 %, respectively. With the unknown channel, our PSD-GSC improves PSNR by 8 % and reduces FID by 17 % compared to MMSE equalizer-enhanced SD-GSC.

Federated fine-tuning of pre-trained Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving data privacy. However, the large model size and heterogeneity in client resources pose significant computational and communication challenges. To address these issues, in this paper, we propose a novel Heterogeneous Adaptive Federated Low-Rank Adaptation (LoRA) fine-tuned LLM framework (HAFL). To accommodate client resource heterogeneity, we first introduce an importance-based parameter truncation scheme, which allows clients to have different LoRA ranks, and smoothed sensitivity scores are used as importance indicators. Despite its flexibility, the truncation process may cause performance degradation. To tackle this problem, we develop an importance-based parameter freezing scheme. In this approach, both the cloud server and clients maintain the same LoRA rank, while clients selectively update only the most important decomposed LoRA rank-1 matrices, keeping the rest frozen. To mitigate the information dilution caused by the zero-padding aggregation method, we propose an adaptive aggregation approach that operates at the decomposed rank-1 matrix level. Experiments on the 20 News Group classification task show that our method converges quickly with low communication size, and avoids performance degradation when distributing models to clients compared to truncation-based heterogeneous LoRA rank scheme. Additionally, our adaptive aggregation method achieves faster convergence compared to the zero-padding approach.

Efficient video transmission is a critical task within the realm of current wireless communication system. Addressing the imperative to alleviate the transmission burden and conserve communication resources, we propose an optical flow-based goal-oriented semantic communication framework for video transmission (OF-GSC). Our framework features an optical flow-based semantic encoder that includes a motion extractor for optical flow estimation and a patch-level optical flow-based semantic information (SI) extractor to effectively identify and select important SI, thereby reducing the transmission load while ensuring the high-quality video reconstruction in semantic decoder. Specifically, the first original frame is leveraged as the base knowledge. Once the base knowledge and the received SI are embedded at the receiver, the embedded data is then fed into the customized autoencoder model within the semantic decoder of OF-GSC framework for efficient video reconstruction. In comparison to DeepJSCC, our OF-GSC framework achieves a significant improvement in generated video quality, as evidenced by a 13.47% increase in the Structural Similarity Index Measure (SSIM) score. Under stringent communication constraints, OFGSC surpasses M-JPEG by 14.04% in SSIM score. These results highlight the robustness and superiority of our proposed OF-GSC in efficient video transmission.

Using ambient magnetic for indoor localization has been a research focus in recent years. However, intricate magnetic feature patterns in complicated indoor ambiance, with particular emphasis on the multi-scale dynamics arising from diverse user motion states, further hinder localization accuracy and universality. To address these challenges, this paper originally proposes a novel mixed-frequency magnetic data representation learning-based framework (MixLoc) for accurate and universal localization. Our core idea is to systematically model the multi-scale dynamics-affected pedestrian indoor localization as a location-semantic learning problem based on mixed-frequency magnetic data. First, we propose a data augmentation method to automatically construct a mixed-frequency magnetic dataset. Then, we propose a novel encoder to learn temporal and spatial representations from these data and extract subtle differences among multi-scale sequences. Finally, a novel localization model is proposed to accurately infer locations by capturing significant global and local features from both temporal and spatial representations. The evaluation results based on comprehensive experiments show that the accuracy of MixLoc increases about 42% compared to other state-of-the-art approaches.

In this paper, we present an autonomous control system for Unmanned Aerial Vehicles (UAVs), specifically designed to inspect a detected suspicious vessel and capture information-rich images in a maritime environment. The maritime environment is ever-changing and uncertain, making it challenging to perform maritime monitoring tasks efficiently and reliably. The proposed UAV control system consists of multiple modules, including path planning, vessel searching, image processing, and image optimization. A novel image optimization approach utilizing deep reinforcement learning (DRL) is proposed to enhance the quality of the captured images by jointly controlling the movement of the UAV and camera orientation. The effectiveness and efficiency of the proposed system were validated and evaluated by searching the vessel and optimizing the captured images in the self-developed simulation environment in Gazebo.

With the emergence of the metaverse and its role in enabling real-time simulation and analysis of real-world counterparts, an increasing number of personalized metaverse scenarios are being created to influence entertainment experiences and social behaviors. However, compared to traditional image and video entertainment applications, the exact transmission of the vast amount of metaverse-associated information significantly challenges the capacity of existing bit-oriented communication networks. Moreover, the current metaverse also witnesses a growing goal shift for transmitting the meaning behind custom-designed content, such as user-designed buildings and avatars, rather than exact copies of physical objects. To meet this growing goal shift and bandwidth challenge, this paper proposes a goal-oriented semantic communication framework for metaverse application (GSCM) to explore and define semantic information through the goal levels. Specifically, we first analyze the traditional image communication framework in metaverse construction and then detail our proposed semantic information along with the end-to-end wireless communication. We then describe the designed modules of the GSCM framework, including goal-oriented semantic information extraction, base knowledge definition, and neural radiance field (NeRF) based metaverse construction. Finally, numerous experiments have been conducted to demonstrate that, compared to image communication, our proposed GSCM framework decreases transmission latency by up to 92.6% and enhances the virtual object operation accuracy and metaverse construction clearance by up to 45.6% and 44.7%, respectively.

Unmanned aerial vehicle (UAV) assisted non-orthogonal multiple access (NOMA) communications show great potential in providing simultaneous service to various users. This paper proposes a novel reconfigurable intelligent surface (RIS) aided UAV-swarm NOMA system, where the Coordinated Multi-Point (CoMP) technique is employed to manage inter-cell interference for all users. The primary objective is to maximize the overall security rate by jointly optimizing the UAV swarm trajectories, power distribution among the UAVs, and the reflection coefficients of the RIS. To address the non-convex nature and interdependence of this optimization problem while accommodating long-term dynamic requirements, we introduce an optimization framework that combines alternating optimization (AO) with the weighted-QMIX algorithm. Specifically, successive convex optimization (SCA) is utilized within the AO method to determine the optimal power allocation and RIS coefficients. Meanwhile, the weighted-QMIX algorithm conducts online trajectory optimization for all UAVs through a multi-agent deep reinforcement learning process. Numerical results demonstrate that: 1) the proposed method achieves a higher communication security rate compared to systems lacking RIS, NOMA, or stationary UAVs; and 2) the proposed algorithm outperforms a pure deep reinforcement learning (DRL) algorithm in terms of both computational complexity and security performance.

As one of the most promising technologies in industry, the Digital Twin (DT) facilitates real-time monitoring and predictive analysis for real-world systems by precisely reconstructing virtual replicas of physical entities. However, this reconstruction faces unprecedented challenges due to the ever-increasing communication overhead, especially for digital robot arm reconstruction. To this end, we propose a novel goal-oriented semantic communication (GSC) framework to extract the GSC information for the robot arm reconstruction task in the DT, with the aim of minimising the communication load under the strict and relaxed reconstruction error constraints. Unlike the traditional reconstruction framework that periodically transmits a reconstruction message for real-time DT reconstruction, our framework implements a feature selection (FS) algorithm to extract the semantic information from the reconstruction message, and a deep reinforcement learning-based temporal selection algorithm to selectively transmit the semantic information over time. We validate our proposed GSC framework through both Pybullet simulations and lab experiments based on the Franka Research 3 robot arm. For a range of distinct robotic tasks, simulation results show that our framework can reduce the communication load by at least 59.5% under strict reconstruction error constraints and 80% under relaxed reconstruction error constraints, compared with traditional communication framework. Also, experimental results confirm the effectiveness of our framework, where the communication load is reduced by 53% in strict constraint case and 74% in relaxed constraint case. The demo is available at: https://youtu.be/2OdeHKxcgnk.

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.

The emerging field of federated learning (FL) holds significant promise for advancing edge intelligence while preserving data privacy. However, as FL systems scale or become more heterogeneous, challenges such as spectrum scarcity and the straggler problem arise. To address these issues, this paper proposes SA-AirFed, a semi-asynchronous FL architecture compatible with Over-the-Air Computation (AirComp). We develop an efficient scheduling scheme that meets AirComp’s requirements and analyze the factors affecting convergence under the Lipschitz-Smooth condition. Building on insights from the convergence analysis, we design an adaptive algorithm that mitigates staleness from semi-asynchronous aggregation and noise from AirComp by dynamically adjusting aggregation weights, formulated as a convex quadratic programming problem. Experimental results on MNIST and CIFAR-10 demonstrate that SA-AirFed significantly reduces wall-clock training time while achieving greater robustness compared to baseline models.

As one of the most promising technologies in industry, the Digital Twin (DT) facilitates real-time monitoring and predictive analysis for real-world systems by precisely reconstructing virtual replicas of physical entities. However, this reconstruction faces unprecedented challenges due to the ever-increasing communication overhead, especially for digital robotic arm reconstruction. To this end, we propose a novel goal-oriented semantic communication (GSC) design to extract GSC information for the robotic arm reconstruction task in the DT, with the aim of minimising the communication load without sacrificing the reconstruction accuracy. Specifically, rather than transmitting a message that contains complete robotic arm states for reconstruction, we design a Wireless Feature Selection (WFS) algorithm that first segments the motion of the physical robot into several phases, and then extracts and transmits GSC information from this message according to the current phase. Our proposed design is validated through both Pybullet simulations and real-world experiments using the Franka Research 3 robotic arm, where their communication loads are reduced by 44.1% and 35%, respectively, while maintaining the reconstruction error in the same level. The accompanying demo is available online at: https://youtu.be/IAZjoTcbaFA.

Time-triggered federated learning, in contrast to conventional event-based federated learning, organizes users into tiers based on fixed time intervals. However, this network still faces challenges due to a growing number of devices and limited wireless bandwidth, increasing issues like stragglers and communication overhead. In this paper, we apply model pruning to wireless Time-triggered systems and jointly study the problem of optimizing the pruning ratio and bandwidth allocation to minimize training loss under communication latency constraints. To solve this joint optimization problem, we perform a convergence analysis on the gradient l2-norm of the asynchronous multi-tier federated learning (FL) model with adaptive model pruning. The convergence upper bound is derived and a joint optimization problem of pruning ratio and wireless bandwidth is defined to minimize the model training loss under a given communication latency constraint. The closed-form solutions for wireless bandwidth and pruning ratio by using KKT conditions are then formulated. As indicated in the simulation experiments, our proposed TT-Prune demonstrates a 40% reduction in communication cost, compared with the asynchronous multi-tier FL without model pruning, while maintaining the model convergence at the same level.

The ultra-reliable and low-latency communication (URLLC) offered by the fifth-generation (5G) mobile commu-nication network cannot support the safe operation of robots. Nowadays, the sixth-generation (6G) mobile communication net-work is proposed with the aim to provide the service of hyper-reliable and low-latency communication to enable more accurate and safe control for robots. However, current research mainly focused on improving communication performance, while the robotics community mostly assumed the communication to be ideal without packet loss or latency. To jointly consider and optimize communication and robotic control with a focus on the robotic task, we integrate task-oriented and semantic-aware communication in robotic control (TSRC) to exploit the context of data and its importance in achieving the task. The TSRC framework includes a proactive repetition scheme for control and command (C&C) data transmission, and the value of information (Vol) and the age of information (Aol) based queue ordering mechanism (VA-QOM) utilized to reorganize the queue of re-ceived C&C data based on their semantic information extracted from the Aol and the Vol. To verify the effectiveness of our proposed TSRC framework, we compare it with the traditional unmanned aerial vehicle (UAV) control framework in a real-time UAV waypoint transmission task via simulation, which validates that our proposed TSRC framework achieves great enhancement in performance compared with the traditional UAV control framework.

Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. Hierarchical FL (HFL), as a device-edge-cloud aggregation hierarchy, can enjoy both the cloud server's access to more datasets and the edge servers' efficient communications with devices. However, the learning latency increases with the HFL network scale due to the increasing number of edge servers and devices with limited local computation capability and communication bandwidth. To address this issue, in this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We present the convergence rate of an upper on the$l_{2}$-norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation. By decoupling the optimization problem and using Karush-Kuhn-Tucker (KKT) conditions, closed-form solutions of pruning ratio and wireless resource allocation are derived. Simulation results show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50% communication cost.

