The emergence of new intelligent applications has fostered the development of a task-oriented communication paradigm, where a comprehensive, universal, and practical metric is crucial for unleashing the potential of this paradigm. To this end, we introduce an innovative metric, the Task-oriented Age of Information (TAoI), to measure whether the content of information is relevant to the system task, thereby assisting the system in efficiently completing designated tasks. We apply TAoI to a wireless monitoring system tasked with identifying targets and transmitting their images for subsequent analysis. To minimize TAoI and determine the optimal transmission policy, we formulate the dynamic transmission problem as a Semi-Markov Decision Process (SMDP) and transform it into an equivalent Markov Decision Process (MDP). Our analysis demonstrates that the optimal policy is threshold-based with respect to TAoI. Building on this, we propose a low-complexity relative value iteration algorithm tailored to this threshold structure to derive the optimal transmission policy. Additionally, we introduce a simpler single-threshold policy, which, despite a slight performance degradation, offers faster convergence. Comprehensive experiments and simulations validate the superior performance of our optimal transmission policy compared to two established baseline approaches.

This paper focuses on spectrum sharing in heterogeneous wireless networks, where nodes with different Media Access Control (MAC) protocols to transmit data packets to a common access point over a shared wireless channel. While previous studies have proposed Deep Reinforcement Learning (DRL)-based multiple access protocols tailored to specific scenarios, these approaches are limited by their inability to generalize across diverse environments, often requiring time-consuming retraining. To address this issue, we introduce Generalizable Multiple Access (GMA), a novel Meta-Reinforcement Learning (meta-RL)-based MAC protocol designed for rapid adaptation across heterogeneous network environments. GMA leverages a context-based meta-RL approach with Mixture of Experts (MoE) to improve representation learning, enhancing latent information extraction. By learning a meta-policy during training, GMA enables fast adaptation to different and previously unknown environments, without prior knowledge of the specific MAC protocols in use. Simulation results demonstrate that, although the GMA protocol experiences a slight performance drop compared to baseline methods in training environments, it achieves faster convergence and higher performance in new, unseen environments.

Recent popularization of the Internet of Vehicles (IoVs) and vehicles-to-everything (V2X) enables the emergence of real-time vehicular applications, posing challenges to resourcelimited vehicles. Toward this end, vehicle edge computing (VEC) has been proposed to alleviate the computational burden on vehicles by leveraging resources from roadside units (RSUs) and VEC servers. While existing works mainly focus on the task requirement for either vehicles or RSUs, the joint task offloading for both V2X and RSUs-to-everything (R2X) has not been fully studied. In this paper, we aim at optimizing the task offloading strategies for both vehicles and RSUs, and adopt a multi-hop task offloading manner to fully utilize the VEC network resources. This problem introduces a severe state-action space shift issue with varying dimensions and representation, which poses challenges for conventional DRL approaches. To address it, we propose a Bidirectional Encoder Representations from Transformers (Bert)-based matching Q-network (BMQN) algorithm. First, we design the BMQN model to efficiently capture correlations among all vehicles and RSUs through bidirectional attention. Then, we propose type-embedded grouped attention and available action embedding to mitigate the overfitting sequence length issue, thereby enhancing generalization capacity. Moreover, we propose to address the state-action space shift issue through a matching-based manner, which can significantly enhance the task offloading ability by matching the states among devices. Simulation results demonstrate that: 1) the BMQN can achieve much better performance than other approaches in scenarios comprising various numbers of vehicles and RSUs as well as diverse road lengths; 2) the BMQN has sufficient generalization capacity to adapt to inexperienced scenarios through matching-based architecture and available action embedding.

The rapid scalability of large language models (LLMs) has driven significant advancements across various natural language processing tasks. However, the immense size of LLMs and the growing demand for large-scale datasets present challenges in fine-tuning these models in resource-constrained environments. Federated learning (FL) has emerged as a promising solution, enabling collaborative model fine-tuning on distributed private data without requiring data sharing. Despite its potential, the heavy computational and communication burdens imposed by LLMs hinder the widespread adoption of FL-based fine-tuning. To mitigate these challenges, we propose FedSIT (Federated Split Importance-Based Tuning), a novel federated fine-tuning framework designed to optimize LLM training in environments with limited computational resources. FedSIT splits the pre-trained model into Bottom, Trunk, and Top layers, offloading the computationally intensive Trunk layer to the server while distributing the Bottom and Top layers to client devices. Additionally, FedSIT leverages layer importance scores to selectively fine-tune the most critical layers, reducing the number of parameters to be fine-tuned. Our extensive experiments demonstrate that FedSIT achieves comparable performance to existing methods while significantly reducing resource requirements, offering an efficient and scalable solution for federated fine-tuning of LLMs in real-world settings.

Traditional bus systems with fixed routes and timetables struggle to accommodate dynamic and diverse passenger demands. Demand-Responsive Transit (DRT) offers a flexible solution through dynamic route planning. However, multi-route cooperative scheduling faces challenges such as high combinatorial optimization complexity and insufficient real-time responsiveness. We propose a Multi-Agent Deep Reinforcement Learning framework for cooperative optimization in dynamic multi-route DRT scheduling (MARL-DRT). The problem is modeled as a multi-agent Markov Decision Process (MDP) aimed at minimizing a weighted total cost, including operating costs, passenger waiting costs, trip cancellations, and real-time demand profit. We employ the Multi-Actor-Attention-Critic (MAAC) algorithm to solve the problem, where each agent dynamically generates station sequences through a policy network based on an encoder-decoder structure. A centralized critic network and the policy gradient method are used to improve global cooperation and scheduling stability. Extensive experiments on real-world and benchmark networks demonstrate that our algorithm outperforms baseline methods in total cost, responsiveness, and service quality, providing a more efficient DRT system with lower operational costs and higher passenger satisfaction.

The rapid proliferation of Internet of Things (IoT) is generating an unprecedented volume of distributed data, necessitating efficient decentralized learning paradigms. Federated learning (FL) has emerged as a compelling distributed collaborative intelligence framework, renowned for its privacy protection benefits. However, the communication overhead associated with intermediate model exchanges remains a critical bottleneck in FL. Aiming at reducing the communication cost of FL equipped with promising over-the-air computation (AirComp) technique, this work designs specialized model compression schemes for both uplink and downlink communications. For uplink transmission with AirComp, we analyze its unique constraints and propose a hybrid global sparsification scheme that combines the benefits of conventional Top-k and Rand-k algorithms. We further develop an algorithm to strategically allocate transmission budgets between the two concatenated sparsification operations, accounting for both model temporal correlation and the cost of index synchronization. For downlink transmission, we introduce a group-based mixed-precision quantization (MPQ) scheme and integrates the broadcast of grouping information with uplink sparsification pattern to further mitigate communication burden. Moreover, we conduct theoretical analysis under realistic channel conditions and typical FL settings to validate the advantages and establish convergence guarantees of our approaches. Experimental results demonstrate that, compared to existing schemes, the proposed methods significantly improve communication efficiency and ensure client scalability, and concurrently verify the benefits of the uplink-downlink synergistic design.

Industrial Cyber-Physical Systems (ICPS) have emerged as a critical component in the industrial domain. To facilitate seamless collaboration among massive devices, cloud-edge computing architectures have emerged as a key enabler for ICPS, leveraging distributed intelligence to orchestrate devices and computational tasks. In cloud-edge computing, efficient task offloading and resource management are essential for optimizing task performance and reducing energy costs. However, conventional centralized resource management strategies struggle to satisfy the real-time, adaptability, and performance demands of dynamic ICPS systems. Industrial Cyber-Physical Systems (ICPS) have emerged as a critical component in the industrial domain. To facilitate seamless collaboration among massive devices, cloud-edge computing architectures have emerged as a key enabler for ICPS, leveraging distributed intelligence to orchestrate devices and computational tasks. In cloud-edge computing, efficient task offloading and resource management are essential for optimizing task performance and reducing energy costs. However, conventional centralized resource management strategies struggle to satisfy the real-time, adaptability, and performance demands of dynamic ICPS systems. In this paper, we propose the Distributed Transformer-based Actor-Critic (DTAC) algorithm to jointly determine task offloading and resource management decisions in cloud-edge computing networks, particularly for delay-sensitive applications in ICPS. The DTAC algorithm integrates the powerful transformer model with the popular actor-critic architecture to address the challenge of a hybrid high-dimensional action space. We first train a centralized model to learn coordination among user equipments (UEs) and then introduce a decentralized transfer learning (TL) approach to efficiently adapt the centralized model into the DTAC framework. Using the DTAC model, each UE can independently manage its local resources based solely on local information, avoiding the significant signaling overhead inherent in centralized approaches. Simulation results demonstrate that DTAC not only outperforms other MARL and TL schemes in both small-and large-scale scenarios, but also exhibits strong generalization capabilities in inexperienced settings. Furthermore, DTAC and decentralized TL approaches significantly reduce training costs by 73% compared to other methods, making them more practical for ICPS deployment.

Joint sensing and communication (JSC) systems aim to leverage the same spectral resources for both communication and sensing tasks within a single system. These systems have the potential to enhance sensing capabilities through advanced communication techniques, while also utilizing precise localization and tracking information from sensing technologies to improve communication. However, the integration of information obtained from sensing and transmitted in communication is not yet fully understood. This paper investigates the challenge of guaranteeing timely delivery of sensing information within JSC systems. We introduce a novel metric, termed as the age of estimation information (AoEI), which integrates radar mutual information (MI) and age of information (AoI). This unified metric effectively captures both the passage of time and the accuracy of estimation information, making it well-suited for the JSC system. Further, we delve into the joint optimization of time and power allocation for a single JSC node with both sensing and communication capabilities. Our objective is to minimize the long-term average AoEI while adhering to a long-term average power constraint. To tackle this problem, we formulate it as an average-reward constrained Markov decision process (CMDP) and propose a model-free constrained deep reinforcement learning (CDRL) algorithm, namely the average policy optimization (APO)-Lagrangian based algorithm. Simulation results demonstrate that our proposed algorithm effectively meets the constraint in dynamic and uncertain environments while achieving a favorable balance between AoEI and power consumption. Additionally, our algorithm outperforms four baseline schemes, showcasing its superior performance.

