Quantum optimization is expected to resolve many computationally hard problems, such as Radio Resource Allocation (RRA) in wireless communications and networking. However, given noisy intermediate-scale quantum (NISQ) computers that supply a very limited number of effective logical qubits after quantum error correction, computationally efficient algorithms are highly desirable to facilitate quantum advantages, particularly the required number of qubits. To achieve quantum advantageous optimization by small number of qubits, we formulate RRA as a Maximum Independent Set (MIS) on dense conflict graphs, which is challenging for the Quantum Approximate Optimization Algorithm (QAOA) using NISQ computing machines. We further propose a modified Quantum Local Search (QLS) framework that decomposes the global MIS into overlapping subgraphs defined by a tunable neighborhood radius. Hard (feasibility) constraints are enforced via partial mixers, while soft (adjacency) constraints are resolved in a classical post-processing step. Considering practical interference issues that are typically ignored in quantum computational research, we uniquely extend the method to accommodate minimal-adjacency scenarios by specifying shared boundaries causing limited interference. While complexity analysis shows polynomial scaling in the number of feasible placements, computational experiments demonstrate substantial savings on required qubits and simultaneously achieving optimal solutions across multiple adjacency configurations. Efficient quantum optimization of RRA by NISQ computers of significantly fewer qubits is therefore accomplished.

Ultra-Reliable low-latency Communication (URLLC) fundamentally differs from traditional wireless communication and networking theory. Its requirements are contradictory because closed-loop protocols that address reliability violate the low-latency requirements. URLLC remains a daunting challenge for emerging machine-to-machine (M2M) communication. Suppose we optimize for reliability as opposed to only throughput. In that case, proactive open-loop communications can address the ultra-low-latency requirements. Meanwhile, simultaneous association with multiple Access Points (APs) via a virtual cell can address ultra-reliable requirements. In this paper, we expand upon proactive multi-AP communication via low-complexity URLLC for bursty network traffic by analyzing its heterogeneous dynamic nature and impact. We design a strategy-proof proactive cell association reliability mechanism where a Smart Machine (SM) can utilize a Single Frequency Network (SFN) to achieve URLLC communications. Without additional control overhead, our communication system implicitly provides cell association reliability information to each SM via an AP’s cell breathing (cell radius conveys SM association reliability to each SM). Our analysis uses game theory to study the theoretical and fundamental AP-SM pairing behaviors of proactive open-loop explicit feedback-free protocols in a proactive virtual cell network. We confirm the theoretical results via numerical evaluations and present our insights. Our method can achieve reliable SM connectivity that is superior to non-proactive cell association methods.

Smart factories are considered as an attractive vertical application for beyond 5G and 6G mobile communications, in which ultra-reliable and low-latency communication (uRLLC) serves integrated communication and computing for multi-robot systems. To effectively achieve uRLLC, we investigate Real-Time ALOHA (rt-ALOHA), a grant-free random access protocol to achieve ultra reliability and minimal latency in an indoor factory (InF) scenario. Different from traditional cell formation by base stations or access points, virtual cell is considered with rt-ALOHA, such that each robot becomes the center of a virtual cell and is served by multiple APs, while random selection among available Resource Blocks (RBs) for physical communication in rt-ALOHA. that is, any data packet is transmitted over multiple different diversity paths to simultaneously ensure minimal latency and reliability. Our reliability-latency analysis under Nakagami-m channels and simulations over realistic indoor factory channels show that, given minimal latency, the rt-ALOHA protocol successfully increases the probability of packet success and thus ensures high reliability for an industrial wireless networks operating in a smart factory, a promising B5G/6G and wireless LAN vertical application.

Wireless robotic communications empowering multi-robot systems in a smart factory emerges as an attractive mission-critical vertical application scenario for next generation wireless communication systems and networks. Due to the short age of information for collaborative multi-robot systems, ultra-reliable and low-latency communication (uRLLC) plays an essential role in smart factories for Industry 4.0. However, impulsive interference caused by factory robots’ operation could be a technical challenge to threaten uRLLC. In this paper, based on multiple networking paths through different access points, grant-free radio access network has been developed and shown effective uRLLC under impulsive interference whose activities are modeled by a bursty Markov process. Numerical results verify that such spatial/path diversity and radio resource diversity successfully achieves desirable reliability and low-latency communications against impulsive interferences and noisy fading in a smart factory.

