In recent years, extensive research has been conducted on radio resource allocation (RRA) in vehicular networks. Many studies have employed multi-agent Deep Reinforcement Learning (DRL) as an effective approach for making decentralized RRA decisions in highly dynamic and uncertain vehicular environments. However, a systematic evaluation and comparison of various multi-agent DRL algorithms in vehicular contexts remain lacking. In this paper, we address this gap by framing RRA problems in Cellular Vehicle-to-Everything (C-V2X) networks as a series of multi-agent interference games with ascending complexity as more realistic factors are introduced. We benchmark performance of classical multi-agent DRL algorithms in these environments. Our results offer insights into the relative significance of different Multi-Agent Reinforcement Learning (MARL) challenges in C-V2X RRA tasks, along with a comparative evaluation of multiple algorithms.

Collective Perception Messages (CPMs), which carry sensor data about surrounding objects and vehicle kinematics, are exchanged among Connected and Autonomous Vehicles (CAVs) to enhance situational awareness. This paper proposes a Value of Information (VoI)-driven CPM generation mechanism that enables each CAV to proactively determine when to transmit a CPM and what content to include, based on its assessed value to neighboring CAVs. The VoI of perceptual information is first defined from the perspective of the receiving CAV, by analyzing the spatiotemporal correlation between perceived objects and the receiver’s driving context. Guided by this, each CAV performs a three-stage process to generate CPMs in order to enhance information exchange efficiency, thereby improving Cooperative Perception (CP) performance. Simulation results demonstrate that the proposed mechanism effectively improves perception accuracy and reduces the communication burden on V2X networks.

Abstract The dual function radar and communication technology plays a crucial role in Internet of Vehicles. However, fewer studies have focused on optimizing the joint performance of sensing and communication in full‐duplex Internet of Vehicles while considering fairness. Therefore, this paper considers the scenario that the full‐duplex gNB employs dual function radar and communication technology to simultaneously achieve vehicle localization and communication with multiple half‐duplex vehicles. By leveraging a reasonable beamforming scheme to mitigate the residual self‐interference of the echo signal, an optimization problem that maximizing the joint performance metric of sensing and communication is formulated with limited power at the gNB. After problem transformation, a double descent iteration power allocation algorithm is proposed to solve the non‐convex optimization problem and the complexity of the algorithm is analysed. Simulation results demonstrate that the proposed algorithm converges and could fairly improve both the sensing and communication performance.

Collaborative autonomous driving holds significant potential to improve the performances of Connected Autonomous Vehicles (CAVs). This paper presents a communication-aware hierarchical driving control mechanism designed to operate under non-ideal Vehicle-to-Vehicle (V2V) communication conditions. To address the impact of delayed and partial observations of CAV, a state augmentation method is first introduced to convert the resulting random-delay partially observable Markov decision process (RD-POMDP) into a standard Markov decision process (MDP), enabling the application of deep reinforcement learning (DRL) algorithms with theoretical convergence guarantees. Based on this formulation, a hierarchical DRL framework is developed, comprising an event-triggered upper-level for driving behavior adaptation at a coarse time scale and a periodic lower-level for motion control at a fine time scale. A modified Twin Delayed Deep Deterministic Policy Gradient with Prioritized Experience Replay (TD3-PER) algorithm is used to train the lower-level motion control policy, while an option-critic framework is employed to train the upper-level behavior policy, leveraging the pretrained low-level policies. Simulation results demonstrate the effectiveness of the proposed mechanism in collaborative driving scenarios with imperfect V2V communications.

In Part I of this two-part paper (Multitimescale Control and Communications with deep reinforcement learning (DRL)—Part I: Communication-Aware Vehicle Control), we decomposed the multitimescale control and communications (MTCCs) problem in cellular vehicle-to-everything (C-V2X) system into a communication-aware DRL-based platoon control (PC) subproblem and a control-aware DRL-based radio resource allocation (RRA) subproblem. We focused on the PC subproblem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy. In this article (Part II), we first focus on the RRA subproblem in MTCC assuming a PC policy is given, and propose the MTCC-RRA algorithm to learn the RRA policy. Specifically, we incorporate the PC advantage function in the RRA reward function, which quantifies the amount of PC performance degradation caused by observation delay. Moreover, we augment the state space of RRA with PC action history for a more well-informed RRA policy. In addition, we utilize reward shaping and reward backpropagation prioritized experience replay (RBPER) techniques to efficiently tackle the multiagent and sparse reward problems, respectively. Finally, a sample- and computational-efficient training approach is proposed to jointly learn the PC and RRA policies in an iterative process. In order to verify the effectiveness of the proposed MTCC algorithm, we performed experiments using real driving data for the leading vehicle, where the performance of MTCC is compared with those of the baseline DRL algorithms.

An intelligent decision-making system enabled by vehicle-to-everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of decisions have to be made at different timescales, i.e., vehicle control and radio resource allocation (RRA) decisions. The interplay between RRA and vehicle control necessitates their collaborative design. In this two-part paper (Part I and Part II), taking platoon control (PC) as an example use case, we propose a joint optimization framework of multitimescale control and communications (MTCCs) MTCCs based on deep reinforcement learning (DRL). In this article (Part I), we first decompose the problem into a communication-aware DRL-based PC subproblem and a control-aware DRL-based RRA subproblem. Then, we focus on the PC subproblem assuming an RRA policy is given, and propose the MTCC- PC algorithm to learn an efficient PC policy. To improve the PC performance under random observation delay, the PC state space is augmented with the observation delay and PC action history. Moreover, the reward function with respect to the augmented state is defined to construct an augmented state Markov decision process (MDP). It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay. Different from most existing works on communication-aware control, the MTCC- PC algorithm is trained in a delayed environment generated by the fine-grained embedded simulation of cellular vehicle-to-everything communications rather than by a simple stochastic delay model. Finally, experiments are performed to compare the performance of MTCC- PC with those of the baseline DRL algorithms.

Deep-learning (DL)-based channel state information (CSI) feedback has the potential to improve the recovery accuracy and reduce the feedback overhead in massive multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. However, the length of input CSI and the number of feedback bits should be adjustable in different scenarios, which cannot be efficiently achieved by the existing CSI feedback models. Therefore, an adaptive bidirectional long short-term memory network (ABLNet) for CSI feedback is first designed to process various input CSI lengths, where the number of feedback bits is in proportion to the CSI length. Then, to realize a more flexible feedback bit number, a feedback bit control unit (FBCU) module is proposed to control the output length of feedback bits. Based on which, a target feedback performance can be adaptively achieved by a designed bit number adjusting (BNA) algorithm. Furthermore, a novel separate training approach is devised to solve the model protection problem that the user equipment and next-generation nodeB are from different manufacturers. Experiments demonstrate that the proposed ABLNet with FBCU can fit for different input CSI lengths and feedback bit numbers; the CSI feedback performance can be stabilized by the BNA algorithm; and the proposed separate training approach can maintain the feedback performance and reduce the complexity of the feedback model.

Accurate analysis of traffic flow (TF) data is crucial for the vehicular applications. Conventional deep learning models require task-specific training and are susceptible to high-frequency disturbances, degrading the feature representation capability. To overcome these limitations, this paper proposes a Token-based SelfSupervised Network (TSSN) that can learn TF features in both tokenization and task-agnostic manners. It provides a properly bootstrapped pre-training model for various downstream tasks. In support of the edge computing and vehicular cloud computing, the pooled computational resources facilitate real-time inferences of downstream models. In TSSN, TF data are segmented into tokens. A pretext task, named as Masked Token Prediction (MTP), is then developed to allow TSSN to understand the underlying correlations of TF by predicting randomly masked tokens. By utilizing MTP, TSSN is able to extract the high-level intrinsic semantics of TF, and provide general-purpose token embeddings, leading to improved overall performance and enhanced ability to adapt to different tasks. By substituting the last fully-connected layers with a group of untrained new layers and fine-tuning using small-scale task-specific data, TSSN can be utilized for a variety of downstream tasks in vehicular applications. Simulation results indicate that the TSSN enhances overall performance in comparison to state-of-the-art models.

To achieve accurate awareness of channel condition, the access point (AP) of a Wi-Fi network has to collect channel state information (CSI) from stations (STAs) periodically. However, existing CSI feedback mechanisms in Wi-Fi are situation agnostic, leading to substantial overhead due to the lack of adaptability and intelligence under dynamic and complex environments. To address this challenge, a concurrent dual channel information feedback mechanism with improve situation-awareness is proposed based on need-driven AP-STA coordination, aiming to maintain the accuracy of collected CSI while dramatically reducing the feedback overhead. By analyzing the latency tolerance of the channel information to be gathered, this concurrent dual feedback mechanism consists of both a delayed channel feature information (CFI) feedback by data frame and an immediate CSI feedback via control frame. In the delayed CFI feedback, a STA collaborates with AP and proactively determines when and what content of CFI to be fed back to the AP. Specifically, the CFI represents channel statistical channel features, which are crucial for the AP to learn the evolving channel conditions. Then, CFI is transmitted to the AP by piggybacking it in the uplink data payload at cost of a certain delay. On the other hand, STA can also utilize the existing CSI feedback mechanism for immediate CSI feedback. With the situation-aware CFI updates from both feedbacks, AP can effectively infer the downlink channel pattern and adapt the time-frequency resolution of CSI feedback to reduce the overhead. Accordingly, a deep cooperative multi-agent reinforcement learning algorithm is proposed to enable a closed-loop coordination between STA and AP for feedback. Simulation results confirm the effectiveness of our proposed mechanism.

With the development of Internet of Things (IoT) technology, the wearable devices have been widely used in different fields. However, few studies have focused on the application of wearable devices and IoT technologies on student movement detection systems for Physical Education (PE). This paper mainly designs an Message Queuing Telemetry Transport (MQTT)-based student condition monitoring system for student physical status monitoring, where the network transmission from the perception layer to the application layer in a multi-user scenario is considered. The proposed system consists of a data acquisition module, a communication module for message transmission, and a data analysis module for application. The (MQTT) protocol, as a low-overhead and low-bandwidth-consumption instant messaging protocol, is applied to enable the data to be published to the application layer clients that has subscribed to the corresponding topics in real-time. During the experiment, each perception layer client publishes different amounts of data to the corresponding topics of the broker, simulating multiple users sending data to the broker, and it sends the received data to the application layer client through the data flow function. The results show that the MQTT protocol has low latency in multi-user situations. Also, MQTT is able to provide real-time and reliable messaging services to the wearable devices with minimal data volume and limited bandwidth, which reveals the feasibility of its application in smart education.