The design and engineering of molecular communication (MC) components capable of processing chemical concentration signals is the key to unleashing the potential of MC for interdisciplinary applications. By controlling the signaling pathway and molecule exchange between cell devices, synthetic biology provides the MC community with tools and techniques to achieve various signal processing functions. In this paper, we propose a design framework to realize any order concentration shift keying (CSK) systems based on simple and reusable single-input single-output cells. The design framework also exploits the distributed computation on multicellular consortia with spatial segregation, which has advantages in system scalability, low genetic manipulation, and signal orthogonality. We also create a small library of simple logic engineered cells and apply them to implement binary CSK (BCSK) and quadruple CSK (QCSK) systems to demonstrate the feasibility of our proposed design framework. The simplicity of our engineered cells allows for their reuse in other systems beyond CSK. Importantly, we establish a mathematical framework to theoretically characterize our proposed distributed multicellular systems. Specially, we divide a system into fundamental building blocks, from which we derive the impulse response of each block and the cascade of the impulse responses leads to the end-to-end response of the system. Simulation results obtained from the agent-based simulator BSim not only validate our CSK design framework but also demonstrate the accuracy of the proposed mathematical analysis.

With the emergence of the metaverse and its applications in representing humans and intelligent entities in social and related augmented reality (AR) applications. The current bit-oriented network faces challenges in supporting real-time updates for the vast amount of associated information, which hinders development. Thus, a critical revolution in the sixth generation (6G) networks is envisioned through the joint exploitation of information context and its importance to the goal, leading to a communication paradigm shift towards semantic and effectiveness levels. However, current research has not yet proposed any explicit and systematic communication framework for AR applications that incorporate these two levels. To fill this research gap, this paper presents a goal-oriented semantic communication framework for augmented reality (GSAR) to enhance communication efficiency and effectiveness in 6G. Specifically, we first analyse the traditional wireless AR point cloud communication framework and then summarize our proposed semantic information along with the end-to-end wireless communication. We then detail the design blocks of the GSAR framework, covering both semantic and effectiveness levels. Finally, numerous experiments have been conducted to demonstrate that, compared to the traditional point cloud communication framework, our proposed GSAR significantly reduces wireless AR application transmission latency by 95.6%, while improving communication effectiveness in geometry and color aspects by up to 82.4% and 20.4%, respectively.

Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. Hierarchical FL (HFL), as a device-edge-cloud aggregation hierarchy, can enjoy both the cloud server’s access to more datasets and the edge servers’ efficient communications with devices. However, the learning latency increases with the HFL network scale due to the increasing number of edge servers and devices with limited local computation capability and communication bandwidth. To address this issue, in this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We present the convergence analysis of an upper on the l2-norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation. By decoupling the optimization problem and using Karush–Kuhn–Tucker (KKT) conditions, closed-form solutions of pruning ratio and wireless resource allocation are derived. Simulation results show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50% communication cost.

Intelligent reflecting surface (IRS) is a promising technology that provides high throughput in future communication systems and is compatible with various communication techniques, such as non-orthogonal multiple-access (NOMA). This paper studies the downlink transmission of IRS-assisted NOMA communication, considering the practical case of imperfect channel state information (CSI). Aiming to maximize the system sum rate, a robust IRS-aided NOMA design is proposed to jointly find the optimal beamforming vector for the access point and the passive reflection matrix for the IRS. This robust design is realised using the penalty dual decomposition (PDD) scheme, and it is shown that the results have a close performance to their upper bound obtained from the corresponding perfect CSI scenario. The presented method is compatible with both continuous and discrete phase shift elements of the IRS. Our findings show that the proposed algorithms, for both continuous and discrete IRS, have low computational complexity compared to other schemes in the literature. Furthermore, we conduct a performance comparison between the IRS-aided NOMA and the IRS-aided orthogonal multiple access (OMA). This comparison shows that robust beamforming techniques are crucial for the system to reap the advantages of IRS-aided NOMA communication in the presence of CSI uncertainty.

Cellular-connected unmanned aerial vehicle (UAV) swarm is a promising solution for diverse applications, including cargo delivery and traffic control. However, it is still challenging to communicate with and control the UAV swarm with high reliability, low latency, and high energy efficiency. In this paper, we propose a two-phase command and control (C&C) transmission scheme in a cellular-connected UAV swarm network, where the ground base station (GBS) broadcasts the common C&C message in phase I. In phase II, the UAVs that have successfully decoded the C&C message will relay the message to the rest of UAVs via device-to-device (D2D) communications in either broadcast or unicast mode, under latency and energy constraints. To maximize the number of UAVs that receive the message successfully within the latency and energy constraints, we formulate the problem as a Constrained Markov Decision Process to find the optimal policy. To address this problem, we propose a decentralized constrained graph attention multi-agent Deep-Q-network (DCGA-MADQN) algorithm based on Lagrangian primal-dual policy optimization, where a PID-controller algorithm is utilized to update the Lagrange Multiplier. Simulation results show that our algorithm could maximize the number of UAVs that successfully receive the common C&C under energy constraints.

The ultra-reliable and low-latency communication (URLLC) service of the fifth-generation (5G) mobile communication network struggles to support safe robot operation. Nowadays, the sixth-generation (6G) mobile communication network is proposed to provide hyper-reliable and low-latency communication to enable safer control for robots. However, current 5G/ 6G research mainly focused on improving communication performance, while the robotics community mostly assumed communication to be ideal. To jointly consider communication and robotic control with a focus on the specific robotic task, we propose goal-oriented semantic communication in robotic control (GSRC) to exploit the context of data and its importance in achieving the task at both transmitter and receiver. At the transmitter, we propose a deep reinforcement learning algorithm to generate optimal control and command (C&C) data and a proactive repetition scheme (DeepPro) to increase the successful transmission probability. At the receiver, we design the value of information (VoI) and age of information (AoI) based queue ordering mechanism (VA-QOM) to rank the queue based on the semantic information extracted from AoI and VoI. The simulation results validate that our proposed GSRC framework achieves a 91.5% improvement in the mean square error compared to the traditional unmanned aerial vehicle control framework.

Upon the advent of the emerging metaverse and its related applications in Augmented Reality (AR), the current bit-oriented network struggles to support real-time changes for the vast amount of associated information, creating a significant bottleneck in its development. To address the above problem, we present a novel task-oriented and semantics-aware communication framework for augmented reality (TSAR) to enhance communication efficiency and effectiveness significantly. We first present an analysis of traditional wireless AR point cloud communication framework, followed by a detailed summary of our proposed semantic information extraction within the end-to-end communication. Then, we detail the components of the TSAR framework, incorporating semantics extraction with deep learning, task-oriented base knowledge selection, and avatar pose recovery. Through rigorous experimentation, we demonstrate that our proposed TSAR framework considerably outperforms traditional point cloud communication framework, reducing wireless AR application transmission latency by 95.6% and improving communication effectiveness in geometry and color aspects by up to 82.4% and 20.4%, respectively.

The emerging field of federated learning (FL) provides great potential for edge intelligence while protecting data privacy. However, as the system grows in scale or becomes more heterogeneous, new challenges, such as the spectrum shortage and stragglers issues, arise. These issues can potentially be addressed by over-the-air computation (AirComp) and asynchronous FL, respectively, however, their combination is difficult due to their conflicting requirements. In this paper, we propose a novel asynchronous FL with AirComp in a time-triggered manner (async-AirFed). The conventional async aggregation requests the historical data to be used for model updates, which can cause the accumulation of channel noise and interference when AirComp is applied. To address this issue, we propose a simple but effective truncation method which retains a limited length of historical data. Convergence analysis presents that our proposed async-AirFed converges on non-convex optimality function with sub-linear rate. Simulation results show that our proposed scheme achieves more than 34% faster convergence than the benchmarks, by achieving an accuracy of 85%, which also improves the time utilization efficiency and reduces the impact of staleness and the channel.

The usage of drones has tremendously increased in different sectors spanning from military to industrial applications. Despite all the benefits they offer, their misuse can lead to mishaps, and tackling them becomes more challenging particularly at night due to their small size and low visibility conditions. To overcome those limitations and improve the detection accuracy at night, we propose an object detector called Ghost Auto Anchor Network (GAANet) for infrared (IR) images. The detector uses a YOLOv5 core to address challenges in object detection for IR images, such as poor accuracy and a high false alarm rate caused by extended altitudes, poor lighting, and low image resolution. To improve performance, we implemented auto anchor calculation, modified the conventional convolution block to ghost-convolution, adjusted the input channel size, and used the AdamW optimizer. To enhance the precision of multiscale tiny object recognition, we also introduced an additional extra-small object feature extractor and detector. Experimental results in a custom IR dataset with multiple classes (birds, drones, planes, and helicopters) demonstrate that GAANet shows improvement compared to state-of-the-art detectors. In comparison to GhostNet-YOLOv5, GAANet has higher overall mean average precision (mAP@50), recall, and precision around 2.5%, 2.3%, and 1.4%, respectively. The dataset and code for this paper are available as open source at https://github.com/ZeeshanKaleem/GhostAutoAnchorNet.

Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communication efficiency. However, existing semantic communication approaches still face limitations in achieving considerable inference performance in downstream AI tasks like image recognition, or balancing the inference performance with the quality of the reconstructed image at the receiver. Therefore, this paper proposes a contrastive learning (CL)-based semantic communication approach to overcome these limitations. Specifically, we regard the image corruption during transmission as a form of data augmentation in CL and leverage CL to reduce the semantic distance between the original and the corrupted reconstruction while maintaining the semantic distance among irrelevant images for better discrimination in downstream tasks. Moreover, we design a two-stage training procedure and the corresponding loss functions for jointly optimizing the semantic encoder and decoder to achieve a good trade-off between the performance of image recognition in the downstream task and reconstructed quality. Simulations are finally conducted to demonstrate the superiority of the proposed method over the competitive approaches. In particular, the proposed method can achieve up to 56% accuracy gain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48.

To cater for the data-hungry Internet of Things (IoT) applications, uplink centric broadband communication (UCBC) has been identified as a new service class in the vision of 5.5G, where the unlicensed spectrum has been regarded as a promising solution to boost the uplink capacity. The new radio unlicensed (NR-U) network adopts category-4 (Cat4) listen before talk (LBT) access scheme to exploit the unlicensed spectrum and fairly coexist with the incumbent wireless fidelity (WiFi) network. However, the existing Cat4 LBT access scheme adopts single fixed energy detection (ED) threshold and backoff speed, which cannot adapt to the sophisticated interference and achieve the expected uplink system throughput. To tackle this issue, in this article, we develop a novel Cat4 LBT access scheme with adaptive backoff procedure for UCBC, which includes instantaneous interference level quantification, instantaneous interference level sharing, and backoff speed determination. The results have shown that our proposed adaptive Cat4 LBT scheme achieves over 70% uplink system throughput performance gain where cell throughput of NR-U network rises by over 100%, and cell throughput of WiFi network increases by 25%.

The Internet of underwater things (IoUT) is envisioned to be an essential part of maritime activities. Given the IoUT devices’ wide-area distribution and constrained transmit power, autonomous underwater vehicles (AUVs) have been widely adopted for collecting and forwarding the data sensed by IoUT devices to the surface-stations. In order to accommodate the diverse requirements of IoUT applications, it is imperative to conceive a multi-tier underwater computing (MTUC) framework by carefully harnessing both the computing and the communications as well as the storage resources of both the surface-station and of the AUVs as well as of the IoUT devices. Furthermore, to meet the stringent energy constraints of the IoUT devices and to reduce the operating cost of the MTUC framework, a joint environment-aware AUV trajectory design and resource management problem is formulated, which is a high-dimensional NP-hard problem. To tackle this challenge, we first transform the problem into a Markov decision process (MDP) and solve it with the aid of the asynchronous advantage actor-critic (A3C) algorithm. Our simulation results demonstrate the superiority of our scheme.

The sixth-generation (6G) communication research is currently in the early stage, where ultra-reliable low-latency communication (URLLC) is still an important service as in the fifth-generation (5G). Since 6G networks are expected to provide even higher levels of massive connectivity, high spectrum efficiency, high reliability, and low latency than 5G communication, it would confront much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. However, how to achieve URLLC requirements in NR-U networks is extremely challenging due to interference and collisions among multiple radio access technologies (e.g., WiFi). Therefore, it is urgent to design efficient spectrum-sharing algorithms to support URLLC in emerging 6G networks. In this paper, we develop novel centralized deep reinforcement learning (CDRL) and federated DRL (FDRL) frameworks, respectively, to optimize the downlink URLLC transmission in NR-U and WiFi coexistence systems through dynamically adjusting energy detection (ED) thresholds. Our results show that both CDRL and FDRL approaches have improved the reliability of the NR-U system significantly, but the CDRL framework has sacrificed the reliability of the WiFi system. To guarantee the reliability of the WiFi system while improving the NR-U system, we take fairness into account by redesigning the reward of CDRL.