This paper presents a novel resilient production problem in a multi-robot system (MRS) driven smart factory that suffers diverse degradation of accuracy among heterogeneous robots. The inevitable degradation in production robots’ accuracy undermines desirable productivity and efficiency. Although traditional maintenance and calibration strategies can be employed, they fail to support the continuity, flexibility and agility required in smart factories. Instead of pursuing accurate task execution relative to a consistent global reference, we formulate an innovative MRS coordination strategy, where a collaborative MRS autonomously pursues relative accuracy against accuracy degradation toward resilient operation. This new coordination strategy introduces challenges including adapting to time-dynamic production flows, difficulties in observing production robots’ accuracy, and the absence of a comprehensive architecture of resilient MRS in a smart factory. We propose a computational approach with social learning and AI to overcome these challenges. A cyber-physical MRS model is proposed, in which the physical domain represents the time-dynamic production flows, while the cyber domain forms a partially connected wireless network to automate the collection of data regarding production flows, accuracy degradation, and peer robot measurements. Social learning based on such data is employed to collaboratively estimate accuracy and AI decision-making, thereby enabling adaptive coordinated task execution against accuracy degradation. We further investigated a group decision-based predictive maintenance against point failures caused by accuracy degradation. Computational experiments demonstrate that the proposed approach improves the effective rate and mean-time-to-fail performances against accuracy degradation and point failures in a scalable manner. Note to Practitioners—The practical problem motivating our work revolves around resilient production, which is affected by diverse accuracy degradations among heterogeneous robots. This issue is particularly relevant for implementing smart factories with multi-robot systems (MRS), including production and transportation robots. We propose a cyber-physical multi-robot system (CPMRS) to comprehend the dynamics of MRS-driven smart factories, aiming to facilitate productivity, efficiency, and resilience through MRS coordination, as opposed to accurate production task execution achieved by frequent maintenance and calibration, which sacrifices the flexibility and agility of smart factories. CPMRS allows the autonomous collection and exchange of information about production flows, accuracy degradations, and peer robot measurements with limited wireless communications. CPMRS further enables computational solutions with social learning and reinforcement learning for robots as well as group-decision-based predictive maintenance. It facilitates autonomous coordinated task execution and resilient production. Computational experiments demonstrate a 124 percent improvement in the mean time to failure (MTTF) and a 317.27 percent improvement with predictive maintenance. As in our proposal to general manufacturing, further research may be adapted to specific manufacturing scenarios. This research can benefit broader applications, such as smart logistics, warehouses, and even smart cities, where similar collective actions among multiple robots or agents are required.

Resilient operation of a wireless networked multirobot system (MRS) in a smart factory relies on the effective detection of physical anomalies from robots and cyber anomalies from wireless transmission errors or imprecise artificial intelligence decisions, which leads to a new technological frontier in data-driven industrial informatics: cyber-physical anomaly detection (AD). Furthermore, data patterns in a single smart factory are unlikely enough to train high-quality learning models for this new cyber-physical AD, which suggests the necessity to utilize operating data from multiple smart factories while keeping the privacy of each factory's data. To overcome the aforementioned technical challenges for cyber-physical AD in smart factories, this article proposes an integral mechanism of generative adversarial networks, federated learning, and fuzzy clustering acceleration. Generative adversarial networks facilitate data imputation to regenerate complete datasets alleviating anomalies caused by wireless communications. Federated learning enables rich privacy-preserving datasets to be jointly used among multiple collaborative factories. Furthermore, fuzzy clustering acceleration is embedded to speed up the factory selection algorithm such that efficient training and real-time physical AD in the large-scale operation of multiple smart factories can be achieved. Extensive computational experiments based on the KDD-99 dataset demonstrate the effective and efficient cyber-physical AD of wireless networked MRS in collaborative multiple smart factories.

Smart factories employ intelligent transportaton systems such as autonomous mobile robots (AMRs) to support real-time adjusted production flows for agile and flexible production. While decentralized transportation task execution provides a scalable multirobot system (MRS) for a smart factory, new coordination challenges arise in implementing such a system. Transportation-MRS collaborates with production-MRS to accommodate just-in-time (JIT) production, leading to nonstationary transportation tasks that transportation-MRS must learn and adapt to. Also, decentralized operation on a shared shop floor means that one robot cannot factor in peer robots’ task execution planning, leading to competitive collisions. Meanwhile, predictive coordination with communication among multiple learning and adapting intelligent robots is still an open problem. On top of identifying the aforementioned challenges, this article first proposes a multifloor transportation graph model to discretize transportation task execution and allow real-time adjustment of transportation paths toward collision-free. We introduce a unique collaborative multi-intelligent robot system approach taking each robot as a cyber–physical agent with automated artificial intelligence (AI) workflow. First, it includes a novel multiagent reinforcement learning (MARL) algorithm, where each robot predictively plans collision-avoidant paths. Second, we introduce a token-passing mechanism to resolve inevitable competitive collisions due to nonstationary tasks. The proposed approach innovatively uses the multifloor model as a domain model for planning. By allowing competitive collision to occur and resolve, a robot only needs to learn and adapt to uncertain parts of the environment—nonstationary tasks and peer robots’ paths. Computational experiments show that our approach is both sample-efficient and computationally efficient. The transportation-MRS quickly reaches near-optimal performance levels, which are empirically shown to scale with the number of robots involved.