The need for the campus safety arises with the increase of the number of colleges and campus facilities, posing significant challenges to traditional campus surveillance system. As a result, it is necessary to build a reliable, cross-platform and extensible surveillance system for campus monitoring. Therefore, this paper mainly proposes a web-based campus surveillance system based on ZLMediaKit, which is an open-source and high-performance streaming service framework. The system consists of a push/pull streaming server and a management front-end page. The streaming server supports numerous streaming media protocols and massive client connections, while the front-end page can access to back-end video streams and real-time display. Experimental results show that our system is stable and capable of handling multi-terminal streams with small consumption of resources.

In autonomous vehicle platooning, Vehicle-to-Everything (V2X) communications are leveraged in cooperative adaptive cruise control (CACC) to improve control performance. Since exchanging information at all times incurs significant communication overhead in vehicular networks, it is important to determine when V2X communication is necessary. To solve this problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm named Attention-DDPG, which learns platoon control with Deep Deterministic Policy Gradient (DDPG), and learns when to communicate with an attention network. Specifically, each preceding vehicle is equipped with a deep neural network (DNN), which takes as input its local state and platoon control action and determines whether to transmit its acceleration or not to the following vehicle at each time step. The attention network of a preceding vehicle is trained using the feedback from the following vehicle on the value of V2X information in the form of an advantage function. In order to evaluate Attention-DDPG, simulations are performed using real driving data, and performance is compared with those of two baselines that communicate and do not communicate at all times, respectively. The results demonstrate that Attention-DDPG strikes a competitive tradeoff between control performance and communication overhead while ensuring platoon string stability.

Autonomous vehicles in a platoon determine the control inputs based on the system state information collected and shared by the Internet of Things (IoT) devices. Deep reinforcement learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challenging to learn an efficient car-following policy with convergence stability when there are multiple following vehicles in a platoon, especially with unpredictable leading vehicle behavior. In this context, we adopt an integrated DRL and dynamic programming (DP) approach to learn autonomous platoon control policies, which embeds the deep deterministic policy gradient (DDPG) algorithm into a finite-horizon value iteration framework. Although the DP framework can improve the stability and performance of DDPG, it has the limitations of lower sampling and training efficiency. In this article, we propose an algorithm, namely, finite-horizon-DDPG with sweeping through reduced state space using stationary approximation (FH-DDPG-SS), which uses three key ideas to overcome the above limitations, i.e., transferring network weights backward in time, stationary policy approximation for earlier time steps, and sweeping through reduced state space. In order to verify the effectiveness of FH-DDPG-SS, simulation using real driving data is performed, where the performance of FH-DDPG-SS is compared with those of the benchmark algorithms. Finally, platoon safety and string stability for FH-DDPG-SS are demonstrated.

With the wide range of Internet of Things (IoT) applications, federated learning (FL) is commonly adopted to protect the privacy of IoT data. FL enables privacy-preserving model training while keeping the data locally available. To alleviate the additional load caused by FL, an improved hierarchical aggregation framework is presented in this article to decentralize the model aggregation tasks based on end-device clusters. However, when applying FL to IoT networks, how to keep high efficiency and reliability remains open challenges due to a large number and vulnerability of IoT end devices. In this article, we propose a blockchain-assisted aggregation scheme for FL in IoT networks, where the aggregation node selection is applied for efficiency improvement as well as blockchain for performance verification. During model aggregation, a selection strategy is obtained by the deep deterministic policy gradient (DDPG) algorithm and aims to select the optimal subset of IoT end devices based on multiple metrics. Furthermore, a new performance verification based on the characteristics of blockchain is applied to achieve mutual verification among a number of untrustworthy nodes with the optimal stopping theory, which provides reliable model performance proofs. Simulation results show that the proposed scheme can maintain FL efficiency and reduce the system latency while protecting data privacy.

In this article, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and load data of previous days, where the policy can generate real-time decisions based on observations of past renewable and load data of previous hours collected by connected sensors. The goal is to reduce operating cost on the premise of ensuring supply–demand balance. In specific, a novel finite-horizon partial observable Markov decision process (POMDP) model is conceived considering the spinning reserve. In order to overcome the challenge of discrete-continuous hybrid action space due to the binary DG switching decision and continuous energy dispatch (ED) decision, a DRL algorithm, namely, the hybrid action finite-horizon RDPG (HAFH-RDPG), is proposed. HAFH-RDPG seamlessly integrates two classical DRL algorithms, i.e., deep$Q$-network (DQN) and recurrent deterministic policy gradient (RDPG), based on a finite-horizon dynamic programming (DP) framework. Extensive experiments are performed with real-world data in an IoT-driven MG to evaluate the capability of the proposed algorithm in handling the uncertainty due to interhour and interday power fluctuation and to compare its performance with those of the benchmark algorithms.

With a plethora of remote sensing (RS) images, deep neural network-based semantic segmentation models (SegModels) achieve commendable road extraction performance. However, the occlusions caused by vehicles, roadside objects and shadows cannot be directly identified as road pixels, especially on high-resolution RS images. Therefore, relying only on a single SegModel to guarantee road connectivity and boundary smoothness in road extraction tasks is extremely difficult. To address this issue, this paper puts forward a “Segmentation-with-Reconstruction” framework, which comprises a SegModel to generate the binary road labels from RS images, and a reconstruction model to refine the road labels. Specifically, the former can be compatible with arbitrary existing SegModels, while the latter is built by our proposed model named as all-visible denoising auto-encoder (AV-DAE). The AV-DAE is designed to be an encoder-decoder architecture that takes topology-corruption road labels as inputs and true road labels as outputs. To better train the AV-DAE, we further present three noise-adding strategies to corrupt road labels for diverse patterns, and train the AV-DAE to reconstruct them. Being RS-image-agnostic, the AV-DAE pays more attention to the spatial features rather than the spectral features, which enables it to recover the road topology through improving the connectivity and boundary smoothness. Finally, elaborate simulation results demonstrate that the proposed framework can significantly improve the connectivity and boundary smoothness of the extracted roads, while achieving a competitive road extraction performance and high generalization ability, as compared to the benchmarks.

The integration of the blockchain and Internet of Things (IoT) systems can effectively guarantee data security in IoT applications. To facilitate the use of blockchain on resource-constrained IoT end devices, we propose RAFT+ with a new leader selection scheme in this article, which is based on the distributed consensus algorithm RAFT. The design of RAFT+ aims at mitigating the imparities between different types of IoT end devices and enabling these devices to allow different types of IoT end devices to participate in block consensus, thus maintaining strong consistency of the blockchain network. The leader selection scheme is generated by a deep${Q}$-Network (DQN), which can make the optimal selection of the leader under various conditions by leveraging the limited system resources as well as balancing the load of the consensus mechanism on multiple IoT end devices. Simulation results show that RAFT+ can enhance the system performance while maintaining the security of the system under high load conditions.

Autonomous driving vehicles can reduce congestion and improve safety while increasing traffic efficiency. To reflect the quality of driving more comprehensively, the driving safety, efficiency, and occupant comfort should be jointly optimized for autonomous vehicles. Furthermore, in order to cope with complicated traffic environments and achieve satisfactory driving performance, a powerful behavior decision-making module is indispensable for autonomous vehicles. Toward this end, we study a reinforcement-learning (RL)-based method to intelligently make the behavior decision in this article. A Markov decision process (MDP) model is first formulated with a comprehensive reward function, including the effects of driving safety, efficiency, and comfort. The knowledge of the surrounding vehicles is also leveraged to exploit the behavior prediction of the target vehicle. We then propose a behavior decision strategy based on the actor–critic (AC) mechanism, which can efficiently learn both a Gaussian policy function and a linear value function. Finally, the real traffic data are used to build up the simulations for evaluating the performances of the proposed method thoroughly. Simulation results show that our proposed method can significantly reduce the collision rate for autonomous vehicles.

With the emergence of Blockchain-based Internet of Things (BIoT) applications, smart contracts have become one of the most appealing aspects because they reduce the cost and complexity of distributed administration. However, the immaturity of smart contracts may result in significant financial losses or the leakage of sensitive information. This article first investigates the taxonomy of security issues associated with smart contracts considering BIoT scenarios. To address these security concerns and overcome the limitations of existing methods, a tree-based machine learning vulnerability detection (TMLVD) method is proposed to perform the vulnerability analysis of smart contracts. TMLVD feeds the intermediate representations of smart contracts derived from abstract syntax trees (AST) into a tree-based training network for building the prediction model. Multidimensional features are captured by this model to identify smart contracts as vulnerable. The detection phase can be implemented quickly with limited computing resources and the accuracy of the detection results is guaranteed. The experimental evaluation demonstrated the effectiveness and efficiency of TMLVD on a data set comprised of Ethereum smart contracts.

Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in dynamic vehicular communications still faces many challenges, particularly time-varying traffic of Vehicle-to-Vehicle (V2V) services and the fast-changing network topology. By leveraging the widely deployed LTE infrastructures, we propose a semi-decentralized network slicing framework in this paper based on the C-V2X Mode-4 standard to provide customized network slices for diverse V2V services. With only the long-term and partial information of vehicular networks, eNodeB (eNB) can infer the underlying network situation and then intelligently adjust the configuration for each slice to ensure the long-term QoS performance. Under the coordination of eNB, each vehicle can autonomously select radio resources for its V2V transmission in a decentralized manner. Specifically, the slicing control at the eNB is realized by a model-free deep reinforcement learning (DRL) algorithm, which is a convergence of Long Short Term Memory (LSTM) and actor-critic DRL. Compared to the existing DRL algorithms, the proposed DRL neither requires any prior knowledge nor assumes any statistical model of vehicular networks. Furthermore, simulation results show the effectiveness of our proposed intelligent network slicing scheme.

Efficiency and security have become critical issues during the development of the long-range (LoRa) system for Internet-of-Things (IoT) applications. The centralized work method in the LoRa system, where all packages are processed and kept in the central cloud, cannot well exploit the resources in LoRa gateways and also makes it vulnerable to security risks, such as data falsification or data loss. On the other hand, the blockchain has the potential to provide a decentralized and secure infrastructure for the LoRa system. However, there are significant challenges in deploying blockchain at LoRa gateways with limited edge computing abilities. This article proposes a design and implementation of the blockchain-enabled LoRa system with edge computing by using the open-source Hyperledger Fabric, which is called as HyperLoRa. According to different features of LoRa data, a blockchain network with multiple ledgers is designed, each of which stores a specific kind of LoRa data. LoRa gateways can participate in the operations of the blockchain and share the ledger that keep the time-critical network data with small size. Then, the edge computing abilities of LoRa gateways are utilized to handle the join procedure and application packages processing. Furthermore, a HyperLoRa prototype is implemented on embedded hardware, which demonstrates the feasibility of deploying the blockchain into LoRa gateways with limited computing and storage resources. Finally, various experiments are conducted to evaluate the performances of the proposed LoRa system.

Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This article focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-driven smart isolated MGs with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon partial observable Markov decision process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, finite-horizon deep deterministic policy gradient (FH-DDPG) and finite-horizon recurrent deterministic policy gradient (FH-RDPG), are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated MG data is performed, where the performance of the proposed algorithms are compared with the other baseline DRL and non-DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated, respectively.

Network slicing is a key paradigm in 5G and is expected to be inherited in future 6G networks for the concurrent provisioning of diverse quality of service (QoS). Unfortunately, effective slicing of Radio Access Networks (RAN) is still challenging due to time-varying network situations. This paper proposes a new intelligent RAN slicing strategy with two-layered control granularity, which aims at maximizing both the long-term QoS of services and spectrum efficiency (SE) of slices. The proposed method consists of an upper-level controller to ensure the QoS performance, which enforces loose control by performing adaptive slice configuration according to the long-term dynamics of service traffic. The lower-level controller is to improve SE of slices, by tightly scheduling radio resources to users at the small time-scale. To realize the proposed RAN slicing strategy, we propose a model-free deep reinforcement learning (DRL) framework, which is a hierarchical structure that collaboratively integrating the modified deep deterministic policy gradient (DDPG) and double deep-Q-network algorithm. Specifically, the lower-level control problem is a mixed-integer stochastic optimization problem with multiple constraints. This kind of problem is hard to be directly solved by the exiting DRL algorithms, since it involves searching for the solution in a vast set of mixed-integer action space, which will induce unbearable computational complexity. Thus, we propose a novel action space reducing approach, embedding the convex optimization tools into the DDPG algorithm, to speed up the lower-level control. Furthermore, simulation results confirm the effectiveness of our proposed intelligent RAN slicing scheme.

Autonomous driving technology is one of the research hotspots in recent years. The development of some basic technologies such as motor sensors, vehicle to everything (V2X) and high-definition map also provides more powerful technical support for autonomous driving. However, it remains a challenge to propose a common decision strategy for autonomous driving. Considering the scenario that the networks can cover various urban roads through road side units (RSUs), this paper proposes a two-satage autonomous driving decision strategy based on environmental awareness and asynchronous advantage actor critic (A3C) algorithm. The first stage proposes a preliminary common model with safety guarantee for all roads; based on which the second stage trains a unique model for each road in consideration of different road features. Simulation results indicate that the proposed strategy can ensure high safety performance and driving efficiency.

Car sharing allows car owners to share their cars to tenants, making the control rights of vehicles to be frequently transferred among individuals. The existing control schemes for car shearing with centralized architecture are faced with several threatens, e.g., the single point of failure and lack of mutual trust. To this end, we propose a decentralized car-sharing control scheme by using blockchain and smart contracts. Massive base stations of Internet-of-Vehicles (IoV) deployed over wide areas are used to jointly build the distributed system with blockchain to replace the untrusted third-party server. Having the smart contract, access control procedures can be performed automatically by an arbitrary base station in the decentralized architecture. The scheme provides a secure platform for the interactions among vehicles, individuals and application providers to avoid some security issues. Several simulations are conducted to validate the feasibility and effectiveness of the proposed scheme.

The Long Range (LoRa) systems usually process all the computing tasks on the LoRa central server remotely, which brings large latency to Internet of Things (IoT) applications. In this paper, we propose a new design of a LoRa system with edge computing at the LoRa gateway. Our design enables that some of the time computing tasks for latency-sensitive applications can be dealt with timely. The implementation details of the LoRa gateway are presented along with functionality of each component. Finally, comprehensive experiments are conducted to evaluate the performance of the proposed system. The results show that the proposed system can decrease the latency of IoT applications and balance the workloads between the LoRa central server and the LoRa gateway.

To efficiently support real-time control applications, networked control systems operating with ultrareliable and low-latency communications (URLLCs) become a fundamental technology for the future Internet of Things (IoT). However, the design of control, sensing, and communications is generally isolated at present. In this article, we investigate the joint optimization of control cost and energy consumption for a centralized wireless networked control system. Specifically, with the “sensing-then-control” protocol, we first develop an optimization framework that jointly takes control, sensing, and communications into account. In this framework, we derive the spectral efficiency, linear quadratic regulator cost, and energy consumption. Then, a novel performance metric called the energy-to-control efficiency (ECE) is proposed for the IoT control system. In addition, we optimize the ECE while guaranteeing the requirements of URLLCs, thereupon a general and complex max-min joint optimization problem is formulated for the IoT control system. To optimally solve the formulated problem by reasonable complexity, we propose two radio resource allocation algorithms. Finally, simulation results show that our proposed algorithms can significantly improve the ECE for the IoT control system with URLLCs.

By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet of Things (IoT) devices can be processed and analyzed at the network edge. However, the MEC system usually only has the limited virtual resources, which are shared and competed by IoT edge applications. Thus, we propose a resource allocation policy for the IoT edge computing system to improve the efficiency of resource utilization. The objective of the proposed policy is to minimize the long-term weighted sum of average completion time of jobs and average number of requested resources. The resource allocation problem in the MEC system is formulated as a Markov decision process (MDP). A deep reinforcement learning approach is applied to solve the problem. We also propose an improved deep Q-network (DQN) algorithm to learn the policy, where multiple replay memories are applied to separately store the experiences with small mutual influence. Simulation results show that the proposed algorithm has a better convergence performance than the original DQN algorithm, and the corresponding policy outperforms the other reference policies by lower completion time with fewer requested resources.

Accurate clock synchronization in the industr-ial-Internet-of-Things systems forms the cornerstone of distributed interaction and coordination among various infrastructures and machines in an industrial environment. However, due to the widespread use of wireless networks in industrial applications, constraints inherent to wireless networks including uncertain propagation delays, random packets losses, and unguaranteed communication resources are unavoidable, leading to dramatically increased clock synchronization error and unreliable or even outdated information. Meanwhile, time information transmissions are vulnerable to suffer from malicious attacks, causing unreliable timestamps and insecure synchronization. In this article, we proposed a distributed clock synchronization protocol based on an intelligent clustering algorithm to achieve accurate, secure, and packet-efficient clock synchronization. The varying rate of skew of every clock is collected and utilized for cluster formation as well as malicious node detection. According to established clusters, various synchronization frequencies are assigned, which can avoid excessive network access contention, reduce overall communication resource consumption, and improve synchronization accuracy. Meanwhile, a two-tier fault detection algorithm consists of outlier detection and second-order regressive model prediction is applied to determine potential malicious nodes. The simulation results demonstrate that the proposed protocol overwhelms simultaneous synchronization protocols in terms of synchronization performance and faulty node detection.

The rapid development of the fifth generation mobile communication systems accelerates the implementation of vehicle-to-everything communications. Compared with the other types of vehicular communications, vehicle-to-vehicle (V2V) communications mainly focus on the exchange of driving safety information with neighboring vehicles, which requires ultra-reliable and low-latency communications (URLLCs). However, the frame size is significantly shortened in V2V URLLCs because of the rigorous latency requirements, and thus the overhead is no longer negligible compared with the payload information from the perspective of size. In this paper, we investigate the frame design and resource allocation for an urban V2V URLLC system in which the uplink cellular resources are reused at the underlay mode. Specifically, we first analyze the lower bounds of performance for V2V pairs and cellular users based on the regular pilot scheme and superimposed pilot scheme. Then, we propose a frame design algorithm and a semi-persistent scheduling algorithm to achieve the optimal frame design and resource allocation with the reasonable complexity. Finally, our simulation results show that the proposed frame design and resource allocation scheme can greatly satisfy the URLLC requirements of V2V pairs and guarantee the communication quality of cellular users.

In this paper, a clustering based resource management scheme for latency and sum rate optimization in V2X networks is proposed to identify distinguished demands for different kinds of vehicular links, namely cellular-vehicle-to-vehicle (C-V2V) links and cellular-vehicle-to-infrastructure (C-V2I) links, and to improve the performance of cellular user in terms of sum rate, latency and throughput for C-V2I links while guaranteeing reliability for every C-V2V connection. To address the fast channel changes due to high mobility, we present a clustering based resource management model to achieve band sharing and efficient power management that depends on large scale fading. Besides, we have also considered the resource management problem of cellular V2X and VANETs users to reduce data transmission impacts. Firstly, the total average sum rate of each C-V2I links is used as an optimization goal to increase the throughput and to reduce latency of the entire C-V2I link. Secondly, cluster based optimum algorithms are proposed which give the optimum resource management. Simulation results reveal that the proposed scheme outperforms the existing scheme.

To improve both efficiency and safety of automatic driving in complex traffic scenarios, autonomous vehicles need to have the ability to predict the future trajectories of vehicles. The interaction among vehicles in real traffic scenarios makes the trajectory prediction of vehicles challenging. By utilizing the historical trajectory of the targeted vehicle and the surrounding environment information, this paper proposes a coupling LSTM model in order to effectively predict the future trajectories of vehicles. The proposed model predicts the observable motion intentions of vehicles, and builds grids for the targeted vehicles to extract the implied space and intention information of the neighboring vehicles. Experiment results verify the proposed model in contrast to other basic models in real traffic scenarios.

Fifth generation wireless networks are expected to support a variety of new application scenarios, such as internet-of-things (IoT) and machine-type communications (MTC), etc. These scenarios have a large number of terminal accesses and occupy a lot of spectrum resources. The existing orthogonal frequency division multiple access (OFDMA) technology cannot support massive connectivity, the severe out-of-band (OOB) power leakage not only waste spectrum resources but also hinders the application of OFDM in the fragmented spectrum. Therefore, in this paper, we combine non-orthogonal multiple access (i.e., SCMA) with new multi-carriers (i.e., GFDM and FBMC) to address the problem of massive connections and spectrum resource shortages in the IoT. Meanwhile, in order to further reduce the interference to the primary system in the fragmented spectrum, an active interference cancellation (AIC)-based OOB power leakage suppression scheme is designed for GFDM. Simulation results show that SCMA-GFDM and SCMA-FBMC/QAM have much better BER performance than SCMA-OFDM in asynchronous uplink communications, and AIC technique can obviously suppress the OOB power leakage of GFDM.

In fifth generation (5G) wireless network, it is well known that coverage enhancement is one of the most important aspects for massive machine type communication (mMTC) and Internet of things (IoT), etc. These application scenarios also introduce repeated transmission mechanism to enhance the coverage. Therefore, in this paper, we propose a new complementary generalized frequency division multiplexing (GFDM) repeated transmission scheme. We first analyze the interference model of conventional GFDM and then propose the conditions to eliminate inter-carrier interference (ICI) according to the interference model. Based on this condition, we have designed a pulse shaping filter corresponding to the conventional GFDM to combat ICI. Next, we analyzed the effect of this method on the signal-to-interference ratio (SIR). Simulation results show that this repeated transmission scheme has better BER performance than the conventional GFDM scheme.