Reinforcement learning (RL) is a widely investigated intelligent algorithm and proved to be useful in the wireless communication area. However, for optimization problems in large-scale multi-cell networks whose dimension increases exponentially, it is unrealistic to employ a conventional centralized RL algorithm and make decisions for the entire network. Multi-agent RL, which allows distribute decision-making, is expected to solve the scalability problem but with performance issues due to the unknown global information, i.e., non-stationary environment. In this paper, we propose a parameter-sharing multi-agent RL for grouping decisions of coordinated multi-point in a large-scale network, where agents jointly serve users to enhance the cell-edge service. By sharing information via parameters, our theoretical and simulation results show that parameter sharing can largely benefit the multi-agent algorithm with convergence proof and convergence speed analysis. To reduce the effect of biased local heterogeneous experience, we also propose a transfer learning method for the parameter sharing process, whose performance of transfer learning algorithms is verified by the simulation results.

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique to support massive Ultra-Reliable and Low-Latency Communication (mURLLC) service. However, the dynamic resource configuration in GF-NOMA systems is challenging due to random traffics and collisions, that are unknown at the base station (BS). Meanwhile, joint consideration of the latency and reliability requirements makes the resource configuration of GF-NOMA for mURLLC more complex. To address this problem, we develop a novel learning framework for signature-based GF-NOMA in mURLLC service taking into account the multiple access signature collision, the UE detection, as well as the data decoding procedures for the K-repetition GF and the Proactive GF schemes. The goal of our learning framework is to maximize the long-term average number of successfully served users (UEs) under the latency constraint. We first perform a real-time repetition value configuration based on a double deep Q-Network (DDQN) and then propose a Cooperative Multi-Agent learning technique based DQN (CMA-DQN) to optimize the configuration of both the repetition values and the contention-transmission unit (CTU) numbers. Our results show the superior performance of CMA-DQN over the conventional load estimation-based uplink resource configuration approach (LE-URC) in heavy traffic and demonstrate its capability in dynamically configuring in long term for mURLLC service. In addition, with our learning optimization, the Proactive scheme always outperforms the K-repetition scheme in terms of the number of successfully served UEs, especially under the high backlog traffic scenario.

Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to be a major part of the sixth generation (6G) networks. The UAVs connected to the base station (BS), as aerial users (UEs), could exploit machine learning (ML) algorithms to provide a wide range of advanced applications, like object detection and video tracking. Conventionally, the ML model training is performed at the BS, known as centralized learning (CL), which causes high communication overhead due to the transmission of large datasets, and potential concerns about UE privacy. To address this, distributed learning algorithms, including federated learning (FL) and split learning (SL), were proposed to train the ML models in a distributed manner via only sharing model parameters. FL requires higher computational resource on the UE side than SL, while SL has larger communication overhead when the local dataset is large. To effectively train an ML model considering the diversity of UEs with different computational capabilities and channel conditions, we first propose a novel distributed learning architecture, a hybrid split and federated learning (HSFL) algorithm by reaping the parallel model training mechanism of FL and the model splitting structure of SL. We then provide its convergence analysis under non-independent and identically distributed (non-IID) data with random UE selection scheme. By conducting experiments on training two ML models, Net and AlexNet, in wireless UAV networks, our results demonstrate that the HSFL algorithm achieves higher learning accuracy than FL and less communication overhead than SL under IID and non-IID data, and the learning accuracy of HSFL algorithm increases with the increasing number of the split training UEs. We further propose a Multi-Arm Bandit (MAB) based best channel (BC) and best 2-norm (BN2) (MAB-BC-BN2) UE selection scheme to select the UEs with better wireless channel quality and larger local model updates for model training in each round. Numerical results demonstrate it achieves higher learning accuracy than BC, MAB-BC and MAB-BN2 UE selection scheme under non-IID, Dirichlet-nonIID and Dirichlet-Imbalanced data.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

Reinforcement learning (RL) is a efficient intelligent algorithm when solving radio resource management problems in the wireless communication network. However, for large-scale networks with limited centralization (i.e., high latency connection to center-server or capacity-limited backbone), it is not realistic to employ a centralized RL algorithm to perform joint real-time decision-making for the entire network, which calls for scalable algorithm designs. Multi-agent RL, which allows separate local execution of policy, has been applied to large-scale wireless communication areas. However, it has performance issue which largely varies with different system settings. In this paper, we study a multi-agent algorithm for a coordinate multipoint (CoMP) scenario, which requires cooperation between base stations. We show that the common settings of user distribution, the design of reward, and episodic in the environment can significantly ease the learning of the algorithm and obtain beautiful converge results. However, these settings are not realistic in wireless communication. By validating the performance difference between these settings with our algorithm in a coordinate multipoint (CoMP) scenario, we introduce several possible solutions and highlight the necessity of further study in this area.

To improve the capacity of cellular systems without additional expenses on licensed frequency bands, the 3rd Gen-eration Partnership Project (3GPP) has proposed New Radio Unlicensed (NR-U). It should be noted that each node in NR-U has to perform the Listen-Before- Talk (LBT) operation before transmission to avoid collisions by other unlicensed radio access technologies (e.g., WiFi). Thus, packets transmissions are prone to delay due to the LBT channel access mechanism. How to achieve Ultra-Reliable and Low-Latency Communications (URLLC) requirements in NR-U networks under the coexistence with WiFi networks is of importance and extremely challenging. In this paper, we develop a novel deep reinforcement learning (DRL) framework to optimize the downlink URLLC trans-mission in the NR-U and WiFi coexistence system through dynamically adjusting the energy detection (ED) thresholds. Our results have shown that the NR-U system reliability has been improved significantly via the DRL compared to that without learning approaches, but with the sacrifice of WiFi system reliability. To address this, we redesigned the reward to take fairness into account, which guarantees the WiFi system reliability while improvina the NR- U system reliability.

In this paper, we investigate secure wireless commu-nication in an intelligent reflecting surface (IRS)-assisted system where the IRS is used to secure the communication of one legitimate receiver in presence of an eavesdropper. We assume that the IRS is standalone, i.e. the passive beamforming of the IRS is carried out completely on its own. Thus, we design an IRS with several passive elements and only two RF chains that can obtain a partial channel state information (CSI) among each node and the IRS. The partial CSI is then mapped into full CSI by using the correlation information between the channels of different IRS elements. We develop a deep reinforcement learning (DRL)-based framework using the deep deterministic policy gradient (DDPG) algorithm to obtain the IRS beamforming vector resulting in maximizing the secrecy rate. Numerical results demonstrate the ability of this technique to secure the wireless communication system.

Cellular-connected unmanned aerial vehicle (UAV) swarm is a promising solution for diverse applications, including cargo delivery and traffic control. However, it is still challenging to communicate with and control the UAV swarm with high reliability and low latency. In this paper, we propose a two-phase command and control (C&C) transmission scheme in cellular-connected UAV swarm network, where the ground base station (GBS) broadcasts the common C&C message in Phase I, the UAVs that have successfully decoded the C&C message will then relay the message to the rest of UAVs via device-to-device (D2D) communications under individual latency constraint. To maximize the number of UAVs that receive the message successfully within the latency constraint, we formulate the problem as a decentralized and partially observable Markov process for finding the optimal policies. To address this problem, we further develop a fully decentralized graph attention network (GAT)-based reinforcement learning algorithm to optimize the D2D pair selection, where the GAT is utilized to exploit the dynamic topology information of cellular-connected UAV swarm network. Simulation results show that our algorithm outperforms the other two baselines and could achieve a cooperative target under a mobile UAV swarm scenario.

Unmanned aerial vehicles (UAVs) are the vital components of sixth generation (6G) millimeter wave (mmWave) wireless networks. Fast and reliable beam alignment is essential for efficient beam-based mmWave communications between UAVs and the base stations (BSs). Learning-based approaches may greatly reduce the overhead by leveraging UAV data, such as position, to identify the optimal beam directions. In this paper, we propose a deep reinforcement learning (DRL)-based framework for UAV-BS beam alignment using the hierarchical deep Q-Network (hDQN) in a mmWave radio setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with three dimensional (3D) beams under diverse channel conditions. A BS serves with learnt beam-pairs in an uplink manner upon every communication request from UAV inside the multi-location environment. Compared to our prior DQN-based method, the proposed hDQN framework uses the location information and the fixed spatial arrangement of the antenna elements to reduce the beam search complexity and maximize the data rates efficiently. The results show that our proposed hDQN-based framework converges faster than the DQN-based approach with an average overall training reduction of 43% and, is generic to multi-location environments across different uniform planar array (UPA) configurations and diverse channel conditions.

Realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in massive Ultra-Reliable and Low-Latency Communications (mURLLC) is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced grant-free non-orthogonal multiple access (GF-NOMA) with multiple configured-grants (MCGs), where UE can choose any of these grants as soon as the data arrives. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems. We first design the MCG-GF-NOMA model by characterizing each CG. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to allocate the channel resources among MCGs to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances for mURLLC.

The ever-growing use of unmanned aerial vehicles (UAVs) as aerial users is becoming a major part of the sixth generation (6G) networks, which could provide various applications, like object detection and video surveillance, by exploiting machine learning (ML) algorithms. However, the training of conventional centralized ML algorithms causes high communication overhead due to the transmission of large datasets and may reveal user privacy. Hence, distributed learning algorithms, including federated learning (FL) and split learning (SL), are proposed to train ML models in a distributed manner via sharing model parameters rather than raw data. Due to the different learning structures, they have different communication and learning efficiency. We propose a new distributed learning architecture, namely hybrid split and federated learning (HSFL), by adopting the parallel model training mechanism of FL and the network splitting structure of SL. Through the simulations in wireless UAV networks, the HSFL algorithm is demonstrated to have higher learning accuracy than FL and less communication overhead than SL under non-IID data. We further propose a Multi-Arm Bandit (MAB) based best channel (BC) and best 2-norm (BN2) (MAB-BC-BN2) UE selection scheme to select the UEs with better channel quality and larger local model updates in each round. Numerical results demonstrate it achieves higher learning accuracy than the benchmark schemes, BC, MAB-BC, and MAB-BN2 UE selection schemes.

Fifth Generation (5G) New Radio (NR) does not support data transmission during random access (RA) procedures, which results in unnecessary control signalling overhead, especially for small data transmission (SDT). Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state. To compare the 4/2-step SDT RA schemes with the benchmark 4/2-step RA schemes, we provide a spatio-temporal analytical framework to evaluate the RA schemes, which jointly models the preamble detection, Physical Uplink Shared Channel (PUSCH) decoding, and data transmission procedures. Based on this analytical model, we derive the analytical expressions for the overall packet transmission success probability in each RACH attempt. Our results show that 2-step SDT RA scheme provides the highest overall packet transmission success probability, but performance gain decreases with the increase of device intensity.

The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalization of classic synchronous and asynchronous FL. Taking the resource-constrained and unreliable nature of wireless networks into account, we jointly consider the user selection and bandwidth optimization problem to minimize the FL training loss. The optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed greedy search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.

Grant-free sparse code multiple access (GF-SCMA) is considered to be a promising multiple access candidate for future wireless networks. In this article, we focus on characterizing the performance of uplink GF-SCMA schemes in a network with ubiquitous connections, such as the Internet-of-Things (IoT) networks. To provide a tractable approach to evaluate the performance of GF-SCMA, we first develop a theoretical model taking into account the property of multiuser detection (MUD) in the SCMA system. Then, the error rate performance of GF-SCMA in the case of codebook collision is analyzed to investigate the reliability of GF-SCMA when reusing codebook in massive IoT networks. For performance evaluation, accurate approximations for both success probability and average symbol error probability (ASEP) are derived. To elaborate further, the analytical results are utilized to discuss the impact of codeword sparse degree in GF-SCMA. After that, we conduct a comparative study between SCMA and its variant, dense code multiple access (DCMA), with GF transmission to offer insights into the effectiveness of these two schemes. This facilitates the GF-SCMA system design in practical implementation. Simulation results show that denser codebooks can help to support more user equipments (UEs) and increase the reliability of data transmission in a GF-SCMA network. Moreover, a higher success probability can be achieved by GF-SCMA with denser UE deployment at low detection thresholds since SCMA can achieve overloading gain.

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.