Internet of Robotic Things (IoRT) emphasizes the integrated robotic, artificial intelligence computing, and communication technologies, enabling more sophisticated operations and decision-making. As a crucial element of IoRT, mission-critical applications, such as industrial manufacturing and emergency services, impose stringent requirements on ultra-reliable and low-latency communication (URLLC). The paper focuses on addressing URLLC challenges in the context of IoRT, particularly when autonomous mobile robots (AMRs) coexist with static sensors. We prioritize safe and efficient AMRs’ travel through trajectory design and communication resource allocation in IoRT systems without the need of any prior knowledge. To enhance network connectivity and exploit diversity gains, we introduce the flexible decoding and free clustering as the next-generation multiple access technologies in spectrum-limited downlink IoRT system. Then, aiming at minimizing the decoding error probability and travel time, we formulate a long-term multi-objective optimization problem by jointly designing AMRs’ trajectory and communication resource. To accommodate the inherent dynamics and unpredictability in the IoRT system, we introduce a multi-agent actor-critic deep reinforcement learning (DRL) framework, offering four distinct implementations, each accompanied by comprehensive complexity analyses. Simulation results reveal the following insights: 1) in terms of DRL implementations, off-policy algorithms with deterministic policies outperform their on-policy counterparts, achieving approximately a 67% increase in rewards; 2) In terms of communication schemes, our proposed flexible decoding and free clustering strategies under designed trajectories can effectively reduce decoding errors; and 3) In terms of algorithm optimality, our DRL framework shows superior flexibility and adaptability in communication environments compared to traditional A* search and heuristic methods.

Ultra-Reliable Low Latency Communication (URLLC) remains a daunting challenge toward 6G, of particular interest in use cases involving machine-to-machine communications and Extended Reality (XR) applications. Proactive communication was proposed to effectively establish URLLC for minimal latency. In this paper, we further innovate opportunistic multi-access of proactive virtual cell communications for smart machines. We employ game-theoretic analysis to explore the fundamental behavior of such a decentralized mechanism. Instead of optimizing throughput in traditional wireless access, maximizing ultra-reliability by a strategy-proof proactive mechanism of multi-cell networking architecture disruptively achieves minimal latency without the need for any additional control overhead, whose impressive performance is further confirmed by numerical evaluations.

Smart factories driven by multi-robot systems (MRS) or multi-agent systems (MAS) integrate edge computing, mobile computing, and wireless communications for exceptionally dynamic, flexible, productive, and resource-efficient manufacturing. With real-time multi-robot task allocation assigning tasks to MRS, MRS executes tasks collaboratively to realize the objectives of smart factories. Ultra-low latency and reliable wireless communication enable such collaborative MRSs in an efficient and resilient manner. On top of time-sensitive multi-link wireless access, this paper takes advantage of social network properties to explore the topology control and channel/resource allocation for the transportation MRS of autonomous mobile robots (AMRs) in a smart factory. To satisfy the minimal latency through proactive communication, adaptive channel allocation occurs concurrently with the formation of social communities based on the robot's geometric trajectories. Computational experiments demonstrate that the proposed methodology delivers a more balanced collision rate and a substantial increase in the average channel access rate compared to what traditional static channel allocation offers.

To develop the energy efficient of 6G networks, the fog radio access network (F-RAN) is expected to meet various use cases of high-performance mobile services. Although network virtualization greatly enhances the flexibility to accommodate various services, the interaction and optimization between delay and energy efficiency (EE) in virtual slicing (VS)-based F-RAN have not been well studied. To accomplish the EE and delay optimization of VS of F-RAN. The key technical challenges lie in the construction of new network architecture, the integration and optimization of radio, caching, and computing 3-D resources, reducing the algorithm’s complexity, and simulation verification. We first design a novel network architecture based on VS and fog computing. The VS method in F-RAN to embed VS assembles virtual radio, caching, and computing resources into physical substrates, transforming nonconvex problems into convex optimization problems by transforming constraints and using Lyapunov optimization methods. We further propose a virtual resource allocation optimization algorithm based on EE. To reduce the complexity of the algorithm, a low-complexity EE optimization algorithm is further proposed for virtual resource allocation. The simulation results show that the low-complexity virtual resource allocation EE optimization algorithm proposed in this article has better performance than the existing fog access network resource allocation methods. The EE is further improved by about 30% under the condition that the guaranteed delay threshold is two slots (i.e., 1 ms).