How to effectively support massive access and data transmission in Internet of Things scenarios has been a long-standing and critical issue for various wireless communication networks. To address this issue, a flexible and efficient medium access control protocol is the key. In this paper, we propose Slotted Aloha with Successive Transmission (SAST) scheme, in which upon the successful transmission of the Head-of-Line (HoL) packet, the node delivers the remaining packets with probability 1 until the buffer is cleared or a collision occurs, thereby capitalizing on immediate channel availability. By formulating vacation queuing models of both node and channel, the access/data throughput and access/data delay are explicitly characterized and optimized by properly choosing the transmission probability of the HoL packet. Our analysis reveals that the maximum data throughput of SAST scheme is 0.5, higher than$e^{-1}$in classic slotted Aloha. The practical insights of the analysis are also demonstrated by taking the example of 2-step Small Data Transmission (SDT) random access in 5G. It is shown that the SAST scheme can be seamlessly implemented into 5G and the comparison with 2-step SDT random access reveals that SAST can improve the throughput performance while significantly reduce the signaling overhead, nearly halved in the saturated case and up to 70% reduction in the unsaturated case.

Next-generation multiple access techniques are crucial for providing low-latency and highly efficient data transmission services. Recently, Deep Reinforcement Learning (DRL) has emerged as a prevalent approach in the multiple access domain, aiming to facilitate user coordination and enhance transmission efficiency. However, current DRL approaches face challenges, including limited generalization ability, low sample efficiency, and the complexities associated with Partially Observable Markov Decision Processes (POMDP), which hinder their application in heterogeneous networks with varying numbers of nodes and configurations. In this paper, we propose a foundation model-based multiple access (FMA) algorithm. To address severe POMDP and sample inefficiency issues, we decompose the multiple access problem into two parts: a transmission decision part and a configuration estimation part. We leverage the strong generalization and inference capabilities of the foundation model, utilizing a Deep Learning (DL) approach instead of DRL for training, and adopt the Low-Rank Adaptation (LoRA) technique to fine-tune the foundation model for downstream multiple access tasks. Simulation results demonstrate that: 1) through the decomposition, the FMA approach exhibits sufficient generalization and inference abilities to adapt to various scenarios with various protocols, configurations, and numbers of heterogeneous nodes; 2) by incorporating expert knowledge, the FMA approach can significantly enhance network performance while ensuring certain fairness requirement for heterogeneous nodes.

Multiview clustering (MVC) has been widely studied in machine learning and data mining for its capability of improving clustering performance by fusing the information from multiview data. In the past decade, a large number of MVC methods have made impressive progress, but most of them suffer from computational burdens, especially in large-scale tasks. Binary MVC (BMVC) is proposed to address this issue by representing the large-scale high-dimensional dataset as a group of consensus and low-dimensional binary codes. However, current BMVC-based approaches generate the clustering by executing binary k-means on the obtained binary codes, which fail to capture the embedded geometric information, leading to poor clustering performance. In addition, parameter selection is another "mission impossible" in unsupervised learning tasks including MVC. To tackle these challenges, a framework of multiview clustering via partitioning the signed prototype graph (SPGMVC) is proposed in this work. The SPGMVC framework offers several contributions. First, SPGMVC is designed as a unified framework for MVC. It combines effective technologies, such as consensus binary coding, code compression (CC), signed prototype graph (SPG) partitioning, and prototype-based cluster assignment. Second, SPGMVC partitions the signed graph (SG) based on the relationships between positive and negative edges. By capturing the underlying structure of the data, this partitioning strategy improves clustering accuracy (ACC). CC techniques are applied to reduce the graph's scale, enabling further partitioning and enhancing computational efficiency. Third, SPGMVC employs an alternate minimizing strategy to efficiently handle the optimization problem. This strategy has nearly linear time and space complexity with respect to the data volume, making it suitable for large-scale tasks. Fourth, SPGMVC proposes an automatic parameter selection strategy, eliminating the need for extensive parameter exploration. Comprehensive experiments illustrate the superiority of our model. The implementation of SPGMVC is available at: https://github.com/gepingyang/PSGMVC.

In this paper, an unsupervised deep learning-based framework based on dual-path model-driven auto-encoders (AE) is proposed for angle-of-arrivals (AoAs) estimation in massive MIMO systems. Specifically designed for AoA estimation, the proposed framework differs from the conventional AE in two aspects. Firstly, unlike conventional auto-encoders, our framework employs a dual-path neural network for the encoder, decoupling the estimated parameters and enabling independent updates of each paths. Secondly, the decoder has fixed weights that implement the signal propagation model, instead of learnable parameters. This knowledge-aware decoder ensures the output of meaningful physical parameters (i.e., AoAs) which is unattainable by conventional AEs. We also conduct a thorough analysis to characterize the multiple global optima and local optima of the estimation problem. This analysis inspires the design of a low-complexity two-phase training scheme and confirms the convergence of our proposed framework. Consequently, our framework addresses two key challenges in unsupervised learning: the lack of interpretability and the convergence to local optima. Extensive simulations validate our theoretical analysis and demonstrate the performance improvements of our proposed framework.

The emergence of intelligent applications has fostered the development of a task-oriented communication paradigm, where a comprehensive, universal, and practical metric is crucial for unleashing the potential of this paradigm. To this end, we introduce an innovative metric, the Task-oriented Age of Information (TAoI), to measure whether the content of information is relevant to the system task, thereby assisting the system in efficiently completing designated tasks. Also, we study the TAoI in a remote monitoring system, whose task is to identify target images and transmit them for subsequent analysis. We formulate the dynamic transmission problem as a Semi-Markov Decision Process (SMDP) and transform it into an equivalent Markov Decision Process (MDP) to minimize TAoI and find the optimal transmission policy. Furthermore, we demonstrate that the optimal strategy is a threshold-based policy regarding TAoI and propose a relative value iteration algorithm based on the threshold structure to obtain the optimal transmission policy. Finally, simulation results show the superior performance of the optimal transmission policy compared to the baseline policies.

In this paper, we propose a highly accurate unsu-pervised deep learning framework based on auto-encoder (AE) for angle-of-arrival (AoA) estimation in massive MIMO systems. Our method builds on an improvement of the vanilla AE by incorporating the knowledge of signal propagation models into the decoder. In our proposed knowledge-guided AE (KG-AE), instead of having learnable parameters, the decoder has fixed weights that implement the signal propagation model. Such modification forces the encoder to output meaningful physical parameters of interest (i.e., AoA), which cannot be achieved by standard AE. Furthermore, we rigorously analyze the multiplicity of local optima in unsupervised channel estimation problems. Our analysis informs the design of the cost function and the training scheme for the proposed KG-AE. Specifically, we design a two-stage training scheme, different loss functions in the two stages to achieve good initial points and boost the performance of the proposed KG-AE, respectively. Finally, extensive simulations are performed, and the results corroborate the analysis and demonstrate the performance improvements of the proposed KG-AE over the subspace-based algorithms and the state-of-the-art unsupervised learning-based algorithm.

Mobile-edge Computing (MEC) has now emerged as a complement to cloud computing, providing computational capacity for the resources-constrained edge devices. Recently, intelligent computation offloading and cache placement stands as effective approaches to enhance the performance of dynamic MEC networks. In this paper, we propose an online centralized joint resource management approach, named Transformer-based Actor-Critic (TAC), to minimize the task execution time subject to resource constraints. We decouple this mixed-integer non-linear programming (MINLP) problem into a non-convex offloading decision part and a convex joint resources allocation part, and propose the TAC approach to address the non-convex task offloading problem with low computational complexity. In the joint resources management problem, the high-dimensional state-action space is addressed by the transformer-based actor-critic architecture. Through the proposed TAC, the joint cache, communication and computation resource management can be obtained without the knowledge of future task arrivals. Simulation results demonstrate that the TAC can save 48.4% average task execution time with only 2.3% additional computation delay compared to Random with lowest computational complexity. In particular, it further demonstrates great generalization ability to enhance the performance in untrained scenarios.

Federated learning (FL) is a privacy-preserving distributed learning framework where model training is performed locally on distributed devices. Unlike traditional FL, which assumes a fixed local dataset, this paper focuses on the more realistic scenario of FL with streaming data. In Streaming Federated Learning (SFL), new data continuously arrives over time, and due to the limited local storage capacity on devices, some data is inevitably discarded. The discarded data may be forgotten by the model, leading to a decline in model accuracy. To this end, we introduce Federated Data Slimming (FedDS), a data selection scheme designed to determine which data should be stored locally. Particularly, FedDS considers both gradient norms and directions when making data selections. We evaluate the performance of FedDS against several previously proposed schemes using various datasets. Our experimental results demonstrate that FedDS surpasses all baseline schemes, achieving the fastest convergence rate and the highest test accuracy.

Age of Information (AoI), which measures the time elapsed since the generation of the last received packet at the destination, is a new metric for real-time Internet of Things (IoT) applications. In many applications, status information needs to be extracted through computation, which can be processed at an edge server enabled by mobile-edge computing (MEC). In this article, we consider a status update system with MEC in an offline scenario, where transmission and computation need to be jointly scheduled to minimize AoI. Usually, long queuing delay and large packet generation interval will increase age in the queuing system. Therefore, a reasonable scheduling policy is the no-wait policy, which achieves zero queuing delay and a low generation interval. However, the no-wait policy is not always optimal. We propose an interval-wait policy that allows nonzero queuing delay and study the average age minimization problem in this policy. Theoretical results show that the optimal interval-wait policy has a special structure: the queuing delay is either zero or a fixed value that is determined by the transmission and computation time duration of the packet itself and its adjacency. Based on this, we propose an efficient enumerating-based algorithm to compute the optimal interval-wait policy. Our experimental results show that: 1) the interval-wait policy achieves optimal performance in most cases and 2) our proposed efficient algorithm can find the optimal interval-wait policy.