High-definition (HD) map transmission is considered as a key technology for automatic driving, which enables vehicles to obtain the precise road and surrounding environment information for further localization and navigation. Guaranteeing the huge requirement of HD map data, the objective of this paper is to reduce the power consumption of vehicular networks. By leveraging the mobile rule of vehicles, a collaborative vehicle to everything (V2X) transmission scheme is proposed for the HD map transmission. Numerical results indicate that the proposed scheme can satisfy the transmission rate requirement of HD map with low power consumption.

In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multisource fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while the user is driving. A Deep Neural Network (DNN) is trained to extract acoustic features and make a speech classification. Based on the posterior probabilities obtained from DNN, the vehicle information including the speed and direction of vehicle is applied to choose the suitable parameter from a pair of sensitivity values for the KWS system. The experimental results show that the KWS system with the proposed multi-source fusion scheme can achieve better performances in term of precision rate, recall rate, and mean square error compared to the system without it.

Long Range (LoRa) has become one of the most popular Low Power Wide Area (LPWA) technologies, which provides a desirable trade-off among communication range, battery life, and deployment cost. In LoRa networks, several transmission parameters can be allocated to ensure efficient and reliable communication. For example, the configuration of the spreading factor allows tuning the data rate and the transmission distance. However, how to dynamically adjust the setting that minimizes the collision probability while meeting the required communication performance is an open challenge. This paper proposes a novel Data Rate and Channel Control (DRCC) scheme for LoRa networks so as to improve wireless resource utilization and support a massive number of LoRa nodes. The scheme estimates channel conditions based on the short-term Data Extraction Rate (DER), and opportunistically adjusts the spreading factor to adapt the variation of channel conditions. Furthermore, the channel control is carried out to balance the link load of all available channels with the global information of the channel usage, which is able to lower the access collisions under dense deployments. Our experiments demonstrate that the proposed DRCC performs well on improving the reliability and capacity compared with other spreading factor allocation schemes in dense deployment scenarios.

The broadcasting services of vehicles in both vehicular networks and unmanned aerial vehicle (UAV) networks can be seen as the typical applications of all‐to‐all (A2A) scenario. In order to achieve efficient information broadcasting in A2A scenarios, this study proposes a two‐stage resource allocation scheme. In the first stage, to improve the frequency spectrum reusability, the communication area is divided into as many non‐overlapping square regions as possible based on the reliability requirements and power constraints of the vehicles, where the nonadjacent square regions share the same frequency spectrum. In the second stage, the vehicles belonging to the same region broadcast information with distributed time division multiplexing, and an improved coded slotted ALOHA scheme is proposed based on full‐duplex, where each vehicle can detect whether its broadcasted information is successful. Thus, the number of retransmissions and collision probability can be reduced and therefore the system energy can be saved. Results indicate that the two‐stage resource allocation scheme significantly improves the resource reusability while guaranteeing the communication reliability.

Content caching has brought huge potential for the provisioning of non-safety related infotainment services in future vehicular networks. Assisted by multiaccess edge computing, roadside units (RSUs) could become cache-capable and offer fast caching services to moving vehicles for content providers. On the other hand, deep learning makes it possible to accurately estimate the behavior of vehicles, which enables effective proactive caching strategies. However, caching services considering both the mobility of vehicles and storage could incur increased latency and considerable cost due to the cache size needed in RSUs. In this paper, we model such a problem using Markov decision processes, and propose a heuristic Q-learning solution together with vehicle movement predictions based on a long short-term memory network. The optimal caching strategy which minimizes the latency of caching services can be derived by our heuristic εn-greedy training processes. Numerical results demonstrate that our proposed strategy can achieve better performance compared with several baselines under different prediction accuracies.

The Internet of Things (IoT) connects a huge number of resource-constraint IoT devices to the Internet, which generate massive amount of data that can be offloaded to the cloud for computation. As some of the applications may require very low latency, the emerging mobile edge computing (MEC) architecture offers cloud services by deploying MEC servers at the mobile base stations (BSs). The IoT devices can transmit the offloaded data to the BS for computation at the MEC server. Narrowband-IoT (NB-IoT) is a new cellular technology for the transmission of IoT data to the BS. In this paper, we propose a joint computation offloading and multiuser scheduling algorithm in NB-IoT edge computing system that minimizes the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem into an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. In order to deal with the curse-of-dimensionality problem, we use the approximate dynamic programming techniques, i.e., the linear value-function approximation and temporal-difference learning with post-decision state and semi-gradient descent method, to derive a simple algorithm for the solution of the CTMDP model. The proposed algorithm is semi-distributed, where the offloading algorithm is performed locally at the IoT devices, while the scheduling algorithm is auction-based where the IoT devices submit bids to the BS to make the scheduling decision centrally. Simulation results show that the proposed algorithm provides significant performance improvement over the two baseline algorithms and the MUMTO algorithm which is designed based on the deterministic task model.

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this article, we propose a joint computation off-loading and multiuser scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multiuser resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semi distributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. The simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

Vehicular networks enable vehicles to generate and broadcast messages in order to improve traffic safety and efficiency. However, due to the nontrusted environments, it is difficult for vehicles to evaluate the credibilities of received messages. In this paper, we propose a decentralized trust management system in vehicular networks based on blockchain techniques. In this system, vehicles can validate the received messages from neighboring vehicles using Bayesian Inference Model. Based on the validation result, the vehicle will generate a rating for each message source vehicle. With the ratings uploaded from vehicles, roadside units (RSUs) calculate the trust value offsets of involved vehicles and pack these data into a “block.” Then, each RSU will try to add their “blocks” to the trust blockchain which is maintained by all the RSUs. By employing the joint proof-of-work (PoW) and proof-of-stake consensus mechanism, the more total value of offsets (stake) is in the block, the easier RSU can find the nonce for the hash function (PoW). In this way, all RSUs collaboratively maintain an updated, reliable, and consistent trust blockchain. Simulation results reveal that the proposed system is effective and feasible in collecting, calculating, and storing trust values in vehicular networks.

A Sybil attacker is able to obtain more than one identities and disguise as multiple vehicles in order to interfere the normal operations of the connected vehicle system (CVS). In this paper, we propose a novel classifier to detect Sybil attackers according to their mobility behaviors. Specifically, three levels of Sybil attackers are first defined according to their attack abilities. Through analyzing the mobility behaviors of vehicles, a learning-based model is used in the central server (CS) to extract mobility features and distinguish Sybil attackers from benign vehicles. Three classification algorithms are tested and compared, i.e., the naive Bayes, decision tree, and support vector machine. Furthermore, location certificates issued by base stations are used to resist location forgery by attackers. Based on the location certificates, the CS is able to evaluate the credibilities of uploaded locations using the subjective logic theory. In addition, we develop an edge betweenness-based community detection algorithm to handle the collusion among multiple Sybil attackers. Simulations are conducted based on a real-world vehicle trajectory dataset, which indicate that the proposed scheme is effective to resist Sybil attackers in CVS.

In conventional schedule‐based wireless transmission system, frequent interactions between devices and access networks lead to significant signalling overhead, and thus limit the development of low‐latency applications. Contention‐based data transmission (CBDT) is widely used as a promising solution to address the above issues. In this study, the authors propose a detection‐based retransmission scheme for CBDT to further reduce the delay‐outage probability which cannot be avoided due to collision. In this scheme, each device simultaneously monitors the occupied resource blocks during data transmission. When a collision event is detected, the retransmission for this packet is determined without waiting for the acknowledgment message from the receiver. The reliability performance is significantly improved for low‐latency applications, since the possible retransmission times for each packet is increased within the limited transmission period. In addition, the impacts of imperfect detection on extra transmission load and packet loss are analysed in detail. The simulation and analytical results demonstrate that the detection‐based scheme significantly improves the reliability of CBDT.

By leveraging direct device-to-device interaction, LTE vehicle-to-vehicle (V2V) communication becomes a promising solution to meet the stringent requirements of vehicular communication. In this paper, we propose jointly optimizing the radio resource, power allocation, and modulation/coding schemes of the V2V communications, in order to guarantee the latency and reliability requirements of vehicular user equipments (VUEs) while maximizing the information rate of cellular user equipment (CUE). To ensure the solvability of this optimization problem, the packet latency constraint is first transformed into a data rate constraint based on random network analysis by adopting the Poisson distribution model for the packet arrival process of each VUE. Then, utilizing the Lagrange dual decomposition and binary search, a resource management algorithm is proposed to find the optimal solution of joint optimization problem with reasonable complexity. Simulation results show that the proposed radio resource management scheme can reduce the interference from V2V communication to CUEs and ensure the latency and reliability requirements of V2V communication.

The security of vehicular networks has been paid increasing attention to with the rapid development of automobile industry and Internet of Things (IoT). However, existing approaches mainly focus on ensuring data authentication and integrity, which are not sufficient to assess the credibility of received messages. Recently, reputation systems are proved to be effective approaches to solve the above problem. This paper proposes a new reputation system for data credibility assessment based on the blockchain techniques. In this system, vehicles rate the received messages based on observations of traffic environments and pack these ratings into a “block”. Each block is “chained” to the previous one by storing the hash value of the previous block. Then, a temporary center node is elected from vehicles and it is responsible for broadcasting its rating block to others. Based on ratings stored in the blockchain, vehicles are able to calculate the reputation value of the message sender and then evaluate the credibility of the message. Simulation results reveal that the proposed system is reliable in collecting, validating, and storing reputation information in vehicular networks.

Non-orthogonal multiple access (NOMA) is envisioned to be one of the promising radio access techniques for the fifth generation (5G) mobile networks. In this paper, a portable NOMA testbed based on software defined radio (SDR) is developed by transporting our NOMA system to mini personal computers (PCs). Moreover, the NOMA testbed has been enhanced from 5 MHz bandwidth to 10 MHz bandwidth. As the computation complexity grows higher with the increase of system bandwidth, the portable NOMA testbed is not competent to fully demonstrate the performance of our NOMA system due to the limitation of mini PC. For a better understanding of the NOMA testbed, the system architecture and scenario are introduced in brief. Then, the implementation of NOMA transceiver and the protocol stack are described separately. Finally, a series of experiments are carried out to evaluate its performance loss compared with the original NOMA system based on desktops. The experimental results indicate that the performance of processors may be a short slab for the development of portable SDR-based testbeds towards 5G networks.