Rapid and efficient sensor data acquisition plays a critical role in the decision-making process of each robot in a multi-robot smart factory. This paper investigates the trajectory design of autonomous mobile robots (AMRs) and communication resource allocation problems in industrial Internet of Things. Specifically, by exploiting both power and spatial domains, we adopt non-orthogonal multiple access to improve network connectivity in a spectrum-efficient manner, while the multi-antenna technique is employed to enhance diversity gain. The average sum rate is maximized by jointly optimizing the transmit power of sensors and the trajectory of AMRs. To deal with prior knowledge and dynamic channel conditions, we reformulate the long-term maximization problem as a Markov decision process, and further develop a provably efficient multi-agent reinforcement learning algorithm with a near-optimal regret bound. Our theoretical analysis reveals that both the decentralized execution and the experience exchange method are beneficial to accelerate convergence. Simulation results show that our proposed algorithm can reduce at least 80% convergence time compared to the centralized baseline, and can gain better rewards than the conventional$\epsilon $-greedy exploration.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology in achieving high capacity and massive connectivity. In this paper, the reliable transmission scheme of downlink NOMA systems is investigated. In particular, we divide the disc covered by the base station into several annular areas, where the receivers are randomly located following a uniform distribution. In this way, NOMA pairing is performed by randomly selecting receivers from two different areas. Firstly, we derive the closed-form expressions of bit error rate (BER) with quadrature phase-shift keying (QPSK) modulation, where the channel is modeled as small-scale Rayleigh fading and large-scale path loss. To achieve reliable communications, then, the BER performance of the receiver with the worst channel gain in each area is studied. Finally, an optimal power allocation algorithm is proposed, which obtains the minimum transmission power and optimal power allocation factor with a given BER constraint of all receivers. Extensive simulations demonstrate the accuracy of obtained BER expressions and the effectiveness of the proposed algorithm. These results provide valuable insight into realizing on reliable transmission of NOMA with randomly deployed receivers.

With the advance of artificial intelligence (AI), the Internet of Things (IoT), and 5G communication technologies, various kinds of traffic data from diverse devices can be acquired nowadays, and they can help us look into intelligent transportation systems (ITSs) with a new eye. Graph-based machine learning holds out the potential as a powerful tool for modeling complex structural data relationships and also mining both useful information and temporal patterns which could be used for building powerful analytics for ITS construction. Considering the benefit of graph-based machine learning for ITS, some graph-based machine learning methods/architectures have been proposed. Even though these methods have achieved certain success, there exist various scientific and engineering challenges.

Proactive Mobile Network (PMN) has been proposed to support extremely low latency communications with multi-tier computing architectures, machine-centricity, and data-driven operation features. Nevertheless, the communication reliability of the PMN introduces new substantial technological challenges. As PMN employs unique proactive open-loop communication, any feedback-based control is avoided to enhance end-to-end latency. This paper focuses on machine-initiated uplink transmission in PMN and proposes a reliability-guaranteed resource management scheme. Without requiring feedback control information, our scheme uniquely decomposes conventional resource management into two collaborative decision processes: predictive resource allocation suggested by network anchor nodes (ANs) and proactive smart resource utilization by smart equipment (SE). These two decision-making processes are constructed as independent reinforcement learning (RL) problems, but implicitly share the states of radio resources according to operating environments. Different algorithms for different operating scenarios have been investigated for this dual-decision solution. Simulation results in various scenarios show that our scheme enables PMN’s reliability close to the theoretical optimal value and successfully serves radio resource utilization in the PMNs.

Abstract With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.