With the proliferation of communication demand, goal-oriented communication goes beyond traditional bit-level approaches by emphasizing the significance of information and its relevance to specific goals. This paper addresses the goal-oriented status updating problem, where detecting status changes is crucial. We employ the Age of Changed Information (AoCI) as a metric, which considers both the timeliness and content of the update. Our goal is to minimize the weighted sum of AoCI and transmission cost without channel delay statistics. The investigated problem is formulated as a semi-Markov decision process (SMDP) and is tackled by converting it into a multi-variable optimization problem. We prove that the optimal updating policy is of threshold type, and derive a nearly closed-form expression for the optimal threshold. When delay statistics are available, the optimal threshold can be obtained by a bisection searching algorithm. In the absence of prior delay statistics, we develop an online learning policy. We demonstrate that the optimality gap decays at a rate of$\mathcal {O}(\log K / K)$, where K is the number of samples. Simulation results are presented to compare the performance of various policies under different statistical conditions, showcasing the superiority of our proposed algorithm.

This paper focuses on the timely delivery of sensing information in a joint sensing and communication (JSC) system to meet the requirements of emerging applications. Specifically, we investigate the time allocation of a single JSC node equipped with both sensing and communication functions to minimize the long-term average age of estimation information (AoEI) while satisfying the long-term average power constraint. The proposed metric, AoEI, combines radar mutual information (MI) and age of information (AoI) to capture both the passage of time and the accuracy of estimation information, making it more suitable for the JSC system. To solve this problem, we formulate the time allocation problem as a constrained Markov decision process (CMDP) and propose a model-free constrained deep reinforcement learning (CDRL) based algorithm. The simulation results demonstrate that the proposed algorithm can achieve a good trade-off between AoEI and power consumption and converge to a policy that satisfies the constraint in a highly dynamic and uncertain environment.

Federated learning (FL) is a promising solution to harness the advances of machine learning under the premise of privacy security, whereas the communication overhead of model exchange remains an obstacle to deploying FL in wireless networks. To tackle this challenge, we consider the non-uniform quantization of the global model in this work. By formulating the optimization of quantization intervals as a Markov decision process (MDP), we propose a deep reinforcement learning (DRL)- based approach to improve the performance of the quantizer for FL. Through crafting a compound reward function, the DRL agent is guided to reduce the quantization error and training loss simultaneously. Furthermore, a dual time-scale mechanism between FL and DRL is adopted to ensure that the actor and critic models of DRL converge more steadily. Simulations on various real-world datasets reveal that the proposed method can provide higher accuracy and faster convergence than the existing uniform quantizers, and can retain these benefits when applying the learned policy to a similar learning task.

This paper focuses on spectrum sharing in heterogenous wireless networks, where different nodes utilize various Media Access Control (MAC) protocols to transmit data packets to a common access point on a shared wireless channel. Previous studies have developed Deep Reinforcement Learning (DRL) based multiple access protocols for specific scenarios within heterogeneous wireless networks. However, there exists a wide range of coexisting scenarios, characterized by varying numbers of nodes and the use of different MAC protocols. Existing approaches require training new models from scratch when encountering unseen scenarios, resulting in significant training time. To address this issue, we propose a novel MAC protocol called Generalized Multiple Access (GMA), which employs the Meta-Reinforcement Learning (meta-RL) algorithm. By learning a meta-policy during training, GMA enable the fast adaptation of the agent node to different and previously unknown heterogeneous network environments, without prior knowledge of the specific MAC protocols used in those environments. We conduct a performance comparison between the proposed GMA protocol and existing DRL-based protocols. Simulation results demonstrate that while the GMA protocol experiences a slight performance loss compared to baseline methods in training environments, it demonstrates faster convergence and higher performance in new environments compared to baseline methods.

With the requirement of timeliness increasing, data processing policy should be carefully designed to tackle arrivals. This paper mainly studies the Age of Information (AoI) in data processing system, where packets are generated by a source and processed by a server with known packets' length upon arrival. We aim to minimize the average AoI by deciding either to preempt the current packet or not when a new packet arrives. For the given distributions of inter-arrival time and packets' length, the problem is formulated by Markov Decision Process (MDP) and solved via value iteration. Without prior knowledge of the distributions, we apply Reinforcement Learning (RL) algorithms to learn the policy online. Through simulation experiments, it is revealed that the obtained optimal strategy by MDP greatly reduces the average AoI compared with baseline policies. Further, the RL algorithms have a good performance in solving this problem. The average AoI of RL policies are just slightly higher than those of MDP.

Delivering as many data packets as possible and making the life time of the network as long as possible is one fundamental request for battery-driven wireless network design, where sleeping schemes are usually adopted for prolonging the life time, while, at the sacrifice of the throughput performance. For random access networks, fulfilling this fundamental request is rather challenging due to the distributed nature of the access behavior of nodes. This paper considers massive Machine-Type Communication (mMTC) networks where each node adapts the representative random access scheme Aloha and periodical sleeping-awake cycle. We aim to address how to maximize the life-time throughput of each node, i.e., average number of packets each node can successfully deliver during its life time, with a guarantee of targeted life time via optimal selection of the channel access probability and the sleeping ratio of each node. By deriving the explicit expressions of the life time and the life-time throughput of each node and jointly tuning both the channel access probability and the sleeping ratio, we characterize the maximum life-time throughput with targeted life time, and the corresponding optimal settings. The analysis reveals that if only the channel access probability is optimally tuned, then the throughput and life-time throughput cannot be optimized simultaneously when the network becomes saturated with a large packet arrival rate. In contrast, the network would operate at unsaturated conditions via the joint tuning of the access probability and the sleeping ratio. In this case, the maximum life-time throughput always grows with the packet arrival rate. In addition, it is shown that the effect of the life-time constraint becomes significant only when it exceeds a threshold, where maximum life-time throughput will sacrifice for life-time expectation. The analysis sheds important light on the access and sleeping scheme design of practical Aloha-type networks. By taking Narrow Band-IoT with Power Saving Mode (PSM) as an example, extensive simulation results corroborate that with the proposed optimal setting, the life-time throughput could be significantly improved, especially when the life time requirement is demanding, e.g., 10 years without battery replacement.

With the increasing diversity of mobile apps, users install many apps in their smartphones and often use several apps together to meet a specific requirement. Because of the evolution of user habits and app functions, the set of apps using at the same time, i.e., app usage context, may change over time, which represents the dynamic correlation of different apps and even the evolution trend of the whole app ecosystem. Therefore, understanding how an apps usage context changes over time is very meaningful. In this paper, based on a seven-year app usage dataset, we explore the long-term app usage context dynamics and understand the underlying reasons and influence factors behind. Specifically, we build app co-occurrence graphs in different periods and learn app embeddings accordingly by leveraging graph embedding algorithm. We then measure the change of app usage context by the distance between neighboring app embeddings. As for the whole app ecosystem, we find that the change rate of app usage context undergoes up and down phrases, and varies in different app-categories. Furthermore, we explore three influence factors correlated with such dynamics. These results will be helpful for stakeholders to better understand the evolution of mobile users app usage behavior.

Spectral Clustering (SC) is an effective clustering method for its excellent performance in partitioning non-linearly distributed data. On the other hand, Ensemble Clustering (EC), a different clustering technology, can promote cluster quality by ensembling the results of base clusterings. In this work, we concentrate on an EC framework that utilizes SC as the base method. Nevertheless, SC suffers from scalability due to its high computational complexity in constructing the Laplacian graph and computing the corresponding eigendecomposition. In the past decades, many efforts have been made to it. However, SC suffers from the scalability issue in processing extensive data, especially in web-scale scenarios. Additionally, EC requires multiple clustering results as the ensemble bases, which further aggravates resource consumption. To address this issue, LiteWSEC, a simple yet efficient Lightweight Framework for Web-scale Spectral Ensemble Clustering, is proposed to cluster web-scale data with limited resource requirements. It adopts the Web-scale Spectral Clustering (WSC) as the base method, which has minimal space overhead without computing overall embedding explicitly. LiteWSEC is highly flexible in the memory requirement, which is adaptive to the available resource. It can partition web-scale data (e.g.,$n $= 8,000 k) in an resource-limited host (e.g., memory is restricted to 1 GB). Experiments on real-world, large-scale, and web-scale datasets demonstrate both the efficiency and effectiveness of LiteWSEC over state-of-the-art SC and EC methods.

This study is motivated by the maximum connected coverage problem (MCCP), which is to deploy a connected UAV network with given$K$UAVs in the top of a disaster area such that the number of users served by the UAVs is maximized. The deployed UAV network must be connected, since the received data by a UAV from its served users need to be sent to the Internet through relays of other UAVs. Motivated by this application, in this paper we study a more generalized problem – the$h$-hop independently submodular maximization problem, where the MCCP problem is one of its special cases with$h=4$. We propose a$\frac {1-1/e}{2h+3}$-approximation algorithm for the$h$-hop independently submodular maximization problem, where$e$is the base of the natural logarithm. Then, one direct result is a$\frac {1-1/e}{11}$-approximate solution to the MCCP problem with$h=4$, which significantly improves its currently best$\frac {1-1/e}{32}$-approximate solution. We finally evaluate the performance of the proposed algorithm for the MCCP problem in the application of deploying UAV networks, and experimental results show that the number of users served by deployed UAVs delivered by the proposed algorithm is up to 12.5% larger than those by existing algorithms.

This study is motivated by the maximum connected coverage problem (MCCP), which is to deploy a connected UAV network with given K UAVs in the top of a disaster area such that the number of users served by the UAVs is maximized. The deployed UAV network must be connected, since the received data by a UAV from its served users need to be sent to the Internet through relays of other UAVs. Motivated by this application, in this paper we study a more generalized problem – the h-hop independently submodular maximization problem, where the MCCP problem is one of its special cases with h = 4. We propose a $\frac{{1 - 1/e}}{{2h + 3}}$-approximation algorithm for the h-hop independently submodular maximization problem, where e is the base of the natural logarithm. Then, one direct result is a $\frac{{1 - 1/e}}{{11}}$-approximate solution to the MCCP problem with h = 4, which significantly improves its currently best $\frac{{1 - 1/e}}{{32}}$-approximate solution. We finally evaluate the performance of the proposed algorithm for the MCCP problem in the application of deploying UAV networks, and experimental results show that the number of users served by deployed UAVs delivered by the proposed algorithm is up to 12.5% larger than those by existing algorithms.