The quality of experience (QoE) requirements of wireless virtual reality (VR) can only be satisfied with high data rate, high reliability, and low VR interaction latency. This high data rate over short transmission distances may be achieved via the abundant bandwidth in the terahertz (THz) band. However, THz waves experience severe signal attenuation, which may be compensated by the reconfigurable intelligent surface (RIS) technology with programmable reflecting elements. Meanwhile, the low VR interaction latency can be achieved with the mobile edge computing (MEC) network architecture due to its computation capabilities. Motivated by these considerations, in this paper, we propose an MEC-enabled and RIS-assisted THz VR network in an indoor scenario, by taking into account the uplink viewpoint prediction and position transmission, the MEC rendering, and the downlink transmission. We propose two methods, which are referred to as centralized online gated recurrent unit (GRU) and distributed federated averaging (FedAvg), to predict the viewpoints of the VR users. In the uplink, an algorithm that integrates online long-short term memory (LSTM) and convolutional neural networks (CNN) is deployed to predict the locations and the line-of-sight and non-line-of-sight statuses of the VR users over time. In the downlink, we develop a constrained deep reinforcement learning algorithm to select the optimal phase shifts of the RIS under latency constraints. Simulation results show that our proposed learning architecture achieves near-optimal QoE as that of the genie-aided benchmark algorithm, and about two times improvement in QoE compared to the random phase shift selection scheme.

The components with molecular communication (MC) functionalities can bring an opportunity for emerging applications in fields from personal healthcare to modern industry. In this paper, we propose the designs of the microfluidic transmitter and receiver with quadruple concentration shift keying (QCSK) modulation and demodulation functionalities. To do so, we first present an AND gate design, and then apply it to the QCSK transmitter and receiver design. The QCSK transmitter is capable of modulating two input signals to four different concentration levels, and the QCSK receiver can demodulate a received signal to two outputs. More importantly, we also establish a mathematical framework to theoretically characterize our proposed microfluidic circuits. Based on this, we first derive the output concentration distribution of our proposed AND gate design, and provide the insight into the selection of design parameters to ensure an exhibition of desired behavior. We further derive the output concentration distributions of the QCSK transmitter and receiver. Simulation results obtained in COMSOL Multiphysics not only show the desired behavior of all the proposed microfluidic circuits, but also demonstrate the accuracy of the proposed mathematical framework.

We investigate the optimal signal detection problem in large-scale multiple-input multiple-output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be formulated as a closest lattice point search (CLPS). To identify invalid signals, an efficient pruning strategy is needed while searching on the GSM decision tree. However, the existing algorithms have exponential complexity, whereas they are infeasible in large-scale GSM-MIMO systems. In order to tackle this problem, we propose a memory-efficient pruning strategy by leveraging the combinatorial nature of the GSM signal structure. Thus, the required memory size is squared to the number of transmit antennas. We further propose an efficient memory-bounded maximum likelihood (ML) search (EM-MLS) algorithm by jointly employing the proposed pruning strategy and the memory-bounded best-first algorithm. Theoretical and simulation results show that our proposed algorithm can achieve the optimal bit error rate (BER) performance, while its memory size can be bounded. Moreover, the expected time complexity decreases exponentially with increasing the signal-to-noise ratio (SNR) as well as the system’s excess degree of freedom, and it often converges to squared time under practical scenarios.

In this paper, we investigate a secure mobile edge computing (MEC) network in the presence of multiple eavesdroppers, where multiple users can offload parts of their tasks to the computational access point (CAP). The multiple eavesdroppers may overhear the confidential task offloading, which leads to information leakage. In order to address this issue, we present the minimization problem of the secrecy outage probability (SOP), by jointly taking into account the constraints from the latency and energy consumption. With the aim to improve the system secrecy performance, we then introduce three user selection criteria to choose the best user among multiple ones. Specifically,criterion Imaximizes the locally computational capacity, whilecriterion IIandIIImaximize the secrecy capacity and data rate of main links, respectively. For these criteria, we further analyze the system secrecy performance by deriving analytical and asymptotic expressions for the SOP, from which we can conclude important insights for the system design. Finally, simulation and analytical results are provided to verify the proposed analysis. The results show that the three criteria can efficiently safeguard the MEC networks, compared to the traditional local computing and fully offloading, especially with a large value of user number.

Fifth Generation (5G) New Radio (NR) does not support data transmission during random access (RA) procedures, which results in unnecessary control signalling overhead and power consumption, especially for small data transmission (SDT). Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state. To compare the 4/2-step SDT RA schemes with the benchmark 4/2-step RA schemes, we provide a spatio-temporal analytical framework to evaluate the RA schemes, which jointly models the preamble detection, Physical Uplink Shared Channel (PUSCH) decoding, and data transmission procedures. Based on this analytical model, we derive the analytical expressions for the overall packet transmission success probability and average throughput in each RACH attempt. We also derive the average energy consumption in each RACH attempt. Our results show that 2-step SDT RA scheme provides the highest overall packet transmission success probability, and the lowest average energy consumption, but the performance gain decreases with the increase of device intensity.

With the stringent requirement of receiving video from the unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access-points (AP). To explore the benefit of broadcasting user-correlated decode-dependent video resources to spatially correlated VR users, the network should dynamically schedule the video and cluster APs into virtual cells for a different group of VR users with overlapped video requests. By decomposing the problem into scheduling and association sub-problems, we first introduce the conventional non-learning-based scheduling and association algorithms, and a centralized deep reinforcement learning (DRL) association approach based on the rainbow agent with a convolutional neural network (CNN) to generate decisions from observation. To reduce its complexity, we then decompose the association problem into multiple sub-problems, resulting in a networked-distributed Partially Observable Markov decision process (ND-POMDP). To solve it, we propose a multi-agent deep DRL algorithm. To jointly solve the coupled association and scheduling problems, we further develop a hierarchical federated DRL algorithm with scheduler as meta-controller, and association as the controller. Our simulation results show that our CF-MB network can effectively handle real-time video transmission from UAVs to VR users. Our proposed learning architecture is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users. Also, our proposed algorithms outperform non-learning based methods with significant performance improvement.

The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.

Synthetic biology offers a tool to build biological entities that are capable of carrying out desired signal processing functionalities by controlling and engineering biochemical signaling pathways. The design and study of genetic circuits that exhibit natural behavior can be helpful for an improved understanding of the principles and kinetics behind the gene expression behavior, as well as for engineering cellular systems for synthetic biology. In this paper, we propose a synthetic system capable of generating a pulse-shaped signal, which is a prevalent behavior in natural environment. In particular, our proposed system is based on an engineered cell with NOR logic operation, and the pulse generation exploits the alteration in logic states of NOR inputs that are controlled by two types of diffusive signaling molecules. To quantitatively describe the generated pulse, we not only derive the propagation channel of the signaling molecules but also analyze the behavior of the engineered cell using Shea-Ackers formalism. Simulation results demonstrate that the pulse-shaped signal can be successfully produced in a controllable manner.

The quality of experience (QoE) requirement of wireless virtual reality (VR) can only be satisfied with high data rate, high reliability, and low VR interaction latency. This high data rate over short transmission distances may be achieved via the abundant spectrum in the terahertz (THz) band. However, THz waves suffer from severe signal attenuation, which may be compensated by the reconfigurable intelligent surface (RIS) technology with adjustable phase-shift of each reflecting element. Motivated by these considerations, in this paper, we propose an RIS-assisted THz VR network in an indoor scenario, taking into account the viewpoint prediction and downlink transmission. We first propose a genie-aided online gated recurrent unit (GRU) and integration of online long-short term memory (LSTM) and convolutional neural network (CNN) algorithm to predict the viewpoint, location, and the line-of-sight (LoS) and non-line-of-sight (NLoS) statuses of the VR users over time, with the aim to optimize the long-term QoE of the VR users. We then develop a constrained deep reinforcement learning algorithm to select the optimal phase shifts of the RIS for the downlink transmission under latency constraints. Simulation results show that the proposed ensemble learning architecture achieves near-optimal QoE as that of an exhaustive algorithm, and about two times improvement in QoE compared to the random phase shift selection scheme.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to maximize data rate through the optimal beam-pairs efficiently, upon every communication request from UAV inside the multi-location environment. We compare our proposed framework against Multi-Armed Bandit (MAB) learning-based approach and the traditional exhaustive approach, respectively and also analyse the training performance of DQN-based beam alignment over different coverage area requirements and channel conditions. Our results show that the proposed DQN-based beam alignment converge faster and generic for different environmental conditions. The framework can also learn optimal beam alignment comparable to the exhaustive approach in an online manner under real-time conditions.

With the stringent requirement of receiving video from unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access points (AP). To explore the benefit of broadcasting user-correlated decode-dependent video resources to spatially correlated VR users, the network should dynamically cluster APs into virtual cells for a different group of VR users with overlapped video requests. We first introduce the conventional non-learning-based association algorithms. We then formulate the association problem into a networked-distributed Partially Observable Markov decision process (ND-POMDP). To solve it, we propose a multi-agent deep DRL algorithm based on the rainbow agent with a convolutional neural network (CNN) to generate decisions from observation. Our simulation results shown that our CF-MB network can effectively handle real-time video transmission from UAVs to VR users. Our proposed learning architectures is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users. Also, our proposed algorithms outperform non-learning based methods with significant performance improvement.

Wireless Virtual Reality (VR) users are able to enjoy immersive experience from anywhere at anytime. However, providing full spherical VR video with high quality under limited VR interaction latency is challenging. If the viewpoint of the VR user can be predicted in advance, only the required viewpoint is needed to be rendered and delivered, which can reduce the VR interaction latency. Therefore, in this paper, we use online learning algorithms to predict viewpoint of the VR user using real VR dataset. For the online learning algorithm, based on the VR user’s actual viewpoint delivered through uplink transmission, we compare it with the predicted viewpoint and update the parameters of the online learning algorithm to further improve the prediction accuracy. To guarantee the reliability of the uplink transmission, we integrate the Proactive retransmission scheme into our proposed online learning algorithm. Simulation results show that the online learning algorithm integrated with the proactive retransmission scheme can achieve about 5% prediction error.

Queuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either transmit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned-aerial-vehicle (UAV)-mounted base stations (BSs) for proactively adjusting the aerial BS (ABS)’s placement in accordance with wireless teletraffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS’s battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless teletraffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless teletraffic dynamics is available, and for the case where only their statistical knowledge is available. In contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system’s performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability.

Smartphone-based indoor localization has attracted considerable attentions in both research and industrial areas. However, the localization accuracy and robustness are still challenging problems due to low-cost noisy devices, especially in those complicated localization environments. Considering that pedestrian dead-reckoning (PDR) devices are widely equipped in recent smartphones, we propose a novel indoor localization fusing algorithm that integrates both wireless fidelity (WiFi) features and PDR features. By formulating the fusing indoor localization as a recursive function approximation problem, a sliding-window-based displacement scheme is designed to generate a time-series-based feature data set. We further apply the long short-term memory (LSTM) network for data fusion and localization on this data set by taking advantage of its benefits in time-series prediction and characterization. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art filter-based localization algorithms in three typical movements and three postures of holding smartphones. Extensive experiment results demonstrate the accuracy and robustness of the proposed algorithm in indoor localization, even in some extreme environments.

Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems. However, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision from the simultaneous massive access. Despite that this collision problem has been treated in existing RACH schemes, these schemes usually organize IoT devices' transmission and re-transmission along with fixed parameters, thus can hardly adapt to time-varying traffic patterns. Without adaptation, the RACH procedure easily suffers from high access delay, high energy consumption, or even access unavailability. With the goal of improving the RACH procedure, this paper targets to optimize the RACH procedure in real-time by maximizing a long-term hybrid multi-objective function, which consists of the number of access success devices, the average energy consumption, and the average access delay. To do so, we first optimize the long-term objective in the number of access success devices by using Deep Reinforcement Learning (DRL) algorithms for different RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). The converging capability and efficiency of different DRL algorithms including Policy Gradient (PG), Actor-Critic (AC), Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG) are compared. Inspired by the results from this comparison, a decoupled learning strategy is developed to jointly and dynamically adapt the access control factors of those three access schemes. This decoupled strategy integrates predicted traffic into the learning process to improve training efficiency, where a Recurrent Neural Network (RNN) model is first employed to predict the real-time traffic values of the network environment, and then multiple DRL agents are employed to cooperatively configure parameters of each RACH scheme. Our results demonstrate that the decoupled strategy remarkably accelerate the training speedy.

5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grant-based (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this article. Specifically, we present a spatio-temporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments.