The control–data separation architecture (CDSA) has been regarded as a promising solution to effectively manage the heterogeneous network (HetNet) in the fifth‐generation mobile communication systems. Thus, this study investigates the resource allocation problems in CDSA‐based HetNet. To meet various requirements of data transmission, the authors first present a new framework with flexible resource management and efficient traffic control. In this framework, the control–data transmission supported by macro cell evolved nodeB is time‐sensitive and continuous with high reliability. On the basis of it, a traffic‐aware resource allocation approach is proposed to improve the resource efficiency. Once the traffic‐congestion requirements of the pattern are satisfied with the minimum spectrum, the throughput of small cells is simultaneously maximised due to orthogonal spectrum between macro and small cells by using this approach. Moreover, the traffic‐congestion analytical result is elaborately derived in terms of current network state. Three adaptive resource allocation schemes for implementing this approach are also designed with different complexities. Numerical results validate the accuracy of the proposed analytical method, and also indicate that the traffic‐aware resource allocation approach is more efficient than the static one.

To improve the energy efficiency of multi‐cell massive multiple‐input–multiple‐output system while guaranteeing the information transmission quality, this study proposes a dual‐layer coordinated beamforming scheme. In the proposed scheme, the beamformer at each evolved node B (eNB) is divided into a cell‐layer beamformer and a user‐layer beamformer. The cell‐layer beamformer is used to mitigate inter‐cell interference (ICI) by exchanging long‐term channel state information (CSI) among eNBs. The user‐layer beamformer serves user according to the local real‐time CSI at each eNB. On the basis of the dual‐layer structure, the cell‐layer beamformers, the user‐layer beamformers, and power allocation are jointly optimised in order to minimise the total transmit power across all the eNBs subject to the signal‐to‐interference‐plus‐noise ratio requirements and single‐antenna power constraints. To make the original problem solvable, the ICI is replaced by its upper bound. Then, the problem is partitioned into two convex sub‐problems, and two iterative algorithms are proposed in order to find the sub‐optimal solution to the original optimisation problem. Simulation results show that the proposed scheme performs better than two reference schemes including the existing zero‐forcing scheme and coordinative multiple point schemes.

Low‐energy critical infrastructure monitoring (LECIM) networks is essential for the monitoring of infrastructure facilities in smart cities. One critical requirement of an LECIM network is its wide coverage of up to several kilometres by using a star topology instead of the tree or mesh networks. In meeting this requirement, this study develops a system with a transceiver of extremely high receiver sensitivity based on the IEEE 802.15.4k physical layer specifications. To reduce the energy consumption, the modulation schemes suitable for low complexity detection are chosen for the data transmission in the design. Also, an efficient parallel preamble and payload data detection are adopted at the access point of the proposed LECIM to acquire concurrent packets from respective nodes. Meanwhile, a data‐aided dynamic timing adjustment scheme is proposed for data field detection to rapidly and adaptively synchronise to the long duration of data packet. Furthermore, a testbed is implemented using a software‐defined radio to demonstrate the effectiveness of the proposed system design.

How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel attribute bloom filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any linear secret-sharing schemes access policy without employing much overhead.

Downlink non-orthogonal multiple access (NOMA), where users are paired as user set and multiplexed in the power domain, is a promising technology for fifth generation (5G) communication system. This paper studies joint optimization of user pairing and power allocation to maximize generalized proportional fair metric subject to transmit power constraints. This problem can be divided into two parts: user pairing and power allocation. In order to reduce the computation burden, we present a pre-defined multi-user pairing criterion to exclude user sets, which are unsuitable for multiplexing. Furthermore, for a given user set, a low complexity multi-user power allocation scheme is proposed by exploiting the convexity of the optimization problem. Simulation results show that the proposed user pairing and power allocation scheme can significantly enhance the downlink system performance and reduce complexity compared to the existing schemes in NOMA systems.

Nowadays, self-driving vehicles which would shoulder the burden of driving and set free human on board are gradually becoming a reality. Consequently, the supporting of growing in-vehicle data traffic will be challenging in future 5G and vehicular networks, due to the high mobility nature of vehicles and the densified irregular distribution on road especially during rush time. Therefore in this paper, a Software-Defined Networking (SDN) enabled integrated 5G-VANET architecture is proposed to improve heterogeneous network (HetNet) management and aggregate vehicle traffic through IEEE 802.11p; a novel vehicle clustering method and dual cluster head design are then introduced to reduce signaling overhead and enhance the overall communication quality in 5G-VANET HetNet under the coordination of SDN. It is also proved by simulation that the proposed design reduced 5G users' blocking probability to the operators services with a back-up cluster head (CH) in each cluster, and also realized adaptive clustering without excessive SDN's processing delay.

Software Defined Radio (SDR) system has great advantages of high flexibility and better reconfigurability in simulating and verifying the new communication technologies. Among all the SDR systems based on general purpose processor (GPP), OpenAirInterface (OAI) is one of the most comprehensive and competitive open-source SDR systems. Due to the design of signal-thread processing in the system, the user equipment (UE) of OAI still cannot process the vast amounts of data when the bandwidth is up to 20 MHz in the real-time experiments. This paper provides an overview of the system architecture in the real-time experimentation of OAI and analyzes its baseband signal processing. Then, the authors of this paper propose and implement a method of multithread parallel processing on the UE of OAI. Over-the-air experiments are carried out with the proposed method for the purpose of performance evaluation in the real- time wireless environment. Results demonstrate that the proposed method enhances the performance of the OAI system and shows its potential for the fifth generation (5G) technology.

The IEEE 802.15.4k standard has defined the phys- ical and multiple media access (MAC) layer for low-energy critical infrastructure monitoring (LECIM) networks, which can be used to monitor infrastructure facilities including industrial metering. The main features of LECIM networks are minimal infrastructure with star topology, long range communication with high receiver sensitivity, very limited energy supplied devices. Based on IEEE 802.15.4k specifications, we have designed and developed the prototypes of end device (ED) and access point (AP) using software defined radio technology. The end device is implemented with an ARM-based MCU and a RF module, while the access point is realized by GNURadio and universal software radio peripheral (USRP). A novel parallel preamble and payload detection is applied at AP to acquire multiple packets from respective ED instead of collision avoidance. Furthermore, the field trails are conducted in urban area to demonstrate and evaluate the effectiveness of testbed design.

Non-orthogonal multiple access (NOMA) is a promising technology for the fifth generation. However, the increasing number of multiplexed users brings two challenges. One is the computational complexity of existing grouping schemes increases rapidly. The other is the performance of multiplexed users deteriorates sharply. To deal with these challenges, we propose a novel multi-user grouping scheme with low-complexity to select several UEs for multiplexing. Moreover, a corresponding power allocation scheme is proposed to guarantee the performance of the multiplexed users. Simulation results show that the novel multi-user grouping scheme can reduce the computational complexity at the cost of user fairness in contrast to the full search method. Besides, the proposed power allocation scheme can improve the spectrum efficiency compared with the existing power allocation schemes in NOMA systems.

We consider a downlink massive MIMO system, where the base station simultaneously sends information and energy to information users and energy users, respectively. The aim is to maximize the minimum harvested energy among the energy users while meeting the rate requirements of information users. With perfect channel state information (CSI), the problem is solved by obtaining the asymptotically optimal power allocation of information users and the combination coefficients of the energy precoder. For the CSI estimation in time-division duplex systems, orthogonal pilot sequences are employed by information users during the uplink, and one common pilot sequence is shared by all energy users. It is shown that the energy-harvesting performance of such a shared pilot scheme is always better than that of the orthogonal pilot scheme. Further, exploiting the intercell interference in multicell systems, a joint precoder is proposed for cooperative energy transfer, for which both the centralized and distributed implementations are given. Results indicate that the cooperative energy transfer always outperforms the noncooperative scheme with either perfect or estimated CSI.

With the increase of wireless communication demands, licensed spectrum for long term evolution (LTE) is no longer enough. The research effort has focused on implementing LTE to unlicensed frequency bands in recent years, which unavoidably brings the problem of how to make LTE co-exist with other existing systems on the same band. This paper proposes an adaptive co- existence mechanism for LTE and wireless local area networks (WLAN) to enable a significant system performance of WLAN while LTE does not lose much as well. LTE realizes the co-existence by allocating time resources dynamically according to the traffic load of WLAN system.

Compact structure of the large-scale antenna array and low power angular spread (PAS) in vertical dimension improve the fading correlation of wireless channel dramatically in full-dimension multiple input multiple output (FD-MIMO) systems. Whereby beamforming (BF) can fully exploit the quasi-static spatial correlation to reduce dependence on the instantaneous channel state information at the transmitter (CSIT) and simplify the complexity of signal process, but also incur a loss of system performance. In this paper, we analyze the potential of exploiting spatial correlation in FD-MIMO systems, and propose an interesting spatial-correlation-based partial-channel-aware beamforming (SP-BF) scheme. Through the rationale of Givens rotation, SP-BF leverages the CSIT of partial channels, i.e., reduced-dimension channel vector, to further boost the system performance achieved by exploiting the spatial correlation. By applying this scheme, FD-MIMO in frequency division duplex (FDD) systems, where no channel reciprocity could be exploited, can make a tradeoff between the system performance and the overhead of acquiring CSIT. In addition, we validate the proposed scheme by virtue of the Monte Carlo simulations.

In this paper, an algorithm for energy efficiency optimization is designed in a point-to-point narrowband Multiple-Input Single-Output (MISO) system with Simultaneous Wireless Information and Power Transfer (SWIPT). Power splitting architecture is assumed for the receiver, which would split the received Radio Frequency (RF) signals into two independent streams for information decoding and energy harvesting, respectively. Taking both the Quality of Service (QoS) requirement and the constraint of harvested power requested into consideration, the energy efficiency optimization problem is formulated as a two-dimension non-convex problem. Nonlinear fractional programming, one-dimension searching and convex optimization are involved to develop the “EE-Max algorithm” to resolve the problem. The channel capacity optimization problem and the corresponding algorithm, “Capacity-Max algorithm”, are also studied for comparison. Numerical results demonstrate the excellent performance of energy efficiency for the EE-Max algorithm and reveal the influence of the searching interval, channel fading, the maximum transmission power and the QoS requirement.

In this paper, a quality-of-experience (QoE)-based user scheduling scheme for delay-sensitive mobile cloud services (MCS) is proposed. The proposed scheme aims at optimizing the user QoE, which is mainly determined by both the application-level and network-level quality of services. Packet delay, as an essential factor affecting QoE, is discussed under the context of QoE optimization. The optimization problem is modeled as an infinite- horizon average cost Markov Decision Process (MDP), based on both the dynamics of channel state information (CSI) and queue state information (QSI). In order to reduce the exponential memory requirement and computational complexity, a distributed stochastic learning algorithm which only requires local CSI and QSI is introduced. Simulation results show that the proposed scheme can achieve significant improvement in QoE over conventional schemes.