For multi-user satellite Internet of Things (IoT) systems operating at lower signal-to-noise ratio, spread spectrum techniques are usually used to combat narrowband interference. In addition, the communication performance in the spread spectrum system depends on the anti-jamming ability of the spreading codes (SCs). Therefore, how to design the SCs scheduling strategies under users' requirements and resource constraints has become a crucial problem for satellite IoT systems. In this paper, communication rewards and scheduling delays are introduced as gauges to measure the scheduling performance of the satellite gateway station control center (SGSCC). Specifically, SGSCC must efficiently and effectively allocate limited available SCs over terminal gateways under request at each transmission time slot. The SCs scheduling problem is formulated as a Markov Decision Process (MDP) along with the observed environments composed of resource status and user request status. Then a deep reinforcement learning scheduling algorithm is devised by embedding the idea of Long Short-Term Memory (LSTM) in the standard Double Deep Q-learning (DDQN). Simulation results show that the proposed algorithm can achieve much better performance than traditional algorithms in terms of communication rewards and scheduling delays. Finally, we draw some conclusions.

In this paper, we consider an information update system where a wireless sensor sends timely updates to the destination over an erasure channel with the supply of harvested energy and reliable backup energy. The metric Age of Information(AoI) is adopted to measure the timeliness of the received updates at the destination. We aim to find the optimal information updating policy that minimizes the time-average weighted sum of the AoI and the reliable backup energy cost by formulating an infinite state Markov decision process(MDP). The optimal information updating policy is proved to have a threshold structure. Based on this special structure, an algorithm for efficiently computing the optimal policy is proposed. Numerical results show that the optimal updating policy proposed outperforms baseline policies.

In large-scale wireless networks, severe interference may incur that leads to the age of information (AoI) degradation. It is therefore important to study how to optimize the AoI performance. This paper focuses on the average AoI minimization in random access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the average AoI is derived, based on which the optimal average AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. We further compare the average AoI optimization with the peak AoI optimization. The comparison reveals that the optimal channel access probability for the average AoI optimization and the peak AoI optimization are the same. Yet, the optimal packet arrival rate for the average AoI optimization is smaller than that for the peak AoI optimization. The gap enlarges when the node deployment density becomes small.

Abstract Communication has been recognized as one of the primary challenges of federated learning (FL), but the actual communication algorithm or protocol design is still rarely involved in the existing studies. In the paper, viewing the model exchange in FL as a special kind of traffic, an unequal error protection (UEP) scheme is designed based on multi‐rate channel coding and multi‐layer modulation for it. To answer the question of how to make error control for FL when the wireless channel is no longer simplified as a pipeline, this paper firstly theoretically analyzes the impact of transmission error on machine leanring (ML) model, which reveals that the dynamic range of the weights should be taken into consideration. Guided by the analysis, the UEP scheme is applied to FL in multiple perspectives including parameter, network and time. Furthermore, a UEP‐based adaptive coding method is developed for the case with dynamic signal‐to‐noise ratio (SNR) to ensure faster and more stable convergence of the FL model while saving as much bandwidth as possible. Comprehensive numerical simulation on several real‐world datasets verifies that the proposed UEP transmission schemes can indeed bring significant benefits in accuracy, robustness and efficiency, especially when the channel condition is poor.

Age of Information (AoI), a notion that measures the information freshness, is an essential performance measure for time-critical applications in Internet of Things (IoT). With the surge of computing resources at the IoT devices, it is possible to preprocess the information packets that contain the status update before sending them to the destination so as to alleviate the transmission burden. However, the additional time and energy expenditure induced by computing also make the optimal updating a nontrivial problem. In this article, we consider a time-critical IoT system, where the IoT device is capable of preprocessing the status update before the transmission. Particularly, we aim to jointly design the preprocessing and transmission so that the weighted sum of the average AoI of the destination and the energy consumption of the IoT device is minimized. Due to the heterogeneity in transmission and computation capacities, the durations of distinct actions of the IoT device are nonuniform. Therefore, we formulate the status updating problem as an infinite horizon average cost semi-Markov decision process (SMDP) and then transform it into a discrete-time Markov decision process. We demonstrate that the optimal policy is of threshold type with respect to the AoI. Equipped with this, a structure-aware relative policy iteration algorithm is proposed to obtain the optimal policy of the SMDP. Our analysis shows that preprocessing is more beneficial in regimes of high AoIs, given it can reduce the time required for updates. We further prove the switching structure of the optimal policy in a special scenario, where the status updates are transmitted over a reliable channel and derive the optimal threshold. Finally, simulation results demonstrate the efficacy of preprocessing and show that the proposed policy outperforms two baseline policies.

In theera of Internet of Things (IoT), sensor plays a more vital role and its quality has a great impact on final measurement. From the viewpoint of final measurement, there are two kinds of effects: bias-only measurement and error-growth measurement. The bias-only measurement can be directly read from a sensing device, and each measurement is only distorted by the inherited bias error of the sensor, e.g., measuring temperature by a thermometer sensor. On the other hand, the error-growth measurement cannot be read directly but be calculated by combining multiple former sampling data, therefore multiple bias errors are accumulated in the measurement. For instance, in inertial navigation, the raw data sampled from an inertial measurement unit (IMU) are converted into a trajectory by multiple integral operations, so the errors of new sampling data are continuously added into the trajectory, and the deviation of the traced trajectory will reach an unacceptable level over time. Clearly, a good IMU trajectory strategy is the one with a less error-accumulation effect. Unfortunately, the analysis of error-growth effect remains not well studied, which motivates this work. This letter first proposes a theoretical error-growth effect analysis framework. Next, we use it to analyze three typical inertial methods, namely single-IMU method, gyro-free-IMU (GF-IMU) method, and$\omega$-free accelerometer pair (OFAP) method. Finally, the theoretical derivations of the three inertial methods are proved by simulation results.

In Internet of Things (IoT), the freshness of status updates is crucial for mission-critical applications. In this regard, it is suggested to quantify the freshness of updates by using Age of Information (AoI) from the receiver’s perspective. Specifically, the AoI measures the freshness over time. However, the freshness in the content is neglected. In this paper, we introduce an age-based utility, named asAge of Changed Information(AoCI), which captures both the passage of time and the change of information content. By modeling the underlying physical process as a discrete time Markov chain, we investigate the AoCI in a time-slotted status update system, where a sensor samples the physical process and transmits the update packets to the destination. With the aim of minimizing the weighted sum of the AoCI and the update cost, we formulate an infinite horizon average cost Markov Decision Process. We show that the optimal updating policy has a special structure with respect to the AoCI and identify the condition under which the special structure exists. By exploiting the special structure, we provide a low complexity relative policy iteration algorithm that finds the optimal updating policy. We further investigate the optimal policy for two special cases. In the first case where the state of the physical process transits with equiprobability, we show that optimal policy is of threshold type and derive the closed-form of the optimal threshold. We then study a more generalized periodic Markov model of the physical process in the second case. Lastly, simulation results are laid out to exhibit the performance of the optimal updating policy and its superiority over the zero-wait baseline policy.

Evolutionary algorithms can outperform conventional placement algorithms such as simulated annealing, analytical placement, and manual placement on runtime, wirelength, pipelining cost, and clock frequency when mapping hard block intensive designs such as systolic arrays on Xilinx UltraScale+ FPGAs. For certain hard-block intensive designs, the commercial-grade Xilinx Vivado CAD tool cannot provide legal routing solutions without tedious manual placement constraints. Instead, we formulate hard block placement as a multi-objective optimization problem that targets wirelength squared and bounding box size. We build an end-to-end placement-and-routing flow called RapidLayout using the Xilinx RapidWright framework. RapidLayout runs 5–6 \( \times \) faster than Vivado with manual constraints and eliminates the weeks-long effort to manually generate placement constraints. RapidLayout enables transfer learning from similar devices and bootstrapping from much smaller devices. Transfer learning in the UltraScale+ family achieves 11–14 \( \times \) shorter runtime and bootstrapping from a 97% smaller device delivers 2.1–3.2 \( \times \) faster optimizations. RapidLayout outperforms (1) a tuned simulated annealer by 2.7–30.8 \( \times \) in runtime while achieving similar quality of results, (2) VPR by 1.5 \( \times \) in runtime, 1.9–2.4 \( \times \) in wirelength, and 3–4 \( \times \) in bounding box size, while also (3) beating the analytical placer UTPlaceF by 9.3 \( \times \) in runtime, 1.8–2.2 \( \times \) in wirelength, and 2–2.7 \( \times \) in bounding box size.

Age-of-Information (AoI), or simply age, which measures the data freshness, is essential for real-time Internet-of-Things (IoT) applications. On the other hand, energy saving is urgently required by many energy-constrained IoT devices. This paper studies the energy-age tradeoff for status update from a sensor to a monitor over an error-prone channel. The sensor can sleep, sense and transmit a new update, or retransmit by considering both sensing energy and transmit energy. An infinite-horizon average cost problem is formulated as a Markov decision process (MDP) with the objective of minimizing the weighted sum of average AoI and average energy consumption. By solving the associated discounted cost problem and analyzing the Markov chain under the optimal policy, we prove that there exists a threshold optimal stationary policy with only two thresholds, i.e., one threshold on the AoI at the transmitter (AoIT) and the other on the AoI at the receiver (AoIR). Moreover, the two thresholds can be efficiently found by a line search. Numerical results show the performance of the optimal policies and the tradeoff curves with different parameters. Comparisons with the conventional policies show that considering sensing energy is of significant impact on the policy design, and introducing sleep mode greatly expands the tradeoff range.