Supporting reliable and seamless connectivity for aerial users, such as Unmanned Aerial Vehicles (UAVs), is one of the key challenges for the next-generation cellular networks. To tackle this challenge, we propose a joint beamforming, user association, and UAV-height control framework for cellular-connected multi-UAV communications. Our objective is to maximize the minimum achievable rate for UAVs subject to co-existing terrestrial users' rate constraints. A hierarchical bi-layer iterative algorithm is devised to solve the problem. By using the projection gradient method in inner layer iterations and the geometric program modeling plus the convex-concave procedure in outer layer iterations, our proposed algorithm is proved to converge to a local optimum. We also examine our proposed algorithm under the practical condition where channel estimation is not perfect. Numerical results show that our proposed joint beamforming, user association, and UAV-height control framework outperforms the conventional nearest UAV association method in terms of UAVs' minimum achievable rate for both perfect and imperfect channel estimation cases. We also observe different UAVs' heights (i.e., between 100m and 300m) do not affect the UAVs' achievable rates. For the case with moving UAV, we also study the trade-off between UAVs' minimum achievable rate and the frequency of updating optimization variables.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for a massive number of low-throughput low-cost devices in delay-tolerant applications with low power consumption. To provide reliable connections with extended coverage, a repetition transmission scheme is introduced to NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, the energy harvesting is considered as a promising solution to support energy sustainability in the NB-IoT network. In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission. We model the temporal dynamics of the energy level as a birth-death process, derive the energy availability of each IoT device, and examine its dependence on the energy storage capacity and the repetition value. We show that in certain scenarios, the energy availability remains unchanged despite randomness in the energy harvesting. We also derive the exact expression for the RACH success probability of a randomly chosen IoT device under the derived energy availability, which is validated under different repetition values via simulations. We show that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario.

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternating optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements.

Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from anywhere at anytime. However, providing wireless VR users with seamless connectivity and real-time VR video with high quality is challenging due to its requirements in high Quality of Experience (QoE) and low VR interaction latency under limited computation capability of VR device. To address these issues, we propose a MEC-enabled wireless VR network, where the field of view (FoV) of each VR user can be real-time predicted using Recurrent Neural Network (RNN), and the rendering of VR content is moved from VR device to MEC server with rendering model migration capability. Taking into account the geographical and FoV request correlation, we propose centralized and distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize the long-term QoE of VR users under the VR interaction latency constraint. Simulation results show that our proposed MEC rendering schemes and DRL algorithms substantially improve the long-term QoE of VR users and reduce the VR interaction latency compared to rendering at VR devices.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values and transmission resources. To characterize the RACH performance of the NB-IoT network with three CE groups, this paper develops a novel traffic-aware spatio-temporal model to analyze the RACH success probability, where both the preamble transmission outage and the collision events of each CE group jointly determine the traffic evolution and the RACH success probability. Based on this analytical model, we derive the analytical expression for the RACH success probability of a randomly chosen IoT device in each CE group over multiple time slots with different RACH schemes, including baseline, back-off (BO), access class barring (ACB), and hybrid ACB and BO schemes (ACB&BO). Our results have shown that the RACH success probabilities of the devices in three CE groups outperform that of a single CE group network but not for all the groups, which is affected by the choice of the categorizing parameters.This mathematical model and analytical framework can be applied to evaluate the performance of multiple group users of other networks with spatial separations.

In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption.

In this paper, we study the resource allocation for a secure mission-critical IoT communication system with URLLC, where the security capacity formula under finite blocklength is adopted. In specific, we jointly optimize the power and channel bandwidth unit allocation to minimize the system power consumption subject to each device's security capacity requirement and total available channel bandwidth. We express the power for each device as a function of channel bandwidth unit, and equivalently transform the original problem into a channel bandwidth unit allocation problem. By relaxing the discrete variables into continuous ones, a sufficient condition when the transformed problem is a convex problem is provided. Efficient method is proposed to solve the problem. Simulation results confirm the performance advantage of our proposed algorithm over the benchmark method.

Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems, however, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision during the simultaneous massive. Despite that this collision problem has been treated in existing RACH schemes by organizing IoT devices' transmission and retransmission via the central control at the Base Station (BS), these existing RACH schemes are usually fixed over time, thus can hardly adapt to time-varying traffic patterns. In order to optimize the long-term objective in the number of success devices, this paper aims to design Deep Reinforcement Learning (DRL)-based optimizers with Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG) for optimizing RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). Specifically, we apply DQN to handle discrete action selection for the BO as well as the DQ schemes, and DDPG to handle continuous action selection for the ACB scheme. Both agents are integrated with Gated recurrent unit Gated Recurrent Unit (GRU) network to approximate their value function/policy, which can improve the optimization performance by capturing temporal traffic correlations. Numerical results showcase that our proposed DRL-based optimizers considerably outperform conventional heuristic solutions in terms of the number of success access devices.

Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral- and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS. Pertinent latency-minimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about 20 % computational latency reduction is achieved over the conventional MEC system in a single cell of a 300 m radius and 5 active devices, relying on a 5-antenna access point.

The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the achievable data rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

The design of communication systems capable of processing and exchanging information through molecules and chemical processes is a rapidly growing interdisciplinary field, which holds the promise to revolutionize how we realize computing and communication devices. While molecular communication (MC) theory has had major developments in recent years, more practical aspects in designing components capable of MC functionalities remain less explored. This paper designs chemical reactions-based microfluidic devices to realize binary concentration shift keying (BCSK) modulation and demodulation functionalities. Considering existing MC literature on information transmission via molecular pulse modulation, we propose a microfluidic MC transmitter design, which is capable of generating continuously predefined pulse-shaped molecular concentrations upon rectangular triggering signals to achieve the modulation function. We further design a microfluidic MC receiver capable of demodulating a received signal to a rectangular output signal using a thresholding reaction and an amplifying reaction. Our chemical reactions-based microfluidic molecular communication system is reproducible and its parameters can be optimized. More importantly, it overcomes the slow-speed, unreliability, and non-scalability of biological processes in cells. To reveal design insights, we also derive the theoretical signal responses for our designed microfluidic transmitter and receiver, which further facilitate the transmitter design optimization. Our theoretical results are validated via simulations performed through the COMSOL Multiphysics finite element solver. We demonstrate the predefined nature of the generated pulse and the demodulated rectangular signal together with their dependence on design parameters.

Ultra-reliable low latency communication (URLLC) is one of three primary use cases in the fifth-generation (5G) networks, and its research is still in its infancy due to its stringent and conflicting requirements in terms of extremely high reliability and low latency. To reduce latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and higher decoding error probability. In this case, conventional resource allocation based on Shannon capacity achieved with infinite blocklength codes is not optimal. Security is another critical issue in mission-critical internet of things (IoT) communications, and physical-layer security is a promising technique that can ensure the confidentiality for wireless communications as no additional channel uses are needed for the key exchange as in the conventional upper-layer cryptography method. This paper is the first work to study the resource allocation for a secure mission-critical IoT communication system with URLLC. Specifically, we adopt the security capacity formula under finite blocklength and consider two optimization problems: weighted throughput maximization problem and total transmit power minimization problem. Each optimization problem is non-convex and challenging to solve, and we develop efficient methods to solve each optimization problem. Simulation results confirm the fast convergence speed of our proposed algorithm and demonstrate the performance advantages over the existing benchmark algorithms.

In order to satisfy the requirement of high throughput in most UAV applications, the potential of integrating millimeter wave (mmWave) communications with UAV networks is explored in this paper. A tractable three-dimensional (3D) spatial model is proposed for evaluating the average downlink performance of UAV networks at mmWave bands, where the locations of UAVs and users are randomly distributed with the aid of a Poisson cluster process. Moreover, an actual 3D antenna model with the uniform planar array is deployed at all UAVs to examine the impact of both azimuth and elevation angles. Based on this framework and two typical user selection schemes, closed-form approximation equations of the evaluated coverage probability and area spectral efficiency (ASE) are derived. In a noise-limited scenario, an exact expression is provided, which theoretically demonstrates that a large scale of antenna elements is able to enhance the coverage performance. Regarding the altitude of UAVs, there exists at least one optimal height for maximizing the coverage probability. Numerical results verify the proposed insight that non-line-of-sight transmission caused by obstacles have negligible effects on the proposed system. Another interesting result is that the ASE can be maximized by optimizing both the targeted data rate and the density of UAVs.

The emerging mulsemedia (MULtiple SEnsorial MEDIA) introduces new sensorial data (haptic, olfaction, gustation, etc.), significantly augmenting the conventional audio-visual communication. This can be used in many areas, such as immersive entertainment and innovative education. Previous research has been dedicated to evaluating the impact of other sensorial data on conventional multimedia; however, standalone quality evaluation of new sensorial data, especially vibrotactile data (a type of haptic data), has not been covered. To the best of our knowledge, this paper is the first to empirically demonstrate that the common statistical metrics in audio and visual domains, i.e. signal-to-noise ratio (SNR) and Structural SIMilarity (SSIM), are highly correlated with human vibrotactile perception as well. To be specific, we propose a testing protocol for vibrotactile quality evaluation and conduct subjective experiments. The results suggest that SNR and SSIM are applicable to vibrotactile quality assessment. We also consider a practical scenario where the quality of vibrotactile data varies with time. Based on the validation of SNR and SSIM in the first part, we present an objective metric as a hybrid composition of SNR and SSIM. Instead of assessing the quality of data using an overall score, the hybrid metric evaluates the quality in a time-varying manner. Subjective experiments are conducted and the results demonstrate that the correlation coefficient can be significantly increased using the hybrid metric.

To enhance transmission performance, privacy level, and energy manipulating efficiency of wireless networks, this article initiates a novel simultaneous wireless information and power transfer (SWIPT) full-duplex (FD) relaying protocol, named harvest-and-opportunistically-relay (HOR). Due to the FD characteristics, the dynamic fluctuation of relay's residual energy is difficult to quantify and track. To solve this problem, the Markov Chain (MC) theory is invoked. Furthermore, to improve the privacy level of the proposed HOR relaying system, covert transmission performance analysis is performed, where closed-form expressions of the optimal detection threshold and minimum detection error probability are derived. Last but not least, with the aid of stationary distribution of the MC, closed-form expression of transmission outage probability is calculated, based on which transmission outage performance is analyzed. Numerical results have validated the correctness of analyses on transmission outage and covertness. The impacts of key system parameters on the performance of transmission outage and covertness are given and discussed. Based on mathematical analysis and numerical results, we showcase that the proposed HOR model can not only reliably enhance the transmission performance via smartly managing residual energy but also efficiently improve the privacy level of the legitimate transmission party via dynamically adjusting the optimal detection threshold.

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications defined in the fifth generation new radio (5G NR), and its research is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10-9packet loss probability) and low latency (say 1 ms) simultaneously. In URLLC, short packet transmission is adopted to reduce latency, such that conventional Shannon's capacity formula is no longer applicable, and the achievable data rate in finite blocklength becomes a complex expression with respect to the decoding error probability and the blocklength. To provide URLLC service in a factory automation scenario, we consider that the central controller transmits different packets to a robot and an actuator, where the actuator is located far from the controller, and the robot can move between the controller and the actuator. In this scenario, we consider four fundamental downlink transmission schemes, including orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA (C-NOMA) schemes. For all these transmission schemes, we aim for jointly optimizing the blocklength and power allocation to minimize the decoding error probability of the actuator subject to the reliability requirement of the robot, the total energy constraints, as well as the latency constraints. We further develop low-complexity algorithms to address the optimization problems for each transmission scheme. For the general case with more than two devices, we also develop a low-complexity efficient algorithm for the OMA scheme. Our results show that the relay-assisted transmission significantly outperforms the OMA scheme, while the NOMA scheme performs well when the blocklength is very limited. We further show that the relay-assisted transmission has superior performance over the C-NOMA scheme due to larger feasible region of the former scheme.

In this paper, we consider an unmanned aerial vehicle (UAV) enabled wireless network with a set of ground devices that are randomly distributed in an area and each having a certain amount of data for transmission. The UAV flies over this region from a starting point to a destination. During its flight, the UAV wants to communicate to the ground devices for maximizing the cumulative collected data by optimizing the trajectory of the UAV subject to its flight time constraint. Due to uncertainty in the locations of the ground devices and the communication dynamics, an accurate system model is difficult to acquire and maintain. With the help of stochastic modelling, we present a reinforcement learning based automated trajectory optimization algorithm. By dividing the considered region into small grids with finite state space and action space, we apply the Q-learning based automated trajectory optimization approach for maximizing the cumulative collected data during its flight time. Simulation results demonstrate that the reinforcement learning approach can find an optimal strategy under the flight time constraint.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values. Rather than our previous work only modeled RACH success probability in NB-IoT networks with a single CE group, this paper develops a novel model to analyze the RACH success probabilities in NB-IoT networks with three CE groups, which allow flexible RACH configuration for each CE group. Based on this analytical model, we derive the expression for the RACH success probability of a randomly chosen IoT device in each CE group. The analytical results can also be extended to analyze multiple group users of other networks with spatial separations.