To facilitate Industry 4.0 and smart manufacturing, smart factories shall flexibly achieve mass manufacture of on-demand individualized products. Reconfigurable multirobot system (MRS)-driven smart factories as a representative paradigm fulfill the above requirements through dynamically adjusting production flows while optimizing productivity and energy efficiency. This article innovates real-time multirobot task allocation to coordinate a heterogeneous MRS so that frequent reconfiguration brought by dynamic production demands can be answered. The challenges brought by frequent production flow adjustment are analyzed and addressed by proposing a hypergraph MRS model and a hypergraph search algorithm. The hypergraph MRS model is tailored to characterize temporal–spatial features and heterogeneity of the MRS, task assignments of production robots, and transportation paths of transportation robots. The hypergraph search algorithm fulfills the production demands, dual-objective dual-agent optimization of productivity and energy efficiency, and constant time complexity for real-time coordination. Computational experiments show that the proposed approach delivers effective task assignments of optimized MRS productivity and energy efficiency with robust performance.

Targeting at implementing the next generation radio access networks (RANs) with virtualized network components, the open RAN (O-RAN) has been regarded as a novel paradigm towards fully open, virtualized and interoperable RANs. Through particularly introducing RAN intelligent controllers (RICs), machine learning (ML) can be unprecedentedly installed, adapting to various vertical applications and deployment environments without sophisticated planning efforts. However, the O-RAN also suffers two critical challenges of load balancing and frequent handovers in the massive base station (BS) deployment. In this paper, an intelligent user access control scheme with deep reinforcement learning (DRL) is proposed. To optimize the performance of distributed deep Q-networks (DQNs) trained by user equipments (UEs), a federated DRL-based scheme is proposed with a global model server installed in the RIC to update the DQN parameters. To further predictively train a global DQN with acceptable signaling overheads, the upper confidence bound (UCB) algorithm to select the optimal UE set and a dueling structure to decompose the DQN parameters are developed. With the proposed scheme, each UE effectively maximizes the long-term throughput and avoids frequent handovers. The simulation results well justify the outstanding performance of the proposed scheme over the-state-of-the-arts, to serve as references for the O-RAN standardization.

Smart factories powered by Multi-Robot Systems (MRSs) play a central role in Industry 4.0 and smart manufac-turing. MRS operating under dynamic task assignments of col-laborative robots in production flows suggests a new technology paradigm to achieve productivity, flexibility, and energy efficiency to revolutionize the industry. However, dynamic collaborative MRSs, given resource-limited wireless communication, robotic AI computing, and lacking globally accurate references lead to a technological challenge to facilitate resilient, reliable, and precise operation of smart factories. This paper innovatively resolves this cyber-physical challenge by aiming to align robotic actions in dynamic production flows based on the concept of grouping and propose a social learning-based method that utilizes Bayesian network and reinforcement learning (RL) to provide robust coordination that improves productivity and resilience against bursty cyber and physical inaccuracies in difficult dynamic sys-tems. Numerical experiments demonstrate successful fulfillment of technical requirements for the smart factory.

The Open Radio Access Network (O-RAN) introducing a particular unit known as RAN Intelligent Controllers (RICs) has been regarded as revolutionary paradigms to support multiclass wireless services required in the fifth and sixth generation (5G/6G) networks. Through unprecedentedly installing various machine learning (ML) algorithms to RICs, a RAN is able to intelligently configure resources/communications to support any vertical applications over any operating scenarios. However, to practically deploy this RAN paradigm, the O-RAN still suffers two critical issues of load balance and handover control, and therefore the very first ML algorithm for the O-RAN should effectively address these issues. In this paper, inspired by the superior performance of deep reinforcement learning (DRL) in tackling sequential decision-making tasks, we therefore develop an intelligent user access control scheme with the facilitation of deep Q-networks (DQNs). A federated DRL-based scheme is further proposed to train the parameters of multiple DQNs in the O-RAN, so as to maximize the long-term throughput and meanwhile avoid frequent user handovers with a limited amount of signaling overheads in the O-RAN. The simulation results have fully demonstrated the outstanding performance over the state-of-the-arts, to service the urgent needs in the standardization of the O-RAN.

Each robot utilizes the reinforcement learning (RL) to control its maneuver and these robots can collaborate to accomplish a common goal to form a collaborative multi-agent system (MAS). Due to the constraints of distributive locations and different poses of robots, in practice, each agent (robot) in such a collaborative MAS can only partially observe the environment and other agents (such as competitive agents), and consequently operate based on its belief of the state(s). The alignment of the beliefs of collaborative agents can be therefore enhanced by adopting wireless communications, but is rarely studied in literature. To explore wireless communications applied to collaborative partially-observable reinforcement learning (PORL), we propose that each collaborative agent predicts the environment dynamics, including the behavior of those agents outside the collaborative MAS, and then constructs the learning-based belief of the world (i.e. global state). To assist such prediction and learning, we modify the RL assisted by the wireless communication functionality into two stages: prediction of the state and local actor-and-critic on global value(s). In other words, while one agent predicts and learns its own policy, another agent can updates critics on the sequence of history to update global value(s) that can further assist to validate the prediction. From numerical experiments, we find that the timing of communication or information exchange among collaborative agents has critical impact on the duration of learning and prediction, and thus the performance of MAS, which suggests the desirable communication for distributed PORL among collaborative agents toward an efficient MAS.