To reduce the signaling overhead for sporadic small packet transmission in massive Machine Type Communications (mMTC), Packet-Based Random Access (PBRA) scheme is introduced in 5G system, where devices can transmit data packets in the random access procedure without connection establishment. Yet, even with PBRA, the signaling overhead may surge if the system parameters are configured improperly. This paper aims to address this issue by studying how to tune the Access Class Barring (ACB) factor to maximize the throughput while maintaining the signaling-to-throughput ratio below a certain level. Explicit expressions of maximum throughput and the corresponding optimal ACB factor in saturated and unsaturated cases are derived. It reveals that with a demanding requirement on signaling-to-throughput ratio, the throughput performance has to be sacrificed even with optimal tuning of ACB factor. To boost the throughput performance, the system should either loose the signaling constraint or enlarge the packet length. The analysis is verified by simulations and sheds important light on practical 5G network design for supporting mMTC with PBRA.

In this paper, we study the efficient communication design, including both uplink and downlink communications, for wireless federated learning (FL). We answer the question of what and how to communicate between clients and the parameter server and evaluate the impact of the various quantization and transmission options of the updated model on the learning performance. We provide new convergence analysis of the well-known FEDAVG under non-i.i.d. dataset distributions, partial clients participation, and finite-precision quantization in uplink and downlink communications. These analyses reveal that, in order to achieve an $\mathcal{O}(1/T)$ convergence rate with quantization, transmitting the weight requires increasing the quantization level at a logarithmic rate, while transmitting the weight differential can keep a constant quantization level. Comprehensive numerical evaluation on various real-world datasets reveals that the benefit of a FL-tailored uplink and downlink communication design is enormous – a carefully designed 1-bit quantization (3.1% of the floating-point baseline bandwidth) achieves 99.8% of the floating-point baseline accuracy at almost the same convergence rate on MNIST, representing the best known bandwidth-accuracy tradeoff to the best of the authors’ knowledge.

Compared with a single loop, a FLL-assisted-PLL (Frequency-locked Loop, FLL; Phase-locked Loop, PLL) tracking loop which integrates both the dynamic robustness of FLL and the accuracy performance of PLL has better carrier tracking performance. However, under highly dynamic conditions, its tracking ability still cannot meet the requirements. To overcome this problem, a novel combined control loop based on FLL-assisted-PLL is proposed in this paper. The combined control loop adjusts the action effects of FLL and PLL automatically according to the current tracking state without the need for decision processing, which makes full use of the characteristics of FLL and PLL. Simulation results demonstrate that the proposed combined loop does achieve a shorter convergence time and higher tracking accuracy than the traditional FLL-assisted-PLL tracking loop under significant dynamics.

Federated learning (FL) enables devices to update a global model while keeping the training data local, so that data privacy is protected. However, the local data of devices is usually non-independent and identically distributed (non-i.i.d.), which leads to performance degradation. This paper aims to address this issue by a client-selection approach. In particular, in consideration of balancing the label distribution of the selected clients, a new client selection method called grouping based scheduling (GS) scheme is proposed, with which clients are divided into several groups based on a new metric called group earth mover’s distance (GEMD). Experiment results show that the GS can improve the performance of FL algorithms, compared to the random scheduling scheme. An encryption method is further proposed to enhance privacy protection, which facilitates the application of the proposed GS scheme.

Communication has been known to be one of the primary bottlenecks of federated learning (FL), and yet existing studies have not addressed the efficient communication design, particularly in wireless FL where both uplink and downlink communications have to be considered. In this paper, we focus on the design and analysis of physical layer quantization and transmission methods for wireless FL. We answer the question of what and how to communicate between clients and the parameter server and evaluate the impact of the various quantization and transmission options of the updated model on the learning performance. We provide new convergence analysis of the well-known FED AVG under non-i.i.d. dataset distributions, partial clients participation, and finite-precision quantization in uplink and downlink communications. These analyses reveal that, in order to achieve anO(1/T) convergence rate with quantization, transmitting the weight requires increasing the quantization level at a logarithmic rate, while transmitting the weight differential can keep a constant quantization level. Comprehensive numerical evaluation on various real-world datasets reveals that the benefit of a FL-tailored uplink and downlink communication design is enormous - a carefully designed quantization and transmission achieves more than 98% of the floating-point baseline accuracy with fewer than 10% of the baseline bandwidth, for majority of the experiments on both i.i.d. and non-i.i.d. datasets. In particular, 1-bit quantization (3.1% of the floating-point baseline bandwidth) achieves 99.8% of the floating-point baseline accuracy at almost the same convergence rate on MNIST, representing the best known bandwidth-accuracy tradeoff to the best of the authors' knowledge.

Evolutionary algorithms can outperform conventional placement algorithms such as simulated annealing, analytical placement as well as manual placement on metrics such as runtime, wirelength, pipelining cost, and clock frequency when mapping FPGA hard block intensive designs such as systolic arrays on Xilinx UltraScale+ FPGAs. For certain hard-block intensive, systolic array accelerator designs, the commercial-grade Xilinx Vivado CAD tool is unable to provide a legal routing solution without tedious manual placement constraints. Instead, we formulate an automatic FPGA placement algorithm for these hard blocks as a multi-objective optimization problem that targets wirelength squared and maximum bounding box size metrics. We build an end-to-end placement and routing flow called RapidLayout using the Xilinx RapidWright framework. RapidLayout runs 5-6 times faster than Vivado with manual constraints and eliminates the weeks-long effort to generate placement constraints manually for the hard blocks. We also perform automated post-placement pipelining of the long wires inside each convolution block to target 650 MHz URAM-limited operation. RapidLayout outperforms (1) the simulated annealer in VPR by 33% in runtime, 1.9-2.4 times in wirelength, and 3-4 times in bounding box size, while also (2) beating the analytical placer UTPlaceF by 9.3 times in runtime, 1.8-2.2 times in wirelength, and 2-2.7 times in bounding box size. We employ transfer learning from a base FPGA device to speed-up placement optimization for similar FPGA devices in the UltraScale+ family by 11-14 times than learning the placements from scratch.

Age of Information (AoI), defined as the time elapsed since the generation of the latest received update, is a promising performance metric to measure data freshness for real-time status monitoring. In many applications, status information needs to be extracted through computing, which can be processed at an edge server enabled by mobile edge computing (MEC). In this paper, we aim to minimize the average AoI within a given deadline by jointly scheduling the transmissions and computations of a series of update packets with deterministic transmission and computing times. The main analytical results are summarized as follows. Firstly, the minimum deadline to guarantee the successful transmission and computing of all packets is given. Secondly, a no-wait computing policy which intuitively attains the minimum AoI is introduced, and the feasibility condition of the policy is derived. Finally, a closed-form optimal scheduling policy is obtained on the condition that the deadline exceeds a certain threshold. The behavior of the optimal transmission and computing policy is illustrated by numerical results with different values of the deadline, which validates the analytical results.

The joint iterative decoding assisted (JIDA) algorithm is different from the pilot assisted algorithm. It does not use the pilot signal, but uses the decoding output of the decoder to estimate the carrier frequency offset. Because the decoder can effectively reduce the impact of noise exists in the received signal, the JIDA algorithm has a good performance in satellite IoT transmission system which is in a low signal noise ratio (SNR) environment. However, the JIDA algorithm is difficult to apply in the case of short frame length because of poor estimation accuracy. In this paper, we propose a method that can effectively improve the estimation accuracy of the JIDA algorithm which is an important consideration in narrowband system by using multi frame accumulation while keeping the estimation range. At the same time, we use discrete Fourier transform (DFT) to simplify the estimation expression and reduce the complexity of the algorithm. The simulation results show that the proposed improved JIDA algorithm has higher estimation accuracy than the pilot assisted algorithm at low SNR.

Interleave-Division Multiple Access (IDMA) is a multi-user scheme, in which user-specific interleavers are the only means for user separation. Its receiver involves a chip-by-chip iterative multi-user detector (MUD). In IDMA-based satellite systems, due to the high distance from the satellite to the ground and the large coverage area, the distances between the user ends (UEs) and the satellites are greatly different, which will cause serious asynchronous transmission. In this case, the complexity of MUD is approximately linear with the square of the maximum of users chip delays. Some simplified algorithms were proposed, such as the Simplified Gaussian Chip Detector (sGCD), the MUD with Probabilistic Data Association (PDA) algorithm and the simplified ESE algorithms. But these algorithms are all based on the assumption that the IDMA system is synchronous (without users chip delays). In this paper, two novel reduced complexity MUDs, based on the simplified ESE algorithms and the PDA algorithm, will be proposed for the asynchronous IDMA. We compare the performance of our detectors with the sGCD, the MUD with PDA algorithm and tow simplified ESE algorithms, in terms of Bit Error Rate (BER) and complexity with respect to the number of operations of these detectors for (Additive White Gaussian Noise) AWGN channel. The proposed detectors presents the effective trade-off between performance and complexity. Simulation results show that the proposed detectors have better BER performance than simplified ESE algorithms. Further, results show that one of our detectors outperforms the sGCD when large users chip delays exist in a satellite communication system.

The freshness of status updates is imperative in mission-critical Internet of things (IoT) applications. Recently, Age of Information (AoI) has been proposed to measure the freshness of updates at the receiver. However, AoI only characterizes the freshness over time, but ignores the freshness in the content. In this paper, we introduce a new performance metric, Age of Changed Information (AoCI), which captures both the passage of time and the change of information content. Also, we examine the AoCI in a time-slotted status update system, where a sensor samples the physical process and transmits the update packets with a cost. We formulate a Markov Decision Process (MDP) to find the optimal updating policy that minimizes the weighted sum of the AoCI and the update cost. Particularly, in a special case that the physical process is modeled by a two-state discrete time Markov chain with equal transition probability, we show that the optimal policy is of threshold type with respect to the AoCI and derive the closed-form of the threshold. Finally, simulations are conducted to exhibit the performance of the threshold policy and its superiority over the zero-wait baseline policy.