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resources allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, the problem is to determine, in an online fashion at each Transmission Time Interval (TTI), the configurations that maximizes the long-term average number of IoT devices that are able to both access and deliver data. Given the complexity of optimal algorithms, a Cooperative Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is developed, whereby each DQN agent independently control a configuration variable for each group. The DQN agents are cooperatively trained in the same environment based on feedback regarding transmission outcomes. CMA-DQN is seen to considerably outperform conventional heuristic approaches based on load estimation.

To provide reliable connections with extended coverage in NarrowBand-Internet of Things (NB-IoT), a repetition transmission scheme is introduced during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, energy harvesting from natural resources is considered to be a promising solution to support energy sustainability of NB-IoT network. In this work, we analyze RACH in the self-powered NB-IoT network taking into account the repeated preamble transmission and collision using stochastic geometry. We model the temporal dynamics of the energy level as a birth-death process, and we derive the energy availability of each IoT device and examine its dependence on the energy storage capacity, the cutoff value, and the repetition value. We also derive the exact expression for the RACH success probability of NB-IoT network under time correlated interference and the energy availability, which is validated under different repetition values via practical packet evolution simulations.

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10-9) and low latency (say 1 ms) simultaneously. The achievable data rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transniitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstrate that the relay-assisted transmission significantly outperforms the OMA scheme.

NarrowBand Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in “how to determine the configuration that maximizes the long-term average number of served IoT devices at each transmission time interval (TTI) in an online fashion.” Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the linear approximation-based Q-learning (LA-Q), and the deep neural network-based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning-based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via actions aggregation (AA-LA-Q and AA-DQN) and via cooperative multi-agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency.

Due to the low traffic congestion, high fuel efficiency, and comfortable travel experience, vehicular platoon systems (VPSs) become one of the most promising applications in millimeter wave (mmWave) vehicular networks. In this paper, an effective spatial framework for mmWave vehicle-to-everything (V2X) networks with VPSs is proposed by utilizing stochastic geometry approaches. Base stations (BSs) are modeled by a Poisson point process and vehicles are distributed according to multiple type II Matérn hard-core processes. To characterize the blockage process caused by vehicles, a closed-form expression is deduced to distinguish line-of-sight (LOS) and non-LOS transmission. This expression demonstrates that LOS links are independent of horizontal communication distances. Several closed-form probability density functions of the communication distance between a reference platoon and its serving transmitter (other platoons or BSs) are derived for analyzing the generated path loss. After designing three practical user association techniques, tractable expressions for coverage probabilities are figured out. Our work theoretically shows that the maximum density of VPSs exists and large antenna scales benefit the networks' coverage performance. The numerical results illustrate that platoons outperform individual vehicles in terms of road spectral efficiency and the considered system is LOS interference-limited.

In recent years, communicating information using molecules via diffusion has attracted significant interest in bio-medical applications. To date, most of the studies have concentrated on point-to-point molecular communication (MC), whereas in a realistic environment, multiple MC transmitters are likely to transmit molecular messages simultaneously sharing the same propagation medium, resulting in significant performance variation of the MC system. In this type of large-scale MC system, the collective signal strength at the desired receiver can be impaired by the interference caused by other MC transmitters, which may degrade the system reliability and efficiency. This paper presents the first tractable analytical framework for the collective signal strength at a partially absorbing receiver due to the desired transmitter under the impact of a swarm of interfering transmitters in a 3D large-scale MC system using stochastic geometry. To combat the multi-user interference and the intersymbol interference (ISI) in the multi-user environment, we propose Reed-Solomon (RS) error correction coding, due to its high effectiveness in combating burst and random errors, as well as the two types of information molecule modulating scheme, where the transmitted bits are encoded using two types of information molecules at consecutive bit intervals. We derive analytical expressions for the bit error probability (BEP) of the large-scale MC system with the proposed two schemes to show their effectiveness. The results obtained using Monte Carlo simulations, match exactly with the analytical results, justifying the accuracy of the derivations. Results reveal that both schemes improve the BEP by a factor of 3-4 compared with that of a conventional MC system without using any ISI mitigation techniques. Due to the implementation simplicity, the two-type molecule encoding scheme is better than the RS error correction coding scheme, as the RS error correction coding scheme involves additional encoding and decoding process at both the transmitter and receiver nodes. Furthermore, the proposed analytical framework can be generalized to the analysis of other types of receiver designs and performance characterization in multi-user large-scale MC systems. Also, the two types of information molecule modulating scheme can be extended to M-type of information molecule modulating scheme without loss of generality.

In recent years, unmanned aerial vehicle (UAV) communication technology has played an important role in both military and civilian applications. However, with the rapid development of military equipment, the execution efficiency of single UAVs is often limited, for which complex combat missions cannot be completed well. Therefore, UAV swarm has become an important research trend in the field of UAVs. In this paper, we consider the problem of channel estimation and self-positioning for the UAV swarm, where multiple small UAVs are displaced by arbitrarily unknown displacements due to the dynamic moving. To explore the physical characteristics of UAV swarm, the parameters of the channel are decomposed into the direction of arrival (DOA) information, the relative position information, and the channel gain information. Utilizing the rank reduction (RARE) estimator, DOAs of the different target users can be estimated efficiently, regardless of the position of the UAVs. After obtaining the DOA information, we estimate the channel gain information using small amount of training resources, which significantly reduces the training overhead and the feedback cost. Moreover, the unknown displacements among UAVs can be self-recovered from the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop both the sphere decoding (SD) and the least square (LS) based methods. The deterministic Cramér-Rao bound (CRB) of the self-positioning estimation is derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.

In this paper, we investigate the secrecy performance of energy-constrained wireless-powered networks with considering the passive eavesdropping scenario, where the simultaneous wireless information and power transfer-based full-duplex self-jamming (SWIPT-FDSJ) scheme is developed. The maximal ratio transmission protocol is applied at the multi-antenna source such that the wireless signals are designated to the destination directly. Besides, the energy harvesting and full-duplex self-jamming operations are adopted at the energy-constrained destination to prolong its lifetime as well as to confuse the eavesdropper. Specifically, the exact and asymptotic closed-form expressions of the connection outage probability (COP), the secrecy outage probability (SOP), and the secrecy throughput of the proposed system are obtained, based on which we optimize the time-switching ratio to maximize the secrecy throughput. We also degenerate the proposed SWIPT-FDSJ scheme to the reduced half-duplex with no self-jamming (HDNSJ) scheme. The finds suggest that in the HDNSJ scheme, adding the antenna number of the source only benefits the COP performance, but has no impact on the SOP performance. By contrast, it will promote the COP and SOP performance at the same time in the SWIPT-FDSJ scheme, which eventually results in the great improvement of secrecy throughput. In addition, we present the practical application condition of the SWIPT-FDSJ scheme. It is demonstrated that the secrecy throughput performance of the SWIPT-FDSJ scheme is much superior to the HDNSJ scheme on condition that the application condition is satisfied.

To estimate the molecular communication (MC) parameters (e.g., diffusion coefficient, reaction rate, and absorption rate) via observations at the transmitter and the receiver, we present an analytical framework for a diffusive MC system with a partially absorbing receiver and a general first-order chemical reaction during propagation, in both spherically asymmetric and spherically symmetric scenarios. The time-varying spatial distributions and the expected numbers of messenger molecules and their first-order reaction products inside the transmitter, as well as at the surface of the partially absorbing receiver, are derived in both scenarios, which can be simplified in the special cases of a fully absorbing receiver. Importantly, our analytical expressions are verified by particle-based simulations, which showcase the effect of the reaction rate on the transmitter and the receiver observations. The analytical results of channel impulse responses at the absorbing receiver as well as that inside the transmitter are first treated and solved for spherically asymmetric scenario in this work.

The Random Access (RA) procedure is used to request channel resources for the uplink data transmission in the cellular-based massive Internet of Things (mIoT). To ease the RA failure and the network congestion, power ramping (PR) technique is used to step up the preamble transmit power after each unsuccessful RA attempt. In this paper, we develop a traffic aware spatio-temporal model to analyze the PR scheme in the mIoT network, where the Signal-to-Interference-and-Noise Ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared with existing literature only modelled collision from single cell perspective, we model both the SINR outage and the collision from the network perspective. Based on this analytical model, we derive the exact expression for the RA success probability to show the effectiveness of the PR scheme. Our results show that the geometry PR scheme with smooth increased transmission power is effective in heavy traffic scenario in terms of increasing the RA success probability.

In this study, the authors first propose a sequence with the impulse‐like autocorrelation property named zero autocorrelation code (ZAC) for orthogonal frequency division multiplexing (OFDM) based on offset quadrature amplitude modulation (OQAM) systems. The ZAC sequence can be generated by many prototype filters such as physical layer for dynamic spectrum access and cognitive radio and isotropic orthogonal transform algorithm filters. Thus, it has a wide application range. The authors then propose a synchronisation algorithm based on the ZAC. The proposed algorithm only needs two symbols to compose the preamble while existing algorithms need at least four symbols. They further assess the synchronisation performances via computer simulation. The simulation results show that the proposed algorithm can achieve better performance in terms of root mean square error of the timing and frequency offset estimation, with a 10 dB improvement on timing offset estimation performance over Saeedi‐Sourck's algorithm when signal‐to‐noise ratio is 0 dB.

Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that face a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The random access channel (RACH) procedure is the first step of connection establishment between IoT devices and base stations in the cellular-based mIoT network, where modeling the interactions between static properties of the physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio-temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network is modeled and analyzed based on stochastic geometry in the spatial domain, and the queue evolution is analyzed based on probability theory in the time domain. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with different RACH schemes in each time slot, which offer insights into the effectiveness of each RACH scheme. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device. This mathematical model and the analytical framework can be applied to evaluate the performance of other types of RACH schemes in the cellular-based networks by simply integrating its preamble transmission principle.

This paper proposes a novel direction-of-arrival (DOA)-aided channel estimation for a hybrid millimeter-wave (mm-wave) massive multiple-input multiple-output system with a uniform planar array at the base station. To explore the physical characteristics of the antenna array in mm-wave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the 2-D discrete Fourier transform and enhance the estimation accuracy via the angle rotation technique. We then estimate the channel gain information using a small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, to examine the estimation performance, we derive the theoretical bounds of the mean squared errors (MSEs) and the Cramér-Rao lower bounds (CRLBs) of the joint DOA and channel gain estimation. The simulation results show that the performances of the proposed methods are close to the theoretical MSEs' analysis. Furthermore, the theoretical MSEs are also close to the corresponding CRLBs.

The cellular-based infrastructure is regarded as one of the potential solutions for massive Internet of Things (mIoT), where the random access (RA) procedure is used for requesting channel resources in the uplink data transmission. Due to the nature of the mIoT network with the sporadic uplink transmissions of a large amount of IoT devices, massive concurrent channel resource requests lead to a high probability of RA failure. To relieve the congestion during the RA in mIoT networks, we model RA procedure and analyze as well as evaluate the performance improvement due to different RA schemes, including power ramping (PR), back-off (BO), access class barring (ACB), hybrid ACB and back-off schemes, and hybrid power ramping and back-off (PR&BO). To do so, we develop a traffic-aware spatio-temporal model for the contention-based RA analysis in the mIoT network, where the signal-to-noise-plus-interference ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared to existing literature that only models collision from the single-cell perspective, we model both SINR outage and the collision from the network perspective. Based on this analytical model, we derive the analytical expression for the RA success probabilities to show the effectiveness of different RA schemes. We also derive the average queue lengths and the average waiting delays of each RA scheme to evaluate the packets accumulation status and packets serving efficiency. Our results show that our proposed PR&BO scheme outperforms other schemes in heavy traffic scenarios in terms of the RA success probability, the average queue length, and the average waiting delay.