This study comprehensively investigates the performance of the downlink massive multiple‐input multiple‐output (MIMO) system with channel state information (CSI) delay, when the base station serves multiple user terminals (UTs) taking advantage of maximum ratio transmission precoding. Through the characteristic analysis of the received signal‐to‐interference‐plus‐noise ratio at UT, the tight lower bound of average area spectrum efficiency (SE) and its asymptotic bound with the infinite number of UTs are deduced, respectively. Based on the asymptotic bound and the realistic power consumption model consisting of both radiated power and circuit power, the trade‐off between SE and energy efficiency (EE) is developed, and the optimal EE with respect to SE is attained concisely. Moreover, the approximate expressions of both outage probability and bit error ratio (BER) are derived when the massive MIMO system works at its interference limited region. All theoretical results coincide to numerical results, and they show that deploying more transmission antennas will be helpful to improve SE, EE and reliability simultaneously; SE will be enhanced; however, the reliability, including both outage probability and BER, will be worsened by multiplexing more UTs; enlarging CSI delay can extensively deteriorate SE, EE and reliability.

Carrier aggregation has been accepted as a means of bandwidth extension in the third generation long‐term evolution‐advanced (LTE‐advanced) network, in an effort to support high data rate transmission with backwards compatibility. Since there are two or more component carriers (CCs) to be aggregated, it is crucial to design efficient carrier scheduling schemes. In this study, the authors propose a novel dynamic aggregation carrier (DAC) scheme for downlink transmission, which enables CCs to aggregate with each other in a dynamic manner. The dynamic nature of the new scheme allows the total capacity of all CCs to be fully utilised to serve flows, whereas the number of aggregated supplementary CCs is decreased so as to lower the computational complexity at user equipment (UE). Furthermore, the performances of the new scheme and two other carrier scheduling schemes are evaluated thoroughly through both analytical and simulation results. It is demonstrated that the DAC scheme offers good performances in terms of delay and throughput while reducing energy consumption and the signalling overhead at UEs.

Energy efficiency is of vital importance for telecommunications equipment in future networks, especially battery-constrained mobile devices. In the long term evolution-Advanced (LTE-Advanced) network, the carrier aggregation (CA) technique is employed to allow user equipment (UE) to use multiple carriers for high data rate communications. However, multi-carrier transmission entails increased power consumption at user devices in uplink networks. In this paper, we propose a new dynamic carrier aggregation (DCA) scheduling scheme to improve the energy efficiency of uplink communications. Two scheduling methods, i.e., serving the longest queue (SLQ) and round-robin with priority (RRP), are designed to reduce transmit power while maximizing the utilization of wireless resources. The proposed scheme is analyzed in terms of both the data rate and energy conservation. We build an ideally balanced system (IBS) to investigate the performance upper bound of the DCA scheme, and derive closed-form expressions. Simulation results demonstrate that the proposed scheme can not only enhance the energy efficiency but also perform closely to the optimal IBS.

Massive multiple-input multiple-output (MIMO) has been seen as a promising technology to improve the spectrum efficiency (SE), reliability and energy efficiency (EE) for the next generation wireless communication systems. Excessive energy consumption of wireless communication networks induces both the increasing carbon emission and unaffordable operational expenditure in recent years. In this paper, energy efficient power allocation scheme is investigated for the massive MIMO system with the maximum ratio transmission (MRT) precoding, since MRT precoding can balance the system performance and complexity. As of the intractable expression of the received SINR at user terminal (UT), an approximate expression is deduced by proper simplification. Based on the simplified expression, a power allocation algorithm is proposed to achieve the optimal EE according to convex optimization theory. Compared with the power allocation scheme ignoring the inter user interference, the proposed power allocation algorithm can enhance EE and decrease transmission power, and does not impair the SE. Simulation results also show that both the EE and SE are improved by increasing the number of antennas at BS and the number of multiple UTs.

Green communications is put forward to decrease the operational expenditure (OpEx) and carbon emission of communication networks in recent years. The relationship between spectrum efficiency (SE) and energy efficiency (EE) has induced more attentions in academic field, however, most efforts are spent on SE-EE trade-off of link-level not system-level. In this paper, we analyze the trade-off between average area SE (A2SE) and EE under the realistic power consumption model (PCM) in system-level perspective. First, we give the theoretical closed-form expression of A2SE for a cell as a function of edge SNR. And then, closed-form approximation (CFA) of A2SE is derived, and it largely coincides with theoretical A2SE and is reversible. Based on the CFA, we find the closed-form trade-off of A2SEEE concisely. Further, the optimal EE, corresponding A2SE and transmission power of BS under different cell radiuses are got according to the A2SE-EE trade-off. These results are helpful to design cell radius and transmission power which meet different needs for A2SE and EE of wireless networks.

In this paper, the energy efficiency performance of device-to-device (D2D) communications in orthogonal frequency division multiplexing cellular networks is studied. Different from previous work that was based on the static interference model, we assume a dynamic number of competing flows, such as continuous transfers of file transport protocol or web browsing sessions. The complex, dynamic interaction of the amount of backlogged traffic at the D2D and cellular links introduced by the strong impact of interference between them is captured by the coupled-processors server in the formulated model. The energy efficiency and spectral efficiency of the different resource-sharing strategies are analyzed using the semidefinite optimization approach. It is shown that the simulation results match well with the analytical bounds under different traffic loads and topologies.

With the advent of small‐cell networks (SCNs) to support growing wireless data volumes and thus reduced cell sizes, cooperative communications are significantly facilitated. Applicable to Third Generation Partnership Project long‐term evolution‐A, the authors propose a novel channel/queue‐aware user pairing and scheduling scheme in a cooperative virtual multiple‐input–multiple‐output (VMIMO) system. The queueing performance of the VMIMO system with the scheduling scheme is analysed based on the finite‐state Markov model (FSMM), and compared with that of non‐cooperative systems. Bounds on the average queuing delay of users are derived by using a semi‐definite programming (SDP) approach. The presented analyses are validated through comparing the analytical and simulation results. It is found that the introduced VMIMO pairing process is able to significantly reduce service delays, bringing on a positive impact of cooperative techniques on next generation wireless systems.

Wireless networks are expected to support a diverse range of quality of service requirements and traffic characteristics. This paper undertakes stochastic performance analysis of a wireless finite-state Markov channel (FSMC) by using stochastic network calculus. Particularly, delay and backlog upper bounds are derived directly based on the analytical principle behind stochastic network calculus. Both the single user and multi-user cases are considered. For the multi-user case, two channel sharing methods among eligible users are studied, i.e., the even sharing and exclusive use methods. In the former, the channel service rate is evenly divided among eligible users, whereas in the latter, it is exclusively used by a user randomly selected from the eligible users. When studying the exclusive use method, the problem that the state space increases exponentially with the user number is addressed using a novel approach. The essential idea of this approach is to construct a new Markov modulation process from the channel state process. In the new process, the multi-user effect is equivalently manifested by its transition and steady-state probabilities, and the state space size remains unchanged even with the increase of the user number. This significantly reduces the complexity in computing the derived backlog and delay bounds. The presented analysis is validated through comparison between analytical and simulation results.

With the tremendous growth in wireless networks market, unprecedented energy consumption and emissions of carbon dioxide (CO2) is a growing concern for both operators and governments. OFDM technology has been adopted by many standards organizations due to its high spectrum efficiency, but induces poor energy efficiency of power amplifier (PA). A new scheme named Selecting Sub-Carriers Modulation (SSCM) to lower the input power of PA is given in this paper. SSCM divides the information bits into two parts of bits according to modulation mode: the first part of bits selects some sub-carriers of OFDM to transmit nothing, another part of bits is transmitted by the remainder sub-carriers. Sub-carriers transmitting nothing do not need energy, and fewer number of sub-carriers transmitting modulation symbols can improve PA's efficiency because of the lower peak-to-average power ratio (PAPR). Analysis results show SSCM with different modulation modes can lower input power of PA with different proportion compared with traditional OFDM. Theoretical analysis and simulation results of the reliability of SSCM are given and match closely. In multi-path channel, SSCM with BPSK has the same performance as BPSK in traditional OFDM, while SSCM with QPSK performs worse than QPSK in traditional OFDM.

Carrier aggregation is one of the key techniques for the advancement of long-term evolution (LTE- Advanced) networks. This article proposes a quality- of-experience (QoE)-based carrier scheduling scheme for networks with multiple services. The proposed scheme aims at maximizing the user QoE, which is determined by both the application-level and network-level quality of services. Packet delay, as an essential factor affecting QoE, is first discussed under the context of QoE optimization as well as the data rate. The component carriers are dynamically scheduled according to the network traffic load by the proposed novel scheme. Simulation results show that our approach can achieve significant improvement in QoE and fairness over conventional approaches.

Recently, distributed resource allocation for relay enhanced cellular networks has gained increasing attention. However, it poses a new challenge for the backhaul link scheduling. In this paper, we propose a queue-status-based resource allocation scheme for the backhaul link in a distributed manner in relay enhanced networks. By applying this scheme, the evolved Node B (eNB) adjusts the data rate for each user equipment (UE) in the backhaul link according to its queue status at the relay node (RN). It can guarantee the rate match between the backhaul and access links while minimizing feedback signaling. Simulation results show that the proposed scheme can achieve a good tradeoff between the spectral efficiency and the signaling overhead.

Due to the dense and self-deployment of home eNodeBs (HeNBs) in femtocells, serious inter-cell interference may arise without good coordination. To deal with it, a distributed interference coordination scheme for femtocells networks with carrier aggregation (CA) is proposed in this paper. Firstly, each femtocell operates on all the component carriers (CCs) of the network and gathers the information needed via local measurements. Then, based on the measurements, decisions of maintaining or releasing the component carrier are individually made by each femtocell on each component carrier. Simulation results validate the effectiveness of our proposed scheme in term of the signal to interference and noise ratio (SINR) and throughput.

Femtocell is a promising cost-effective technology to provide the hotspot coverage especially in buildings. Due to the high density of femtocells in urban environment, the need to reuse the scarce spectrum results in co-channel interference. In this paper, we propose a graph-based distributed scheme to manage radio resources efficiently among femtocells, achieving a good tradeoff between system throughput and user fairness. After the interference graph is generated, each femtocell first uses certain number of resources randomly. Then, femtocells may apply for more resources with the given probability. Our simulation results demonstrate the effectiveness of the proposed scheme in terms of throughput and fairness.