The explosive growth of traffic-consumption by mobile devices is leading to severe cellular network congestion, which is posing challenges for Internet Service Providers (ISPs) to provide good quality services with limited cellular capacity and impacting the user's experience. Data pricing has been proven to be an effective way to enhance both the service quality and ISP's profit. However, traditional data pricing schemes do not consider the real Mobile Application (App) Usage Patterns (MAUPs) among large scale cellular networks. In this paper, MAUPs aware smart data pricing scheme is proposed. In our work, we firstly extract and model the users' app usage behaviors of approximately 9,600 cellular towers as two-dimensional MAUPs (time, app category). Then 7 distinct derived MAUPs are considered to be incorporated into the user satisfaction model and ISP's profit model. The performance of our proposal is evaluated and verified by numerical experiments from the aspects of ISP's profit, consumption surplus, capacity utilization and traffic efficiency. The MAUPs based pricing scheme can be periodically updated according to the operational conditions and therefore significantly instructive for ISPs.

Fog computing is envisioned as a promising approach for supporting emerging computation-intensive applications on capacity and battery constrained mobile Internet of Things (IoT) devices. Technically speaking, a massive crowd of devices in close proximity can be harvested and collaborate for computation and communication resource sharing. Hence fog computing enables significant potentials in low-latency and energy-efficient mobile task execution. However, without an efficient incentive mechanism to stimulate resource sharing among devices, the benefits of fog computing cannot be fully realized. Leveraging coalitional game theory, this work presents an efficient incentive mechanism to incentivize mutually-beneficial resource cooperation among the devices for collaborative task execution. In particular, to efficiently achieve mutually beneficial task execution, the proposed mechanism groups the devices into multiple micro computing clusters (MCCs). Within each MCC, devices can exchange mutually beneficial actions by helping to compute or transmit tasks, making all of their performances no worse than local execution or execution in the fog server. The solution to the MCC formation is devised by both centralized and decentralized schemes and further proven to admit nice properties such as top coalition, core solution, individual rationality and computational efficiency. Extensive numerical studies demonstrate the superior performance of our MCC formation mechanisms.

In this paper, we investigate the security of a cache-aided multi-relay communication network in the presence of multiple eavesdroppers, where each relay can pre-store a part of the requested files in order to assist secure data transmission from source to destination. If the relays have cached the requested file, then they can directly send it to the destination; otherwise, traditional dual-hop data transmission is used. For both cases, relay selection is performed to assist the secure data transmission. We analyze the network secrecy performance in both scenarios ofnon-colludingandcolludingeavesdroppers, and obtain a closed-form expression for the average secrecy outage probability (SOP), as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER). Through minimizing the network SOP, we further optimize the cache placement by proposing a stochastic sampling based cache learning (SacLe) strategy, which can be implemented in parallel and thus reduces the implementation latency substantially. Numerical and simulation results are finally presented to verify the proposed analysis, and show that the caching strategy has a significant impact on the network secrecy performance through affecting the caching diversity gain and signal cooperation gain at the relays. The proposed SacLe strategy is shown to be able to achieve the optimal performance obtained by the brute force (BF) algorithm.

Age-of-information is a novel performance metric in communication systems to indicate the freshness of the latest received data, which has wide applications in monitoring and control scenarios. Another important performance metric in these applications is energy consumption, since monitors or sensors are usually energy constrained. In this paper, we study the energy-age tradeoff in a status update system where data transmission from a source to a receiver may encounter failure due to channel error. As the status sensing process consumes energy, when a transmission failure happens, the source may either retransmit the existing data to save energy for sensing, or sense and transmit a new update to minimize age-of- information. A threshold-based retransmission policy is considered where each update is allowed to be transmitted no more than M times. Closed- form average age-of-information and energy consumption is derived and expressed as a function of channel failure probability and maximum number of retransmissions M. Numerical simulations validate our analytical results, and illustrate the tradeoff between average age-of-information and energy consumption.

Fog computing is envisioned as a promising approach for supporting emerging mission-critical applications on capacity and battery constrained mobile devices. By harvesting and collaborating a massive crowd of devices in close proximity for computation and communication resource sharing, it enables significant potentials in low-latency and energy-efficient mobile task execution. It is readily acknowledged, however, that without an efficient incentive mechanism that stimulates resources sharing among devices, the benefits of fog computing cannot be fully realized. Leveraging coalitional game theory, this work presents an efficient incentive mechanism to incentivize mutually-beneficial resource cooperation among the devices for collaborative task execution. Specially, to prevent the over-exploiting and free-riding behaviors that harm resource-rich device's willingness to collaborate, the proposed mechanism groups the devices into multiple micro computing clusters (MC-C). Within each MCC, devices can exchange mutually beneficial actions by helping to compute or transmit tasks, making all of them better off. The solution of the MCC formation is devised by a network-assisted mechanism, which is further proven to admit nice properties such as top coalition and core solution.

In this paper, we study the problem of data collection by an unmanned aerial vehicle (UAV) from a set of sensors located on a straight line. The objective is to minimize the UAV's total aviation time while allowing each of the sensors to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping intervals, in each of which one sensor is served by the UAV. The division of the intervals, the UAV speed and the sensors' power allocation policy are sequentially optimized. We show that the optimal power allocation follows the classical water-filling policy, the optimal UAV speed can be obtained by bisection search, and the optimal division of the intervals can be determined by employing the dynamic programming (DP) approach. Numerical results show that for a single sensor case, the optimal transmission interval is symmetric over the location of the sensor. For multiple sensors, the optimal UAV speed is proportional to the given energy and inversely proportional to the data upload requirement.

In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of sensors on a straight line. The UAV can either cruise or hover while communicating with the sensors. The objective is to minimize the UAV's total flight time from a starting point to a destination while allowing each sensor to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping data collection intervals, in each of which one sensor is served by the UAV. The data collection intervals, the UAV's speed, and the sensors' transmit powers are jointly optimized. The formulated flight time minimization problem is difficult to solve. We first show that when only one sensor is present, the sensor's transmit power follows a water-filling policy and the UAV's speed can be found efficiently by bisection search. Then, we show that for the general case with multiple sensors, the flight time minimization problem can be equivalently reformulated as a dynamic programming (DP) problem. The subproblem involved in each stage of the DP reduces to handle the case with only one sensor node. Numerical results present the insightful behaviors of the UAV and the sensors. Specifically, it is observed that the UAV's optimal speed is proportional to the given energy of the sensors and the inter-sensor distance, but it is inversely proportional to the data upload requirement.

In this paper, the transmission optimization of a dual-hop decode-and-forward relaying system is investigated, where the relay capable of energy harvesting from ambient environment can work in hybrid half-duplex (HD) and/or full-duplex (FD) mode. To maximize the throughput from source to destination, the relay's working mode is optimized under the constraint of random energy arrival. In particular, upon the availability of channel state information (CSI), two cases are sequentially studied: one is that CSI is unavailable to the transmitter and the other means CSI is available to the transmitter. In the former case, a dynamic programming (DP) algorithm is proposed to find the optimal working mode of the relay; moreover, to reduce the computational complexity, a linear programming (LP)-based heuristic algorithm is developed, which performs similar to the DP algorithm. In the latter case, the optimal mode of the relay is also obtainable by the DP algorithm and an approximate DP algorithm is further developed for lower computational complexity. Simulation results demonstrate that the hybrid mode outperforms pure HD and FD modes given that self-interference is efficiently suppressed.

The potential performance gains promised by massive multi‐input and multioutput (MIMO) rely heavily on the access to accurate channel state information (CSI), which is difficult to obtain in practice when channel coherence time is short and the number of mobile users is huge. To make the system with imperfect CSI perform well, we propose a rateless codes‐aided massive MIMO scheme, with the aim of approaching the maximum achievable rate (MAR) as well as improving the achieved rate over that based on the fixed‐rate codes. More explicitly, a recently proposed family of rateless codes, called spinal codes, are applied to massive MIMO systems, where the spinal codes bring the benefit of approximately achieving the MAR with sufficiently large encoding block size. In addition, the multilevel puncturing and dynamic block‐size allocation (MPDBA) scheme is proposed, where the block sizes are determined by user MAR to curb the average retransmission delay for successfully decoding the messages, which further enhances the system retransmission efficiency. Multilevel puncturing, which is MAR dependent, narrows the gap between the system MAR and the related achieved rate. Theoretical analysis is provided to demonstrate that spinal codes with the MPDBA can guarantee the system retransmission efficiency as well as achieved rate, which are also verified by numerical simulations. Finally, a simplified but comparable MIMO testbed with 2 transmit antennas and 2 single‐antenna users, based on NI Universal Software Radio Peripheral (USRP) and LabVIEW communication toolkits, is built up to demonstrate the effectiveness of our proposal in realistic wireless channels, which is easy to be extended to massive MIMO scenarios in future.

Massive MIMO technique is expected to greatly improve spectrum efficiency as well as energy efficiency of a communication system. However, to obtain the benefits, the system needs to get the ideal channel state information (CSI). In practice, however, a pilot-based channel estimation scheme needs to be applied to obtain the CSI, which may cause a serious pilot contamination problem for multi-cell massive MIMO systems. To deal with this issue, an Iterative Interference Cancellation based MMSE Channel Estimation Algorithm is proposed in this paper, by iteratively eliminating the inter-cell interference. Compared to conventional channel estimation algorithms, the proposed algorithm can effectively improve the channel estimation accuracy of a target cell with low computational complexity. Simulation results are provided to demonstrate the advantage of the proposed algorithm.

Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme in 5G systems for the simultaneous access of tremendous number of wireless nodes. On the other hand, RF-enabled wireless energy harvesting is a promising technology for self-sustainable wireless nodes. In this paper, we consider a NOMA system where the near user harvests energy from the strong radio signal to power-on the information decoder. A generalized energy harvesting framework is proposed by combining the conventional time switching and power splitting scheme, and the achievable rate regions for time switching and power splitting are characterized in closed-form. Numerical results demonstrate the relationship among generalized scheme, time switching scheme and power splitting scheme.