Massive multiple input multiple output (MIMO) and full duplex (FD) communication are being considered as potential candidates for the spectrum efficient 5G wireless networks. In this paper, we develop a tractable model for downlink (DL) and uplink (UL) transmission in K-tier heterogeneous cellular networks (HCNs) with massive MIMO macrocells and full duplex (FD) small cells for spectrum efficiency. In the considered HCNs, the performance of the mobile user (MU) is limited by several sources of interference, specifically due to FD nature of small cell base stations (SBSs). A stochastic geometry based model of the proposed HCNs is provided which allows to derive the DL and UL rate coverage probabilities of such a system. Monte Carlo simulations confirm the accuracy of the analytical results, while numerical results reveal that equipping large number of MIMO antennas at macro base stations (MBSs) enhances the DL rate coverage probability of a random MU in HCNs. The results show that to achieve the maximum joint DL and UL performance gain in HCNs with FD small cells, both SBSs' density and SBSs' transmit power should be optimized. Moreover, the UL performance can be improved by decreasing the SBSs receivers sensitivity and increasing the UL power control factor.

The design of communication systems capable of processing and exchanging information through molecules and chemical processes is a rapidly growing interdisciplinary field, which holds the promise to revolutionize how we realize computing and communication devices. While molecular communication (MC) theory has had major developments in recent years, more practical aspects in the design and prototyping of components capable of MC functionalities remain less explored. In this paper, motivated by a bulk of MC literature on information transmission via molecular pulse modulation, the design of a pulse generator is proposed as an MC component able to output a predefined pulse-shaped molecular concentration upon a triggering input. The chemical processes at the basis of this pulse generator are inspired by how cells generate pulse-shaped molecular signals in biology. At the same time, the slow-speed, unreliability, and non-scalability of these processes in cells are overcome with a microfluidic-based implementation based on standard reproducible components with well-defined design parameters. Mathematical models are presented to demonstrate the analytical tractability of each component, and are validated against a numerical finite element simulation. Finally, the complete pulse generator design is implemented and simulated in a standard engineering software framework, where the predefined nature of the output pulse shape is demonstrated together with its dependence on practical design parameters.

This paper proposes a novel direction of arrival (DOA) estimation for hybrid millimeter wave (mmWave) massive MIMO systems with the uniform planar array (UPA) at base station (BS). To explore the physical characteristics of antenna array in mmWave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the two dimension discrete Fourier transform (2D-DFT) efficiently, and then the estimation accuracy can be further enhanced via the angle rotation technique. To examine the estimation performance, we derive the theoretical bounds of the mean squared error (MSE) performance of the DOA estimation in high signal-to-noise ratio (SNR) region. Simulation results are provided to corroborate the proposed studies, and show that the proposed DOA estimation method is close to the theoretical MSE performance.

Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that faces a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The contention-based random access procedure (RACH) is the first step of connection establishment between IoT devices and Base Stations (BSs) in the cellular-based mIoT network, where modelling the interactions between static properties of physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio- temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network are modelled and analyzed based on stochastic geometry, and the queue evolution are analyzed based on probability theory. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with baseline scheme in each time slot. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device.

Traditional cellular networks are moving towards heterogenous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all UEs, where CoMP improves the single-path availability, and CA enhances availability via multi carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP&CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a joint two-step optimization algorithm (JTOA), and our results showcase the effective of our proposed JTOA, and the effective of CoMP in availability improvement in HetNets.

Visible light communication (VLC) system based on orthogonal frequency division multiplexing (OFDM) is promising for its ability to link a huge number of devices with high reliability and low latency. However, OFDM‐based VLC systems are very sensitive to the symbol timing offsets. Thus, synchronisation algorithm is very important for OFDM‐based VLC system. In this study, the authors first propose zero correlation code pair (ZCC pair) and investigate the periodical impulse‐shaped correlation property of ZCC pair. Then the authors propose a novel timing offset estimation algorithm based on ZCC pair. The preamble is processed via peak‐to‐average power ratio (PAPR) reduction algorithms to overcome the PAPR problem in VLC systems. This algorithm achieves high accuracy and has low synchronisation error probability. The effectiveness of the authors’ proposed algorithm has been demonstrated using computer simulation under non‐light‐of‐sight VLC and Gaussian channels. The results show that the authors’ algorithm outperforms the Schmidl Cox's and Park's algorithms in terms of synchronisation error probability and mean square error of timing error.

Full duplex (FD) communication has emerged as an attractive solution for increasing the network throughput, by allowing downlink (DL) and uplink (UL) transmissions in the same spectrum. However, only employing FD base stations in heterogeneous cellular networks (HCNs) cause coverage reduction, due to the DL and UL interferences as well as the residual loop interference. We, therefore, propose HCNs with half duplex massive multiuser multiple-input multiple-output macrocell base stations (MBSs) to relax the coverage reduction, and FD small cell base stations (SBSs) to improve spectrum efficiency. A tractable framework of the proposed system is presented, which allows to derive exact and asymptotic expressions for the DL and the UL rate coverage probabilities, and the DL and the UL area spectral efficiencies (ASEs). Monte Carlo simulations confirm the accuracy of the analytical results, and it is revealed that the equipping massive number of antennas at MBSs enhances the DL rate coverage probability, whereas increasing FD SBSs increases the DL and the UL ASEs. The results also demonstrate that by tuning the UL fractional power control, a desirable performance in both UL and DL can be achieved.

Traditional cellular networks are moving toward heterogeneous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all user equipment (UE), where the CoMP improves the single-path availability, and the CA enhances availability via multi-carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP and CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a two-step optimization algorithm (TSOA) and a joint (JTOA). The TSOA is based on heuristic algorithm for the optimal subcarrier assignment and UE association, and based on the Lagrangian dual method for the power allocation. The JTOA is based on genetic algorithm to achieve the interaction between the first step and the second step. Our results showcase the effective of our proposed JTOA, and the effective of the CoMP in availability improvement in HetNets.

Full-duplex (FD) small cells provide a promising solution for meeting the sought requirements of future wireless networks, specifically, the capacity, coverage and spectral efficiency. Motivated by the recent developments in self-interference (SI) cancellation techniques, the main objective of this paper is to further investigate the impact of using fully FD-capable small cells on conventional HetNets. We analyse a two-tier heterogeneous cellular networks (HetNets), wherein tier 1 consists of legacy half-duplex (HD) macro base stations (BSs) while tier 2 consists of FD small cells. Based on the stochastic geometry approach, we develop a theoretical model and derive closed-form expressions for the outage probability of downlink macrocell users, in addition to downlink and uplink users of small cells since they operate in FD mode. Analytical and simulation results are provided to verify the derived expressions and evaluate the variation of different parameters on the network performance.

Spectrum aggregation provides a promising approach to improve the network capacity for Cognitive Radio Ad-Hoc Networks (CRAHNs). Resource allocation for spectrum aggregation-based CRAHNs has become one of the main issues. In this paper, we propose a cross-layer optimization for CRAHNs with the spectrum aggregation. The main objective of our paper is to maximize the network throughput under the network resource constraints. In this regard, we investigate the joint optimization for channel allocation, power control and routing under signal-to-interference-and- noise ratio (SINR) model. This cross-layer optimization problem is decomposed into two sub-problems: a resource allocation at the physical (PHY) layer, and a throughput optimization at the network layer. At the PHY layer, the particle swarm optimization algorithm is proposed to find the suboptimal solution, then at the network layer, linear programming is applied to evaluate the particle's fitness value for throughput maximization. The simulation results demonstrate that the joint optimization of channel allocation and power control is an effective way to improve network throughput, especially when the network has sufficient power supply.

Information delivery using chemical molecules is an integral part of biology at multiple distance scales and has attracted recent interest in bioengineering and communication. The collective signal strength at the receiver (i.e., the expected number of observed molecules inside the receiver), resulting from a large number of transmitters at random distances (e.g., due to mobility), can have a major impact on the reliability and efficiency of the molecular communication system. Modeling the collective signal from multiple diffusion sources can be computationally and analytically challenging. In this paper, we present the first tractable analytical model for the collective signal strength due to randomly-placed transmitters, whose positions are modelled as a homogeneous Poisson point process in three-dimensional (3D) space. By applying stochastic geometry, we derive analytical expressions for the expected number of observed molecules at a fully absorbing receiver and a passive receiver. Our results reveal that the collective signal strength at both types of receivers increases proportionally with increasing transmitter density. The proposed framework dramatically simplifies the analysis of large-scale molecular systems in both communication and biological applications.

The exponential growth in data traffic and dramatic capacity demand in fifth generation (5G) has inspired the move from traditional single-tier cellular networks towards heterogeneous cellular networks (HetNets). To face the coming trend in 5G, the high availability requirement in new applications, needs to be satisfied to achieve low latency service. In this work, we present a tractable multi-tier multi-band availability model to examine the high availability in carrier aggregation (CA)-enabled HetNets. We first derive a closed-form expression for the availability in CA- enabled HetNets based on the signal-to-interference- plus-noise model. By doing so, we formulate the joint subcarrier and power allocation problem, to maximize the availability under the power constraint. The optimization problem is non-convex problem, which is challenging to solve. To cope with it, the genetic algorithm (GA) is proposed to optimize availability through joint subcarrier and power allocation. The average availability in CA-enabled HetNets improves with decreasing the number of UEs, and increasing the power budget ratio interestingly.

This paper presents an analytical comparison of active and passive receiver models in diffusive molecular communication. In the active model, molecules are absorbed when they collide with the receiver surface. In the passive model, the receiver is a virtual boundary that does not affect molecule behavior. Two approaches are presented to derive transforms between the receiver signals. As an example, two models for an unbounded diffusion-only molecular communication system with a spherical receiver are unified. As time increases in the three-dimensional system, the transform functions have constant scaling factors, such that the receiver models are effectively equivalent. Methods are presented to enable the transformation of stochastic simulations, which are used to verify the transforms and demonstrate that transforming the simulation of a passive receiver can be more efficient and more accurate than the direct simulation of an absorbing receiver.

In this paper, we present an analytical model for a diffusive molecular communication (MC) system with a reversible adsorption receiver in a fluid environment. The time-varying spatial distribution of the information molecules under the reversible adsorption and desorption reaction at the surface of a bio-receiver is analytically characterized. Based on the spatial distribution, we derive the number of newly-adsorbed information molecules expected in any time duration. Importantly, we present a simulation framework for the proposed model that accounts for the diffusion and reversible reaction. Simulation results show the accuracy of our derived expressions, and demonstrate the positive effect of the adsorption rate and the negative effect of the desorption rate on the net number of newly-adsorbed information molecules expected. Moreover, our analytical results simplify to the special case of an absorbing receiver.

In this paper, we model and analyze the downlink (DL) wireless power transfer and uplink (UL) information transmission of K-tier heterogeneous cellular networks (HCNs). Due to the densely located BSs and universal frequency reuse between all tiers in HCNs, the typical mobile terminal (MT) is allowed to harvest energy from the serving BS by direct beamforming, as well as from the other interfering BSs. Equipped with large storage battery, the typical MT utilizes the harvested energy to provide constant transmit power for the UL information transmission. Stochastic geometry is used to model and evaluate the intrinsic relationship between the energy harvested from the BSs in the DL and the information transmission performance in the UL. To well evaluate the system performance, we derive exact expressions for the maximum transmit power at MT and the UL average ergodic rate. Our results show that the UL average ergodic rate per random MT is not significantly improved by increasing the energy conversion efficiency.

In this paper, we model and analyze the downlink (DL) wireless power transfer and uplink (UL) information transmission of K-tier heterogeneous cellular networks (HCNs) with randomly located base stations (BSs) and mobile terminals (MTs). In the DL and UL, each energy-constrained MT pairs up with its corresponding BS, which provides the maximum received power at the MT. Due to the densely located BSs and universal frequency reuse between all tiers in HCNs, the typical MT is allowed to harvest energy from the serving BS by direct beamforming as well as from the other interfering BSs. Equipped with large storage battery, the typical MT utilizes the harvested energy to provide constant transmit power for the UL information transmission. Stochastic geometry is used to model and evaluate the intrinsic relationship between the energy harvested from the BSs in the DL and the information transmission performance in the UL. To well evaluate the system performance, we first derive exact expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. As the number of BS antennas goes to infinity, we further derive asymptotic expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. Our results show that the UL outage probability per MT first decreases and then increases with increasing the time allocation factor (the fraction of time allocated to the DL), and the UL outage probability, and the UL average ergodic rate per MT, can be largely improved by using the massive antenna arrays at the BSs.

We investigate beamforming and artificial noise generation at the secondary transmitters to establish secure transmission in large scale spectrum sharing networks, where multiple noncolluding eavesdroppers attempt to intercept the secondary transmission. We develop a comprehensive analytical framework to accurately assess the secrecy performance under the primary users' quality of service constraint. Our aim is to characterize the impact of beamforming and artificial noise generation (BF&AN) on this complex large scale network. We first derive exact expressions for the average secrecy rate and the secrecy outage probability. We then derive an easy-to-evaluate asymptotic average secrecy rate and asymptotic secrecy outage probability when the number of antennas at the secondary transmitter goes to infinity. Our results show that the equal power allocation between the useful signal and artificial noise is not always the best strategy to achieve maximum average secrecy rate in large scale spectrum sharing networks. Another interesting observation is that the advantage of BF&AN over BF on the average secrecy rate is lost when the aggregate interference from the primary and secondary transmitters is strong, such that it overtakes the effect of the generated AN.