With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges. This paper introduces a novel framework, Federated Generative Adversarial Networks and Automatic Classification (FGAN-AC), which integrates decentralized data synthesizing with traffic classification. FGAN-AC is able to synthesize and classify multiple types of service data traffic from decentralized local datasets without requiring a large volume of manually labeled dataset or causing any data leakage. Two types of data synthesizing approaches have been proposed and compared: computation-efficient FGAN (FGAN-I) and communication-efficient FGAN (FGAN-II). The former only implements a single CNN model for processing each local dataset and the later only requires coordination of intermediate model training parameters. An automatic data classification and model updating framework has been proposed to automatically identify unknown traffic from the synthesized data samples and create new pseudo-labels for model training. Numerical results show that our proposed framework has the ability to synthesize highly mixed service data traffic and can significantly improve the traffic classification performance compared to existing solutions.

To achieve the minimal latency, proactive wireless communication has been proposed to facilitate proactive mobile networks. Due to lacking closed-loop control, random selection of radio resource units (RRUs) serves the only way to the critical radio resource allocation (RRA), which inevitably suffers collisions of simultaneous utilizing the same RRUs to result in loss of packets, particularly in the uplink. Macroscopic view on the uplink and downlink network operation cycle insightfully suggests that it is still possible to construct a delayed version of semi-closed-loop operation to predictively utilize radio resource in the uplink of proactive mobile network. A two-stage reinforcement learning mechanism to enable the recommendation of radio resource utilization from network infrastructure to mobile smart machines is proposed, which consists of the multi-armed bandit scheme for RRA and the neural network to learn historical utilization of RRUs. Simulations verify machine learning enables smart and predictive RRA with superior performance that can lead to the minimal latency of mobile networking.

Smart manufacturing based on artificial intelligence and information communication technology will become the main contributor to the digital economy of the upcoming decades. In order to execute flexible production, smart manufacturing must holistically integrate wireless networking, computing, and automatic control technologies. This article discusses the challenges of this complex system engineering from a wireless networking perspective. Starting from enabling flexible reconfiguration of a smart factory, we discuss existing wireless technology and the trends of wireless networking evolution to facilitate multirobot smart factories. Furthermore, the special sequential decision-making of a multirobot manufacturing system is examined. Social learning can be used to extend the resilience of precision operation in a multirobot system by taking network topology into consideration, which also introduces a new vision for the cybersecurity of smart factories. A summary of highlights of technological opportunities for holistic facilitation of wireless networked multirobot smart factories rounds off this article.

Attaining reliable communications traditionally relies on a closed-loop methodology but inevitably incurs a good amount of networking latency thanks to complicated feedback mechanism and signaling storm. Such a closed-loop methodology thus shackles the current cellular network with a tradeoff between high reliability and low latency. To completely avoid the latency induced by closed-loop communication, this article aims to study how to jointly employ open-loop communication and multi-cell association in a heterogeneous network (HetNet) so as to achieve ultra-reliable and low-latency communications. We first introduce how mobile users in a HetNet adopt the proposed proactive multi-cell association (PMCA) scheme to form their virtual cell that consists of multiple access points (APs) and then analyze the communication reliability and latency performances. We show that the communication reliability can be significantly improved by the PMCA scheme and maximized by optimizing the densities of the users and the APs. The analyses of the uplink and downlink delays are also accomplished, which show that extremely low latency can be fulfilled in the virtual cell of a single user if the PMCA scheme is adopted and the radio resources of each AP are appropriately allocated.

Since early 2020, the COVID-19 global pandemic has significantly impacted almost every aspect of the human society throughout the world. Until now, middle of 2021, although with all the efforts on pandemic intervention and vaccination, COVID-19 is still hovering around the world, resulting in more than 177 million confirmed cases and 3.8 million deaths.

Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The overhead of additional pilot transmission at the price of spectral efficiency is shown necessary to achieve high resilience in operation. An efficient method to deal with high dynamics of UAVs applies machine learning, particularly Q-learning, to analog beam tracking. The proposed Q-learning-based beam tracking scheme uses current/past observations to design rewards from environments to facilitate prediction, which significantly increases the efficiency of data transmission and beam switching. Given the selected analog beams, the goal of digital beamforming is to maximize the SINR. The received pilot signals are utilized to approximate the desired signal and interference power values, which yield the SINR measurements as well as the optimal digital weights. Since the selected analog beams based on the received power do not guarantee the hybrid beamforming achieving the maximization SINR, we therefore reserve additional analog beams as candidates during the beam tracking. When the candidates include the ideal beams, the combination of analog beams with their digital weights achieving the maximum SINR consequently provides the optimal solution to the hybrid beamforming.

In the Fog Radio Access Networks (F-RANs), the local storage and computing capability of Fog Access Points (FAPs) provide new communication resources to address the latency and computing constraints for delay-sensitive applications. To achieve the ultra-low latency, a novel joint user association and channel allocation scheme is proposed in this paper, where the FAPs are clustered from a user-centric perspective. The delay performance is improved regarding both the control signaling procedure and the data transmission procedure. Specifically, the multiple access interference (MAI) between users is analyzed, where the closed-form expression for the effective rate of a typical user with multiple FAP connections and arbitrary interfering users is obtained. With the consideration of MAI, the proposed distributed joint user association and channel allocation algorithm provides a guaranteed delay violation probability. Moreover, the distributed algorithm can be conducted on individual FAPs, whose calculation is simplified by look-up tables. Simulation results show that the proposed algorithm is capable of providing statistical delay performance guarantee including both average delay and delay bound violation probability, which demonstrates its superiority in supporting delay-sensitive applications in F-RANs.

Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles. Most connected vehicular applications require frequent status updates and Age of Information (AoI) is a more relevant metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI. In particular, we report a study on three month measurements of a multi-vehicle campus shuttle system connected to cloud/fog servers via a commercial LTE network. We establish empirical models for AoI in connected vehicles and investigate the impact of major factors on the performance of AoI. We also propose a Deep Q-Learning Network (DQN)-based algorithm to decide the optimal driving route for each connected vehicle with maximized confidence level. Numerical results show that the proposed approach can lead to a significant improvement on the AoI confidence for various types of services supported.

Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

Mobile edge computing (MEC) supporting localized context awareness creates a new technological frontier for 5G and beyond. Due to very asymmetric traffic related to MEC and the time division duplexing (TDD) system, we efficiently exploit the networking and computing functionalities for TDD orthogonal frequency division multiple access (TDD-OFDMA) technology supporting multiple services. The primary technical challenge of TDD-OFDMA systems lies in dynamic configuring based on the unknown characteristics of future traffic, i.e., the information lag. Therefore, a model-free online TDD configuration scheme is proposed based on context analysis and multi-armed bandit (MAB) optimization. The characteristics of future traffic are predicted by the context-aware MEC computing, so that TDD configuration is novelly modeled as a contextual MAB problem. Solving MAB by the contextual upper-confidence-bound, TDD configuration can be dynamically adjusted according to network traffic. To simultaneously reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is further developed to select MDs and allocate resources. GRA algorithm decomposes the complex multi-factor coupling non-convex problem into a series of convex sub-problems, thereby asymptotically obtaining the selection and allocation with polynomial time complexity. Simulations justify significant performance gain in mobile networking and MEC.

Ultra-low latency communication for mobile intelligent machines, such as autonomous vehicles and robots, is a central technology in Internet of Things (IoT) to achieve system reliability. Proactive network association and communication has been suggested to achieve ultra-low latency under the assistance of mobile edge computing. Highly dynamic and stochastic nature of IoT mobile machines suggests applying machine learning methodology to effectively enhance the proactive network association. In this paper, an online proactive network association is proposed for this distributed computing and networking scenario, in order to minimize the average task delay subject to time-average energy consumption. We first formulate an event-triggered delay model for mobility-aware anticipatory network association mechanism that takes future possible handovers into account. Based on the Markov decision processes (MDP) and Lyapunov optimization, a two-stage online decision algorithm for proactive network association is innovated for individual mobile machine without the statistical knowledge of random events that may lack of enough prior data. Theoretical analysis proves that the delay performance of proposed algorithm attains asymptotic optimality within the bounded deviation. Furthermore, an asynchronous online distributed association decision algorithm based on the nonlinear problem transformation is proposed to support more general scenarios of multi-machine event-triggered associations. Simulations verify the effectiveness of the proposed methodology.