In this paper, we aim to obtain the optimal delay-power tradeoff and the corresponding optimal scheduling policy for arbitrary i.i.d. arrival process and adaptive transmissions. The number of backlogged packets at the transmitter is known to a scheduler, who has to determine how many backlogged packets to transmit during each time slot. The power consumption is assumed to be convex in transmission rates. Hence, if the scheduler transmits faster, the delay will be reduced but with higher power consumption. To obtain the optimal delay-power tradeoff and the corresponding optimal policy, we model the problem as a Constrained Markov Decision Process (CMDP), where we minimize the average delay given an average power constraint. By steady-state analysis and Lagrangian relaxation, we can show that the optimal tradeoff curve is decreasing, convex, and piecewise linear, and the optimal policy is threshold-based. Based on the revealed properties of the optimal policy, we develop an algorithm to efficiently obtain the optimal tradeoff curve and the optimal policy. The complexity of our proposed algorithm is much lower than a general algorithm based on Linear Programming. We validate the derived results and the proposed algorithm through Linear Programming and simulations.

Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme in 5G systems to simultaneously accommodate tremendous number of wireless nodes. On the other hand, RF-enabled wireless energy harvesting is a promising technology for self-sustained wireless devices. In this paper, we study a NOMA system where the near user harvests energy from the strong radio signal to power the information decoder. Both constant and dynamic decoding power consumption models are considered. For the constant decoding power model, the achievable rate regions for time switching and power splitting are characterized in closed-form. A generalized scheme is proposed by combining the conventional time switching and power splitting schemes, and its achievable rate region can be found by solving two convex optimization subproblems. For the dynamic decoding power model where the decoding power consumption is proportional to data rate, the achievable rate region can be found by a low-complexity search algorithm. Numerical results show that the achievable rate region of the generalized scheme is larger than those of the time switching scheme and power splitting scheme, and rate-dependent decoder design helps to enlarge the achievable rate region.

In this paper, we aim to obtain the optimal tradeoff between the average delay and the average power consumption in a communication system. In our system, the arrivals occur at each timeslot according to a Bernoulli arrival process, and are buffered at the transmitter waiting to be scheduled. We consider a finite buffer and allow the scheduling decision to depend on the buffer occupancy. In order to capture the realism in communication systems, the transmission power is assumed to be an increasing and convex function of the number of packets transmitted in each timeslot. This problem is modeled as a constrained Markov decision process (CMDP). We first prove that the optimal policy of the Lagrangian relaxation of the CMDP is deterministic and threshold-based. We then show that the optimal delay-power tradeoff curve is convex and piecewise linear, and the optimal policies of the original problem are also threshold-based. Based on the results, we propose an algorithm to obtain the optimal policy and the optimal tradeoff curve. We also show that the proposed algorithm is much more efficient than using general methods. The theoretical results and the algorithm are validated by linear programming and simulations.

In this paper, we consider a renewable energy powered wireless relay node which can work in either full- duplex (FD) or half-duplex (HD) mode. It decodes and stores data bits sent from source node, and then transfers them to destination node. We aim to maximize the average throughput from source to destination by optimizing the relay's working mode under the random energy arrival constraint. Optimal and sub-optimal policies are obtained by dynamic programming algorithm and linear programming based heuristic algorithm, respectively. It is found that pure FD/HD mode can achieve the optimal at high energy arrival rate regime or low rate regime. With moderate energy arrival rate, hybrid FD/HD mode is preferred, and the proposed heuristic algorithm performs close to the optimal. In addition, HD is optimal at low SNR regime as the strong self-interference greatly degrades the performance of FD.

Cross-layer design is a promising way to improve Quality of Services (QoS) by making use of the state information from different layers. In this paper, transmissions with random data arrival over fading channels are investigated. Probabilistic scheduling and adaptive modulation aware of both the queue state and the channel state are applied, hence we can formulate a Markov Decision Process. Based on its inherent Markov Reward Process, the average delay and power consumption are analysed and expressed by the steady-state probability distribution. We minimize the average delay given an average power constraint, so that by varying the power constraint, the optimal delay-power tradeoff curve is obtained. It is discovered that the optimization can be transformed into a linear programming. We further study the properties of the optimal scheduling policy and discover that it is threshold-based. These results are validated by numerical and Monte-Carlo simulations.

Soft errors induced by space radiation environments seriously influence the reliability of spacecrafts in space and satellite communications, especially with ever shrinking geometries, higher-density circuits, and power saving techniques. Most of the existing soft error mitigation methods depend on triple modular redundancy (TMR) or dual-modular redundancy (DMR) to the original design target directly, which enlarge the resource overhead dramatically. In this paper, the high level synthesis (HLS) is considered to help to reduce the resource consumptions of TMR or DMR. By the HLS on node sensitivity, all design resources can be classified into three types: sensitive submodules, semi-sensitive sub-modules, and insensitive submodules. TMR can be applied for sensitive sub-modules to provide the highest reliability, while gate sizing can be applied for semi-sensitive sub-modules, which can help to mitigate the soft errors and to minimize the overhead introduced by the fault-tolerant techniques efficiently. In order to verify the effectiveness of the above proposal, appropriate scheduling schemes combined with the HLS are performed to an FIR filter. By simulations it is shown that, with the reduction of area relative to TMR over 60% for the FIR design, the reliability can reach over 99.9%.

Wireless body area network (WBAN) has drawn more and more interests in recent years. As one alternative communication scheme for WBAN, human body communication (HBC) uses human body as the communication medium and it provides better performance for communication security, spectrum efficiency, power consumption, and electromagnetic compatibility. The aims of this paper are to measure and characterize a capacitive HBC channel and to build simple models for it. Measurements have been carried out for different electrode positions and different body shapes. The results show that the path-loss of HBC channel is a function of frequency and it needs to be modeled by separated frequency intervals. A general model is proposed for the path-loss for different scenarios and the model parameters are extracted by fitting methods. The impact of electrode positions on the path-loss is analyzed. To describe the dispersion effects in HBC channel, the coherent bandwidth and delay spread of HBC channels in different scenarios are also extracted from measured data. These results aim at providing references for the design and deployment of HBC systems.

Full-duplex relaying (FDR) is an efficient technique to serve remote users with low power consumption, however, careful interference suppression is needed to mitigate self interference (SI) and multi-user interference (MUI). When multiple FDRs are employed, inter-relay interference (IRI) emerges and degrades system performance severely by consuming excessive transmit power. In this paper, distributed interference suppression in multiple full-duplex relays aided multiuser networks is investigated. A general model addressing all the three types of interference is established first. To minimize the total transmit power of the system, a distributed beamforming algorithm is proposed to jointly mitigate the three types of interference under individual user rate constraints. The algorithm requires only local information exchange at the relays, hence evidently reducing the signaling overhead in large-scale networks. Furthermore, theoretical proof for the algorithm's convergence is provided, and an upper bound for the iterative stepsize guaranteeing convergence is also derived. Numerical simulations validate effectiveness and stability of our proposal.

Because of the high system capacity and excellent capability against narrowband interference (NBI), Direct Sequence-Code Division Multiple Access (DS-CDMA) is widely used in Geosynchronous Earth Orbit satellite systems. However, due to the existence of the uncertain non-cooperative external interference, traditional colored noise suppression methods cannot achieve high performance in DS-CDMA systems. In this paper, based on spectrum shaping, combining with the feature analysis of the external interference, an eigen-based spreading sequences design framework for CDMA satellite systems is proposed. In this proposal, by the uniform orthogonal transformation (UOT), the eigen-based spreading sequences can combat not only the multiple access interference (MAI) but also the external interference and support multiple users' performance fairness. Furthermore, the design physical significance is analyzed. By simulations, it's verified that both MAI and the external interference can be eliminated by the proposed eigen-based spreading sequences and the fairness of different users can be efficiently guaranteed.

This paper considers an OFDM wiretap channel with a legitimate transmitter (Alice), multiple legitimate receivers (Bobs), and an eavesdropper (Eve). Alice simultaneously transmits confidential message to each individual Bob. The timedomain Artificial noise (AN) is firstly employed to the wiretap OFDM channel with multiple Bobs. Under the proposed AN approach, a nonconvex sum secrecy rate maximization problem is formulated to jointly optimize subcarrier allocation, power allocation and AN design. To solve this tough problem, the optimal subcarrier allocation is found at first, and then a low-complexity Lagrange dual method is developed to jointly optimize power allocation and AN design. Finally, numerical results demonstrate the effectiveness of the proposed algorithms, including power allocation gain, AN gain and multiuser gain.

Paired Carrier Multiple Access (PCMA) is widely used in bandwidth limited satellite network systems for its high frequency efficiency and compatibility with existing communication methods. With blind detection for PCMA signal being an important topic, various blind detection methods for PCMA signals with specific properties have been proposed. However, information theory analysis is still required for general blind detection method design. This paper introduces information theoretical bound for blind detection using a simulation based computation method, and applies a Viterbi detection method to verify the bound. Given a PCMA signal, the information theoretical bound helps to evaluate whether blind detection is possible, and guides how to design specific blind detection methods efficiently. Simulation result shows how mutual information carried by PCMA signal of the communicating peers is influenced by signal fading, propagation delay and other parameters numerically.

High-resolution wideband digital channelizer is the key component for extraction and reconstruction of multichannel signals in software radio systems. It has been proved that complex-exponential modulated (CEM) perfect reconstruction (PR) filter-bank is an efficient structure for the wideband channelizer, in which the Parks-McClellan (PM) algorithm based design method for the PR prototype filter and PR filterbank is effective when the number of sub-channels is less than 1024. However, due to the limitation of the PM algorithm for the filters with extremely high-order or narrow transition band, it doesn't work well when the number of sub-channels increases further. In this paper, we propose an efficient design method for such high-order PR filterbanks, which is based on the classical Frequency Sampling (FS) method and the criterion of least mean square error (LMSE). By this proposal, the designs for any high-order PR filters and related high-resolution wideband channelizers can be guaranteed with excellent reconstruction performance. Some numerical simulations are provided to verify the effectiveness of the proposed method, even when the number of sub-channels reaches to 8192.