In this paper, a multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, including one base station (BS) equipped with multiple antennas, one desired single-antenna information receiver (IR), and N (N > 1) single-antenna energy-harvesting receivers (ERs) is considered. Assuming that the information signal to the desired IR may be eavesdropped by ERs if ERs are malicious, we investigate the secrecy performance of the target MISO SWIPT system when imperfect channel state information (CSI) is available and adopted for transmit antenna selection at the BS. Considering that each eavesdropping link experiences independent but not necessarily identically distributed Rayleigh fading, the closed-form expressions for the exact and the asymptotic secrecy outage probability, and the average secrecy capacity are derived and verified by simulations. Furthermore, the optimal power splitting factor is derived for each ER to realize the tradeoff between the energy harvesting and the information eavesdropping. Our results reveal the impact of the imperfect CSI on the secrecy performance of MISO SWIPT systems in the presence of multiple wiretap channels.

In this paper, a game theory-based cooperation scheme is investigated to enhance the physical layer security in both primary and secondary transmissions of a cognitive radio network (CRN). In CRNs, the primary network may decide to lease its own spectrum for a fraction of time to the secondary nodes in exchange of appropriate remuneration. We consider the secondary transmitter node as a trusted relay for primary transmission to forward primary messages in a decode-and-forward fashion and, at the same time, allows part of its available power to be used to transmit artificial noise (i.e., jamming signal) to enhance primary and secondary secrecy rates. In order to allocate power between message and jamming signals, we formulate and solve the optimization problem for maximizing the secrecy rates under malicious attempts from eavesdroppers. We then analyze the cooperation between the primary and secondary nodes from a game-theoretic perspective where we model their interaction as a Stackelberg game with a theoretically proved and computed Stackelberg equilibrium. We show that the spectrum leasing based on trading secondary access for cooperation by means of relay and jammer is a promising framework for enhancing security in CRNs.

This paper develops a tractable framework for exploiting the potential benefits of physical layer security in three-tier wireless sensor networks (WSNs) using stochastic geometry. In such networks, the sensing data from the remote sensors are collected by sinks with the help of access points, and the external eavesdroppers intercept the data transmissions. We focus on the secure transmission in two scenarios: 1) the active sensors transmit their sensing data to the access points and 2) the active access points forward the data to the sinks. We derive new compact expressions for the average secrecy rate in these two scenarios. We also derive a new compact expression for the overall average secrecy rate. Numerical results corroborate our analysis and show that multiple antennas at the access points can enhance the security of three-tier WSNs. Our results show that increasing the number of access points decreases the average secrecy rate between the access point and its associated sink. However, we find that increasing the number of access points first increases the overall average secrecy rate, with a critical value beyond which the overall average secrecy rate then decreases. When increasing the number of active sensors, both the average secrecy rate between the sensor and its associated access point, and the overall average secrecy rate decrease. In contrast, increasing the number of sinks improves both the average secrecy rate between the access point and its associated sink, and the overall average secrecy rate.

In this paper, we develop a tractable model for joint downlink (DL) and uplink (UL) transmission of K -tier heterogeneous cellular networks (HCNs) with simultaneous wireless information and power transfer (SWIPT) for efficient spectrum and energy utilization. In the DL, the mobile users (MUs) with power splitting receiver architecture decode information and harvest energy based on SWIPT. While in the UL, the MUs use the harvested energy for information transmission. Since cell association greatly affects the energy harvesting in the DL and the performance of wireless powered HCNs in the UL, we compare the DL and UL performance of a random MU in HCNs with nearest base station (NBS) cell association to that with maximum received power (MRP) cell association. We first derive the DL average received power for the MU with the NBS and the MRP cell associations. To evaluate the system performance, we then derive the outage probability and the average ergodic rate in the DL and UL of a random MU in HCNs with the NBS and MRP cell associations. Our results show that increasing the small cell base station (BS) density, the BS transmit power, the time allocation factor, and the energy conversion efficiency, weakly affects the DL and UL performance of both the cell associations. However, the UL performance of both the cell associations can be improved by increasing the fraction of the DL received power used for energy harvesting.

Wireless energy harvesting is regarded as a promising energy supply alternative for energy-constrained wireless networks. In this paper, a new wireless energy harvesting protocol is proposed for an underlay cognitive relay network with multiple primary user (PU) transceivers. In this protocol, the secondary nodes can harvest energy from the primary network (PN) while sharing the licensed spectrum of the PN. In order to assess the impact of different system parameters on the proposed network, we first derive an exact expression for the outage probability for the secondary network (SN) subject to three important power constraints: 1) the maximum transmit power at the secondary source (SS) and at the secondary relay (SR); 2) the peak interference power permitted at each PU receiver; and 3) the interference power from each PU transmitter to the SR and to the secondary destination (SD). To obtain practical design insights into the impact of different parameters on successful data transmission of the SN, we derive throughput expressions for both the delay-sensitive and the delay-tolerant transmission modes. We also derive asymptotic closed-form expressions for the outage probability and the delay-sensitive throughput and an asymptotic analytical expression for the delay-tolerant throughput as the number of PU transceivers goes to infinity. The results show that the outage probability improves when PU transmitters are located near SS and sufficiently far from SR and SD. Our results also show that when the number of PU transmitters is large, the detrimental effect of interference from PU transmitters outweighs the benefits of energy harvested from the PU transmitters.

The emerging fifth-generation (5G) wireless communication system is expected to provide higher capacity, seamless connectivity and reduced energy consumption to support data intensive multimedia applications. Simultaneous wireless information and power transfer (SWIPT) in heterogeneous cellular networks (HCNs) is a promising approach to offer efficient spectrum and energy utilization in the 5G system. In this paper, we develop a tractable model for joint uplink (UL) and downlink (DL) transmission in a K-tier HCN with SWIPT. In this model, we use the power splitting (PS) protocol where the receiver splits the received signal power in two parts for harvesting the energy and decoding the information. The harvested energy in the DL is utilized for UL information transmission. We derive the exact analytical expressions for the average received power and the outage probability for both DL and UL for the system design. Monte carlo simulations confirm the accuracy of the derived results, and numerical analysis reveal that SWIPT is a reasonably efficient technique to power the cellular users. In particular, we observe that with the increase of the picocell density, both the DL and the UL outage probability in macrocell decreases significantly. Moreover, the DL and the UL outage probability in a tier is shown to decrease with the increase of BS transmit power of its own tier.

This paper develops a tractable framework for exploiting the potential benefits of physical layer security in three-tier wireless sensor networks. In such networks, the sensing data from the remote sensors are collected by sinks with the help of access points, and the external eavesdroppers intercept the data transmissions. We adopt the stochastic geometry approach to model the random locations and spatial densities of the sensors, access points, sinks, and eavesdroppers. We focus on the secure transmission in two scenarios: i) the active sensors transmit their sensing data to the access points, and ii) the active access points forward the data to the sinks. We derive new compact expressions for the overall average secrecy rate in such networks. Numerical results corroborate our analysis and show that multiple- antenna technique at the access points can enhance the security. Our results show that the overall average secrecy rate first increases with increasing the number of access points, and there exists a critical value beyond which the overall average secrecy rate decreases with increasing the number of access points. When adding the number of active sensors, the overall average secrecy rate decreases. In contrast, increasing the number of sinks improves the overall average secrecy rate.

We investigate beamforming and artificial noise generation at the secondary transmitters to establish secure transmission in large scale spectrum sharing networks, where multiple non-colluding eavesdroppers attempt to intercept the secondary transmission. We develop a comprehensive analytical framework to accurately assess the secrecy performance under the primary user's quality of service constraint. Our aim is to characterize the impact of beamforming and artificial noise generation on this complex large scale network. We first derive the exact expressions for the average secrecy rate and the secrecy outage probability. Our results show that there exists an average secrecy rate wall beyond which the primary user's quality of service is violated. Interestingly, we find that different from the conventional network with fixed nodes where equal power allocation achieves near optimal average secrecy rate, the equal power allocation may not be a good option for large scale spectrum sharing networks.

In this paper, we propose cyclic prefix single carrier (CP-SC) full-duplex transmission in cooperative spectrum sharing to achieve multipath diversity gain and full-duplex spectral efficiency. Integrating full-duplex transmission into cooperative spectrum sharing systems results in two intrinsic problems: 1) the peak interference power constraint at the PUs are concurrently inflicted on the transmit power at the secondary source (SS) and the secondary relays (SRs); and 2) the residual loop interference occurs between the transmit and the receive antennas at the secondary relays. Thus, examining the effects of residual loop interference under peak interference power constraint at the primary users and maximum transmit power constraints at the SS and the SRs is a particularly challenging problem in frequency selective fading channels. To do so, we derive and quantitatively evaluate the exact and the asymptotic outage probability for several relay selection policies in frequency selective fading channels. Our results manifest that a zero diversity gain is obtained with full-duplex.

Molecular communication is set to play an important role in the design of complex biological and chemical systems. An important class of molecular communication systems is based on the timing channel, where information is encoded in the delay of the transmitted molecule - a synchronous approach. At present, a widely used modeling assumption is the perfect synchronization between the transmitter and the receiver. Unfortunately, this assumption is unlikely to hold in most practical molecular systems. To remedy this, we introduce a clock into the model - leading to the molecular timing channel with synchronization error. To quantify the behavior of this new system, we derive upper and lower bounds on the variance-constrained capacity, which we view as the step between the mean-delay and the peak-delay constrained capacity. By numerically evaluating our bounds, we obtain a key practical insight: the drift velocity of the clock links does not need to be significantly larger than the drift velocity of the information link, in order to achieve the variance-constrained capacity with perfect synchronization.

We propose transmit antenna selection with receive generalized selection combining (TAS/GSC) in dual-hop cognitive decode-and-forward (DF) relay networks for reliability enhancement and interference relaxation. In this paradigm, a single antenna which maximizes the receive signal-to-noise ratio (SNR) is selected at the secondary transmitter and a subset of receive antennas with the highest SNRs are combined at the secondary receiver. To demonstrate the impact of multiple primary users on the cognitive relay network, we derive new closed-form expressions for the exact and asymptotic outage probability with TAS/GSC in the secondary network. Several important design insights are reached. We corroborate that the full diversity gain is achieved, which is entirely determined by the total number of antennas in the secondary network. The negative impact of the primary network on the secondary network is reflected in the SNR gain.

We examine the impact of transmit antenna selection with receive generalized selection combining (TAS/GSC) for cognitive decode-and-forward (DF) relaying in Nakagami-m fading channels. We select a single transmit antenna at the secondary transmitter which maximizes the receive signal-to-noise ratio (SNR) and combine a subset of receive antennas with the largest SNRs at the secondary receiver. In an effort to assess the performance, we first derive the probability density function and cumulative distribution function of the end-to-end SNR using the moment generating function. We then derive new exact closed-form expression for the ergodic capacity. More importantly, by deriving the asymptotic expression for the high SNR approximation of the ergodic capacity, we gather deep insights into the high SNR slope and the power offset. Our results show that the high SNR slope is 1/2 under the proportional interference power constraint. Under the fixed interference power constraint, the high SNR slope is zero.

We propose transmit antenna selection with receive generalized selection combining in dual-hop cognitive decode-and-forward relay networks with spectrum sharing for reliability enhancement and interference relaxation. In this network, a single antenna, which maximizes the receive signal-to-noise ratio (SNR) is selected at the secondary transmitter, and a subset of receive antennas with the highest SNRs is combined at the secondary receiver. To demonstrate the advantages of our proposed framework, we derive new exact closed-form expressions for the outage probability and the symbol error rate of the secondary network in Rayleigh fading. We also derive easy-to-evaluate asymptotic expressions in the high-SNR regime to gain practical insights. Several important design insights are reached. Under the proportional interference power constraint, the full diversity gain is achieved and is entirely determined by the total number of antennas available in the secondary network. This result is independent of the number of receive antennas combined and the number of primary users. The positive impact of the number of receive antennas combined and the negative impact of the number of primary users on the secondary network are showcased in the SNR gain. Under the fixed interference power constraint, error floors are displayed, and the diversity gain is lost.