Carrier aggregation (CA) is one of the promising techniques for the further advancements of the third-generation (3G) long-term evolution (LTE) system, referred to as LTE-Advanced. When CA is applied, a well-designed carrier scheduling (CS) scheme is essential to the LTE-Advanced system. Joint user scheduling (JUS) and separated random user scheduling (SRUS) are two straightforward CS schemes. JUS is optimal in performance but with very high complexity, whereas SRUS is contrary. Consequently, the authors propose a novel CS scheme, termed as ‘separated burst-level scheduling’ (SBLS). In SBLS, the connected component carrier (CC) of one user can be changed in burst level, whereas in SRUS, it is fixed. Meanwhile, SBLS limits the users to receive from only one of the CCs simultaneously, which is the same as that in SRUS. In this way, SBLS is expected to achieve higher resource utilisation than SRUS but with acceptable complexity increase. There are two factors that are important to the performance of SBLS, namely the dispatching granularity and the dispatching policy. The authors' analysis is verified by system-level simulations. The simulation results also show that the resultant performance gain of SBLS over SRUS is notable and increasing dispatching granularity will quickly deteriorate the performance of SBLS.

The theoretical analysis on quality-of-service (QoS) performances is required to provide the guides for the developments of the next-generation wireless networks. As a good analysis tool, the probabilistic network calculus with moment generating functions (MGFs) recently can be used for delay and backlog performance measures in wireless networks. Different from the existed studies which mostly focused on the single-hop networks with single-user under a two state Markov channel model, this study develops an analytical framework for wireless multi-hop relaying networks under the finite-state Markov channel by using probabilistic network calculus with MGFs. By using the concatenation character of network calculus, the authors regard a two-hop wireless relaying channel as a single server equivalently, which consisting of two dynamic servers in series. When the single-user model is straightforwardly extended and applied in multi-user scenarios, the state space of service process is increased exponentially with the number of users, which is only applicable in case of very small user number. Then, in order to avoid the limitation of user number, the authors propose to reflect the multi-user effects by using the equivalent data rate of the modified service process, whose transition and stationary probabilities are kept unchanged with those in single-user scenarios. Next, delay and backlog bounds of multi-hop wireless relaying networks are derived with the proposed analytical framework. Simulation results show that analytical bounds match simulation results, whose accuracy depends on the required violation probability. The effectiveness of the relaying techniques in improving the performances is also demonstrated.

Femtocell technology has gained widespread attention recently due to its advantages, such as infrastructure cost reduction, improved service coverage and high data throughput in indoor environments. As femtocell networks are customer-deployed without proper network planning, their interference environment tends to be much more complicated than traditional cellular networks. The authors present the framework of channel allocation in orthogonal frequency-division multiplexing access (OFDMA) femtocell network with the graphical approaches. A novel graph-based interference coordination scheme is proposed to maximise the system throughput while ensuring proportional rate fairness among femtocells. The scheme explicitly uses the received signal-to-interference-plus-noise-ratio to generate the interference graph of OFDMA femtocell networks, so as to guarantee the acceptable inter-cell interference for all the links. First, all the femtocells are partitioned into different groups by applying a greedy graph colouring algorithm to maximise the sum throughput of each group. The femtocells in the same group share the assigned subchannels while those in different groups are allocated to orthogonal subchannels. Then, an optimisation problem is formulated to determine the number of subchannels assigned to each group. Further, an approximation method is proposed to solve the optimisation problem. Simulation results are conducted to demonstrate the effectiveness of the proposed scheme in terms of throughput and fairness index.

There has emerged lots of interests in the resource allocation of the relay-enhanced orthogonal frequency division multiplexing (OFDM) system. Most of the existing research works are with the aim to maximise the transmit rate of the system. However, few of them have taken into account the fairness requirements. In order to achieve the trade-off between the system transmit rate and user fairness, this study originally studies the optimality of proportional fairness (PF) in a downlink relay-enhanced OFDM system, where one source communicates with multiple destinations by the aid of one or many relays. We investigate the joint subcarrier and power allocation problem with PF constraint. Firstly, the resource allocation problem is formulated. Then, the problem is studied in the framework of concave maximisation. By using mathematical decomposition techniques, the optimisation problem is decomposed into multiple-layer subproblems which can be resolved efficiently. With this method, a cross-layer algorithm is derived to achieve the near optimal solution. Finally, numerical results are illustrated. Compared to the existing schemes, our proposed algorithm can both maximise the sum of logarithmic per user average transmit rate and enhance the system transmit rate.

The QR decomposition (QRD)-based relaying protocol can achieve both the distributed array gain and the intra-node array gain in multi-input multi-output multi-relay systems. In this paper, the distributed precoding technique is first utilised in the QRD-based multi-relay system to further improve the capacity performance of the system. In order to find the optimum distributed precoder, a novel version of the particle swarm optimisation (PSO) algorithm is presented by redefining the evolution of particles on the spherical surface. Then, the complexity analysis of the proposed PSO algorithm is carried out to show its feasibility. Finally, numerical and simulation results demonstrate the performance gain of the proposed design in terms of the ergodic capacity.

Multihop relaying is one of the promising techniques in future generation wireless networks. The adaptive modulation and coding (AMC) mechanisms can be applied in order to increase the spectral efficiency of wireless multihop networks. However, most of these mechanisms concentrate on the physical layer without taking the queuing effects at the data link layer into account, whose performances are overestimated. Therefore the cross-layer analytical framework is presented in analysing the quality-of-service (QoS) performances of the decode-and-forward (DF) relaying wireless networks, where the AMC is employed at the physical layer under the conditions of unsaturated traffic and finite-length queue at the data link layer. Considering the characteristics of DF relaying protocol at the physical layer, the authors first propose modelling a two-hop DF relaying wireless channel with AMC as an equivalent Finite State Markov Chain (FSMC) in queuing analysis. Then, the performances in terms of queuing delay, packet loss rate and average throughput are derived. The numerical results show that the proposed analytical method can be efficiently applied for studying the issues including the relay deployment and the cross-layer design in the multihop relaying networks.

In multi-input-multi-output relay systems with amplify-and-forward protocols, the source-relay and the relay-destination links can be treated as two groups of spatial channels. In this letter, an adaptive spatial channel mapping matrix is presented to be used between the two groups. A unified framework of the spatial channel mapping matrix design is presented, which includes various relay protocols. The spatial channel matrix is designed to reduce the relaying noise power or the local noise in the destination node. Simulation results demonstrate the advantage of the adaptive spatial channel mapping on the system reliability and capacity with different relay protocols.

In wireless sensor networks, several relay nodes can be used to support multiple transmission links between source and destination nodes simultaneously to achieve higher spectral efficiency. A channel state information sharing scheme among multiple relay nodes is presented in this letter, where only one angle per source-destination pair is sent from one relay node to another. Then, to enhance received signal powers, a distributed multiuser precoding scheme is presented to be used at each relay node with the angles sent by another relay node. Simulation results demonstrate that the proposed distributed precoding scheme enhances the capacity performance with slight overhead.

QR decomposition (QRD) based relay protocols can achieve both the distributed array gain and the intra-node array gain in multi-relay systems. Because the QRD is not unique if column permutation is utilized, the traditional QRD based relay protocol can be further improved. Row permutation of the forward channel and column permutation of the backward channel are proposed in this paper. Exhaustive and iterative search algorithms are presented to solve the permutation optimization problem. The iterative search algorithm with modified QRD is a good trade-off between capacity performance and complexity.

In this paper, a novel observe-and-forward (OF) cooperative scheme is proposed for relay-enhanced wireless cooperative systems. In this scheme, the codewords transmitted from the source are regenerated by the decoder at the relay and forwarded without re-encoding or interleaving. The decoding processes for different streams of a transmitted codeword can be parallel, which greatly reduces the relaying latency and the complexity of the relay. Furthermore, classical Viterbi algorithm (VA) decoding is adopted at the destination while a Turbo decoding is necessary in decoded-and-forward with coded cooperation (DF-CC) scheme. Union bounds on error performances and the achievable rates are analyzed for the OF scheme. Both the analysis and simulation show that the proposed OF scheme works as well as the classical DF scheme and even better than the DF-CC scheme in some scenarios. The features of the proposed OF scheme are smaller relaying latency and lower complexity, which facilitate the application of this relaying scheme.

This paper considers subcarrier allocation for the multihop orthogonal frequency division multiple-access (OFDMA) broadcast networks consisting of one source, multiple destinations, and one amplify-and-forward (AF) relay. In this paper, proportional fair (PF) based subcarrier allocation is discussed to get a tradeoff between the system transmission rate and fairness. First, the problem is formulated as an optimization problem with prohibitive complexity. Second, by analyzing the optimal solution for the subcarrier allocation problem without PF constraint, two suboptimal schemes are proposed. The simulation results indicate that with the same fairness performance, the proposed schemes achieve considerable capacity gain compared with the conventional PF scheduling method which is extended simply from the single-hop system.

In this paper, we consider the optimal resource allocation problem for dual-hop systems with a source-destination pair and multiple decode-and-forward (DF) relays. Orthogonal frequency division multiplexing (OFDM) is adopted in both hops. To maximize the system capacity, we formulate a mixed binary integer programming problem to optimally allocate the subchannels and transmit power for both the source and the relay nodes. Because of the prohibitive complexity, a greedy heuristic algorithm is proposed to decompose the original problem into solvable subproblems. Simulation results demonstrate that the proposed algorithm has a near-optimal performance.

Due to similarity between cooperative systems and traditional multi-input-multi-output (MIMO) systems, precoding techniques can be introduced into MIMO relay systems. In this paper, a practical distributed precoding scheme with limited feedback scheme is proposed in the MIMO relay system, where the zero-forcing (ZF) relaying protocol is proposed to be used in the relay node so that the information of relaying channel and noise can be compressed into two positive real coefficients. Precoding is proposed to be operated through two continuous transmitted vectors of the source node, which is termed as distributed spatial-temporal precoding (DSTP). Power allocation is combined with the proposed DSTP for higher spectrum efficiency. DSTP outperforms the direct transmission and the simple ZF relaying protocol. Analysis and numerical results demonstrate that DSTP with suitable receiver performs very close to the capacity low bound of the ZF relaying system.

The incremental relaying (IR) protocol outperforms simple amplify-and-forward and decode-and-forward protocols due to usage of feedback. Based on the IR protocol, a novel protocol termed as fractional incremental relaying (FIR) is firstly presented in this paper to achieve higher spectral efficiency. When a packet of data needs to be transmitted by the relay node, symbols are transmitted a few at a time as needed instead of the entire packet. Then, analysis result shows that the proposed protocol makes more efficient use of channel freedom degrees than the traditional IR protocol does. Finally, numerical results under independent Rayleigh fading demonstrate the advantage of the proposed FIR protocol.