SUMMARY Cognitive radio (CR) can significantly alleviate the network pressure caused by the rapid development of wireless communications through allowing secondary users (SUs) to obtain spectrum resource from primary users (PUs). One key issue of CR technology is spectrum sharing, that is, how spectrum should be allocated between SUs without causing interference to PUs. In this paper, we propose abilateral bargainingmechanism to achieve this goal between two SUs. The general network scenario with multiple SUs can be decomposed into multiple pairs of bilateral bargaining studied in this paper. The SUs have to reach a mutual satisfactory agreement on the partition of spectrum by making alternating offers to each other. We model such bargaining process as dynamic finite/infinite horizon multistage game with observed actions and fully characterize the corresponding subgame perfect equilibria. Moreover, theoretical analysis and numerical results indicate that our proposed scheme can effectively allocate spectrum resource between SUs. Copyright © 2012 John Wiley & Sons, Ltd.

Most of the existing works on two-way frequency division multiplexing (OFDM) relay channels was centered on per-subcarrier decode-and-forward (DF) relaying, where each subcarrier is treated as a separate channel, and channel coding is performed separately over each subcarrier. In this paper, we show that this per-subcarrier DF relay strategy is suboptimal. More specifically, we present a multi-subcarrier DF relay strategy which achieves a larger rate region by adopting cross-subcarrier channel coding. Then we develop an optimal resource allocation algorithm to characterize the achievable rate region of the proposed multi-subcarrier DF relay strategy. Compared to standard Lagrangian duality optimization algorithms, our algorithm has a much smaller computational complexity due to the use of the structure property of the optimal resource allocation solution. We further prove that our multi-subcarrier DF relay strategy tends to achieve the capacity region of the two-way OFDM relay channels in the low signal-to-noise ratio (SNR) regime, and the amplify-and-forward (AF) relay strategy tends to achieve the multiplexing gain region of the two-way OFDM relay channels in the high SNR regime. Our theoretical analysis and numerical results demonstrate that DF relaying has better performance in the low to moderate SNR regime, while AF relaying is more appropriate in the high SNR regime.

Optimal power allocation for orthogonal frequency division multiplexing (OFDM) wiretap channels with Gaussian channel inputs has already been studied in some previous works from an information theoretical viewpoint. However, these results are not sufficient for practical system designs. One reason is that discrete channel inputs, such as quadrature amplitude modulation (QAM) signals, instead of Gaussian channel inputs, are deployed in current practical wireless systems to maintain moderate peak transmission power and receiver complexity. In this paper, we investigate the power allocation and artificial noise design for OFDM wiretap channels with discrete channel inputs. We first prove that the secrecy rate function for discrete channel inputs is nonconcave with respect to the transmission power. To resolve the corresponding nonconvex secrecy rate maximization problem, we develop a low-complexity power allocation algorithm, which yields a duality gap diminishing in the order of O(1/√N), where N is the number of subcarriers of OFDM. We then show that independent frequency-domain artificial noise cannot improve the secrecy rate of single-antenna wiretap channels. Towards this end, we propose a novel time-domain artificial noise design which exploits temporal degrees of freedom provided by the cyclic prefix of OFDM systems to jam the eavesdropper and boost the secrecy rate even with a single antenna at the transmitter. Numerical results are provided to illustrate the performance of the proposed design schemes.

This paper investigates the distributed power allocation problem for coordinated multipoint (CoMP) transmissions in distributed antenna systems (DAS). Traditional duality-based optimization techniques cannot be directly applied to this problem, because the non-strict concavity of the CoMP transmission's achievable rate with respect to the transmission power induces that the local power allocation subproblems have non-unique optimum solutions. We propose a distributed power allocation algorithm to resolve this non-strict concavity difficulty. This algorithm only requires local information exchange among neighboring base stations serving the same user, and is thus flexible with respect to network size and topology. The step-size parameters of this algorithm are determined by only local user access relationship (i.e., the number of users served by each antenna), but do not rely on channel coefficients. Therefore, the convergence speed of this algorithm is quite robust to channel fading. We rigorously prove that this algorithm converges to an optimum solution of the power allocation problem. Simulation results are presented to demonstrate the effectiveness of the proposed power allocation algorithm.

This paper presents a novel two-way decode-and-forward (DF) relay strategy for Orthogonal Frequency Division Multiplexing (OFDM) relay networks. This DF relay strategy employs multi-subcarrier joint channel coding to leverage frequency selective fading, and thus can achieve a higher data rate than the conventional per-subcarrier DF relay strategies. We further propose a low-complexity, optimal power allocation strategy to maximize the data rate of the proposed relay strategy. Simulation results suggest that our strategy obtains a substantial gain over the per-subcarrier DF relay strategies, and also outperforms the amplify-and-forward (AF) relay strategy in a wide signal-to-noise-ratio (SNR) region.

Relative to the Triple Modular Redundancy (TMR) scheme, the arithmetic residue codes based fault-tolerant DSP design consumes much less resources. However, the price for the low resource consumption is the fault missing problem. The basic tradeoff is that, smaller modulus used for the fault checking consumes fewer resources, but the fault missing rate is higher. The relationship between the value of modulus and the fault missing rate is analyzed theoretically in this paper for fault-tolerant FIR filter design, and the results are verified by FPGA implemented fault injections.

In this paper we propose a novel algorithm to predict the performance of interleaver division multiple access (IDMA) systems, which is called mutual information evolution algorithm (MIEA). The proposed MIEA is a semi-analytical technique, which is based on but a little different from the traditional extrinsic information transfer (EXIT) chart especially when used for the successive interference cancellation (SIC) structure in IDMA systems. Based on MIEA, not only the convergence behavior of iteration structures in IDMA systems can be analyzed, but also the trade-off between code-rate (CR) and spreading-factor (SF) can be obtained. Moreover, the MIEA can be used to search the optimal codes in IDMA. Simulation results are also presented to confirm our analysis.

In this paper, a novel analytical model structure for wideband multiple-input multiple-output (MIMO) channel is presented. It is based on the power coupling between direction of departure (DoD), direction of arrival (DoA) and delay domain. As its realizations, firstly the singular value decomposition (SVD) based model is introduced and the virtual presentation model is extended to the wideband situation. Then a hybrid model is given on basis of the power coupling between the transmit eigenmodes, receive eigenmodes and frequency steering vectors. The hybrid model can provide the tradeoff between the complexity and accuracy. At last, the novel coupling-based model structure is summarized. With a 3.52 GHz wideband MIMO sounder, measurements are carried out in different indoor scenarios. Monte Carlo simulations are used to generate the channel realizations according to these proposed wideband models. Good agreements are achieved between the discussed models and measured data.

Multicell processing (MCP) techniques have attracted considerable research attention since they can effectively mitigate other-cell interference (OCI). However, the potential gain of MCP comes with several practical challenges including huge overhead and stringent synchronization requirements. In this paper, we propose cooperative opportunistic scheduling to coordinate OCI for downlink transmission in multiple antenna cellular networks, which incurs less overhead and relaxes synchronization requirements. As a baseline for comparison, we study the average throughput with noncooperative opportunistic scheduling and derive the throughput expression in closed form. We show that the scaling law of average throughput with noncooperative opportunistic scheduling follows the log log K form, where K is the number of users per cell. Furthermore, with the assumption that adjacent cells can exchange channel state information, we propose a cooperative opportunistic scheduler. Using extreme value theory, we obtain concise expression for the average throughput with the cooperative opportunistic scheduler, which is proven to scale like log K. Our analysis are verified by simulation results.

The combination of relay and Multiple-Input Multiple-Output (MIMO) techniques can significantly benefit the signal transmission in cellular networks. In this work, we propose a novel relaying scheme for the relay assisted MIMO broadcast channel including user classification and preceding. The users are firstly classified as Direct Link (DL) users and Relay Link (RL) users according to their channel conditions. After classification, both DL and RL users may coexist in the system, and Zero-Forcing (ZF) preceding is employed at both the BS and the relay for interference cancellation. Analysis and simulation results show that the proposed relaying scheme can effectively combat the large scale fading of the channels and provide significant performance gain compared with the conventional MIMO relaying schemes without user classification.

A low-complexity Kalman-filter-based channel estimation method for orthogonal frequency-division multiplexing systems is proposed. This method belongs to the pilot-symbol-aided parametric channel estimation method in which the channel responses are characterised as a collection of sparse propagation paths. Because of the slow variation of the signal subspace in the channel samples' correlation matrix, the estimation of channel parameters is translated into an unconstrained minimisation problem. Then, in order to solve this optimisation problem, a subspace tracking by Kalman filter is carried out, which is characterised in that the state equation and the measurement equation are constructed upon the constant signal subspace. Further, this Kalman-filter-based method is extended to the multi-antenna scenarios efficiently. Simulation results show that the proposal can effectively track the time variations in both the block fading channels and the Doppler frequency spread channels.

In this paper, a group of low-complexity subspace tracking based pilot-aided channel estimation methods for orthogonal frequency-division multiplexing (OFDM) systems is studied. These methods are based on a parametric channel model where the channel response is characterized as a collection of sparse propagation paths. Considering the slow variations of the channel correlation matrix's signal subspace, we first translate the estimation of channel parameters into an unconstrained minimization problem. Then, to solve this minimization problem, a novel Kalman-filter based subspace tracking method is proposed, which employs the constant signal subspace to construct state equation and measurement equation. In contrast, other two adaptive filters, LMS and RLS, are applied and evaluated. These three methods constitute a group of low-complexity subsapce tracking schemes. It is shown that the proposed Kalman filter method is able to effectively track the time-varying channels, and outperforms LMS and RLS methods with large Doppler frequency spread.

In orthogonal frequency division multiplexing (OFDM) systems, time variation of multipath channels over an OFDM block period leads to a loss of orthogonality among subcarriers, resulting in intercarrier interference (ICI). In this paper, based on the analysis of the symbol energy distribution and the ICI generation mechanism, we propose a low-complexity partial minimum mean-squared error (MMSE) with successive detection (PMMSESD) scheme in frequency domain to mitigate ICI caused by time-varying channels. Each time, the proposed method detects the symbol with the largest received signal-to-interference-plus-noise ratio (SINR) among all the undetected symbols, using an MMSE detector only considering the interference of several neighborhood subcarriers. Analysis and simulation results show that our PMMSESD method can effectively reduce the ICI with low computational complexity, it outperforms the MMSE method at relatively high Eb/N0, and its performance is close to the MMSE with successive detection (MMSESD) method in the relatively low Doppler frequency region