In this paper, we investigate the linear processing at either base station or relay station in cellular network with a non-regenerative MIMO relay assisting. The purpose of linear processing is to equalize the two-hop fading channel in order to support multiuser transmission in downlink and uplink. Under the transmit power constraint at each station, the closed-form expressions of the suboptimal MMSE processing matrix in downlink and the optimal MMSE processing matrix in uplink are derived when the linear processing is performed at either base station or relay station. Simulation results demonstrate that the proposed MMSE strategies significantly outperform the corresponding ZF strategies in terms of BER.

This paper investigates the spatial resource allocation algorithms for the uplink Virtual-MIMO (Multiple Input and Multiple Output) systems, where K client users (each with one or multiple antennas) are served by one multiple-antenna BS (Base Station). To fully exploit the spatial multi-user diversity gain, the finding of the optimal spatial co-channel users is discussed, which is formulated as a mixed binary integer programming problem. Based on the property of hermitian matrix, simulated annealing is used to recursively find the suboptimal spatial co-channel user group. By simulation results, our proposed strategy has almost the same system throughput performance as the optimal greedy algorithm, and can maintain lower computation complexity.

A two-hop relaying system where a source communicates with its destination through multiple relay nodes is studied for capacity maximization. Amplify-and-forward (AF) relaying is adopted for its simplicity. Taking the independent channel transfer function of different subcarriers and links into consideration, we formulate a resource allocation optimization problem for joint subcarrier assignment, subcarrier matching and power allocation of the two-hop transmission. A heuristic suboptimal algorithm which decomposes the joint problem is proposed. First, equal power allocation is assumed in a partial- update manner to help assigning subcarriers to each relay for matching; then optimal power allocation is achieved by joint waterfilling of source and relays under separate power constraints to further increase system capacity. Numerical results show that the proposed algorithm has a near-optimal performance.

In this paper, influence of spatial correlation on codebook-based precoding technology is analyzed. Optimum pre- coder distribution under spatial correlated channel is achieved at first, which is no longer isotropical on the unitary matrix (vector) set. Selection probability of the matrix (vector) in codebook is not uniform too. Then, a novel codebook design is presented for spatial correlated channel, which is termed as nonuniform codebook. Codebook is designed as function of spatial correlation characteristic of channel and presents a nonuniform quantization of unitary vector set. Smaller quantization error and higher spectral efficiency can be achieved by proposed codebook design than that of traditional fixed uniform codebook design, with the cost of slight feedback overhead increasing.

In the multiuser spatial multiplexing multiple-input multiple-output (MIMO) system, the joint transmitter-receiver (Tx-Rx) design is investigated to minimize the weighted sum power under the post-processing signal-to-interference-and-noise rate (post-SINR) constraints for all subchannels. Firstly, we show that the uplink-downlink duality is equivalent to the Lagrangian duality in the optimization problems. Then, an iterative algorithm for the joint Tx-Rx design is proposed according to the above result. Simulation results show that the algorithm can not only satisfy the post-SINR constraints, but also easily adjust the power distribution among the users by changing the weights accordingly. So that the transmitting power to the edge users in a cell can be decreased effectively to alleviate the adjacent cell interference without performance penalty.

In order to achieve the improved diversity over the original coded cooperation in fast fading channel, the novel cooperation strategy with hierarchical modulation is proposed in this paper, where users send both their own as well as their partners' parity bits with hierarchical error protection during the cooperation frame. Only the frame-level synchronization is necessary when using the proposed strategy, which facilitates the implementation on the uplink. The bounds for the block-error rate (BLER) of the proposed strategy are analyzed and the simulation results given.

In current wireless system performance evaluation, separate link and system level simulators are needed due to less computation complexity. The link-to-system (L2S) interface method to interconnect two simulators is essential and need careful definition. Exponential effective signal-to-interference-and-noise ratio mapping (EESM) is a typical L2S interface method suitable for evaluation of orthogonal frequency division multiplexing systems. When itpsilas used in simulation, all the sub-carriers for one user have to use the same modulation and coding scheme. In this paper, two modified L2S interface EESM methods are presented based on the idea that a high-level modulation symbol could be equivalently converted to several low-level modulation symbols with different reliability. As a result, usage of EESM can be extended to the situation that sub-carriers for one user use different modulation schemes.

Hybrid automatic-repeat-request(HARQ) scheme can be incorporated with the linear precoder in multi-input multi- output(MIMO) transmission to ensure highly reliable communications. To fully utilize type-I HARQ diversity gain especially in the slow fading channels, we propose the optimal design principle of linear precoders, whose column vectors are orthogonal to each other correspondingly. Simulation results demonstrate the effectiveness of these precoders in reducing the detection bit-error rate and increasing the unified throughput.

The time-variation of channels during one OFDM symbol destroys the orthogonality among subcarriers and results in intercarrier interference (ICI), which may detail substantial error performance. Assumed channels vary in linear fashion during one OFDM symbol, we propose an iterative partial-ICI-cancellation (PIC) based detector for OFDM systems with linear modulation over doubly-selective Rayleigh fading channels. The tentatively detected information of channel and data are fed back to regenerate the ICI and then partially subtracted from the received signals. The optimal partial ICI cancellation factor (PICF) is determined by minimizing the mean square error (MSE) of data estimation. The proposed detector iteratively renews the channel estimation and data detection after PIC, and results in performance enhancement. Based on Jake's model for the Doppler effects, the bit error ratio (BER) performance of the proposed detector is illustrated for OFDM systems with phase-shift keying (PSK) and quadrature-amplitude modulation (QAM) in Rayleigh fading channels at several speeds.

In orthogonal frequency division multiplexing (OFDM) systems, conventional channel estimation techniques using comb- type preambles and interpolation between pilot subcarriers give relatively large mean square errors (MSEs) at the edge sub- carriers. Furthermore, the spacing between pilot subcarriers in the frequency domain has to be close enough according to sampling theorem. To solve these problems, an iterative discrete Fourier transform (DFT)-based channel estimation with comb-type pilot-aided is proposed in this paper. The complete channel frequency responses (CFRs) including virtual subcarrier positions are estimated by recursive method with the small size of DFT/Inverse DFT. Simulation results show that the proposed algorithm outperforms the conventional linear minimum mean square error(LMMSE) algorithm using comb-type preamble.

Cyclic delay diversity (CDD) is an attractive approach to achieve spatial and multipath diversity. Its simplicity and conformability with current standards makes it desirable for orthogonal frequency division multiplexing (OFDM) systems. In this paper, we propose the transmission diversity scheme with hierarchical modulation for the broadcast service in the mobile communication system, where CDD is applied between the different cells and space-time block coding (STBC) or CDD is used inside the cell. A hierarchical modulation scheme is adopted in order to provide the layered transmission in the broadcast area with high frequency efficiency. Computer simulation results show that the proposed transmit diversity technique increase the diversity order greatly. Furthermore, this technique with hierarchical modulation scheme outperforms that with the common modulation scheme when different prioritized streams are transmitted.

This paper represents a method based on generalizing Delbrouck's algorithm for calculating the access capacities of multi-service time division duplex-code division multiple access (TDD-CDMA) networks. The method models the system as a multi-service model with state dependent soft blocking probabilities, and it uses the soft capacity limit caused by TDD-CDMA system in calculating the call blocking rate of multi-services. The traffic models used in this method are insensitive to the service time distribution and thus the method is very robust for applications. So it is more accurate than multi-dimensional Erlang-B formula, which does not consider the soft capacity, and it is also simpler than resolving the complex linear system equations with soft capacity. The paper provides both analytical results and simulation results to support the method, and these results match. Thus the proposed method can be used in TDD-CDMA network dimensioning and optimizing.

Software radio is the promising technology that allows the different wireless standards easily be converged. Using general purpose processors and open-source operating systems instead of dedicated hardware and software to build the wireless communication system is very flexible and low-cost. In this article,the open-source platform based on common PCs is described, which allows rapid development and verification of software radio systems. We also discuss the efficient distributed strategies essential for this platform. Finally, the demonstration system of TD-SCDMA is developed and the conclusion given

In hierarchical networks, the topology and QoS parameters of a domain have to be first aggregated before being propagated to other domains. However, topology aggregation may distort useful information. This paper focuses on minimizing the distortion caused by reducing a full-mesh representation to a spanning tree. An optimization method of minimizing the distortion of additive parameters caused by spanning tree aggregation is presented. Based on this new method, two approximation algorithms are proposed. Simulation results show that both algorithms perform much better than the traditional way of decoding the spanning tree with upper or lower bounds.

Code division multiplex access with cyclic prefix (CP-CDMA) is regarded as one of the best candidates for the broadband wireless communication systems in the uplink. This paper proposes a selective parallel interference cancellation (SPIC) for uplink CP-CDMA. With less complexity and good resistance to near-far effect, the S-PIC can achieve better bit error rate(BER) performance than the conventional interference cancellation. Computer simulation demonstrates its effectiveness and conclusion is followed.

OFDM combined with CDMA has emerged as an attractive technique due to its low equalization complexity and good performance in multipath fading channels due to frequency Rake receiver. Using the transmitter diversity scheme and layered space-time (LST) architecture with OFDM-CDMA, high spectral efficiency and good error rate performance can be achieved at the same time. In this paper, the transceiver structure in OFDM-CDMA with multiplexed space-time block codes (STBC) is proposed, in which the number of receive antennas can be less than that of transmit antennas. Computer simulation demonstrates effectiveness of this detector and conclusion is followed

This paper proposes a novel detection algorithm for multiple-input multiple-out (MIMO) code division multiplex-orthogonal frequency division multiplex access (CDM-OFDMA) system in the uplink. Vertical Bell Laboratories Layered Space-Time (V-BLAST) detection is processed only after despreading/combining at the receiver, which not only achieves good frequency diversity gain but also has low implementation complexity. Parallel interference cancellation (PIC) algorithm can also be applied to improve the performance of systems with heavy loads. Computer simulation demonstrates effectiveness of this detector and conclusion is followed.

OFDM-CDMA systems have been regarded as the most promising candidates for future mobile communication systems. This paper explores a novel OFDM-CDMA system with joint time-frequency spreading method proposed. The average bit error probability of this system using maximum-ratio combining (MRC) is derived in a frequency-selective fading channel. Numerical analysis and simulation results indicate in detail that the proposed system outperforms the conventional MC-CDMA system.

Multi-carrier code division multiplex access (MC-CDMA) is one of the best candidates for the future broadband wireless multimedia communication systems. This work investigates the influence of channel estimation on the performance in MC-CDMA systems and the optimum pilot-to-data power ratio (PDR) is derived. Numerical results are presented and our conclusion is followed.