Opportunistic links are emerging as a novel technology for state-of-the-art wireless networks, such as cognitive radio networks, energy-harvesting networks, and low-latency vehicular networks. To increase the reliability of these networks, path-permutation codes (PPCs) were advocated in [1]. These codes are based on a virtual multiple-input multiple-output (MIMO) model on the session/network layer, and have been tailored for increased reliability of opportunistic links. By exploiting multipath-multihop routes, PPCs access one relay path at a time while repeatedly transmitting the same QAM symbol. In this paper, we introduce a generalized PPC scheme which simultaneously accesses multiple relay paths and comprises multiple QAM symbols in the packet. Phase rotations are also introduced in the PPC codewords for additional performance improvement. Numerical simulation results validate our analyses and demonstrate the superior performance of this novel coding scheme, i.e., better error rate performance, higher throughput, and stronger robustness to transmission outages.

Applying the concept of virtual cell together with open-loop communications has been recently shown to achieve ultra-reliable and low-latency communication (uRLLC) in vehicular networks. Nevertheless, losing perfect synchronization due to proactive communications creates difficulty in mitigating multiple access interference (MAI). Multiple carrier frequency offsets (CFOs) resulting from different oscillators at different access points (APs) incur serious inter-carrier interference (ICI) to further complicate downlink MAI. Asynchronous multiuser detection (MUD) with ICI-Whitening was shown leading to satisfactory performance, but the whitening scheme needs the covariance matrix of ICI that is practically hard to obtain for downlink receivers. We develop a two-stage ICI suppression method to resolve this challenge. The first-stage processing is Pseudo-ICI-Whitening (P-ICI-W) or its simplified version Pseudo-Truncate ICI-W (PT-ICI-W), which does not rely on the estimation of ICI covariance and is suitable for asynchronous downlink. In terms of post-processing signal-to-interference-plus-noise ratio (SINR) and bit-error rate (BER), our proposed mechanism can approach ICI-Whitening. The second-stage processing is based on the recently proposed generalized linear minimum mean square error (LMMSE) projection to further cancel some ICI terms. Moreover, our proposed mechanism is compatible with space-time-block-coded signals, namely Alamouti coding and Complex Interleaved Orthogonal Design (CIOD), to yield more reliable proactive wireless communications.

Aerial deployment of access points (APs) to network infrastructure has been shown to be an effective technological solution for rapidly supplying networking services in diverse scenarios, particularly post-disaster networking after major hurricane, tsunami, and earthquake events, or for the purposes of homeland security. A straightforward yet helpful method for deployment of APs in this emerging technology is to consider the problem in two 2D planes, one for users and another for APs. However, considering the need for effectively connecting low-power user devices that may suffer from severe fading due to the blocking of line-of-sight(LoS) transmissions, genuine 3D deployment is very desirable, even though a simpler form of it had been mathematically an open problem until 2017 for 400 years. To meet the engineering requirements, we first leverage 3D random geometric graphs (RGG) to develop the joint optimization of 3D AP placement with a given number of APs for proper coverage. We further propose a computationally-efficient iterative algorithm to effectively deploy the adequate number of APs in 3D space and respond to dynamics of such wireless networks. Our simulations demonstrate the superior performance of this genuine 3D placement of APs over traditional approaches when considering connections for user devices, particularly for larger number of APs in rapid deployment.

Collaborative robots as a multi-agent system to complete a common mission without public reference but operate individual decision and learning mechanism represent a wide range of applications in artificial intelligence. With an illustrative example, reinforcement learning with localization and planning capabilities is developed to represent each robot's operation. It is shown that wireless robotic communication can significantly enhance overall performance of collaborative MAS in the distributed operating manner. After identify useful information (i.e. reward map and private reference) to exchange, according to properties of content, p-persistent real-time ALOHA is suggested to serve as the multiple access protocol of the ad-hoc style networking toward ultra-reliability and ultra-low latency, resulting in satisfactory overall performance close to ideal communication. Wireless robotic communication therefore reveals new technological opportunities for robotics, multi-agent systems, artificial intelligence, and communications.

The operation of autonomous multi-agent systems (MAS) under the complex interactions among intelligent agents heavily relies on the consensus protocols to facilitate multi-agent collaboration. In this paper, we novelly study the coordination and control strategy for robust consensus building in a distributed MAS of bayesian social learning agents that make sequential decisions with finite horizon. Unlike the conventional consensus problems in literature, the idiosyncratic information cascading behavior arising from multi-agent social learning poses a new challenge to consensus protocol design. Although cascading can alleviate randomness and enhance consensus, cascading in the early stages of sequential decision making may cause error-prone bifurcation behavior. Therefore, how to properly utilize the advantage of information cascade and meanwhile mitigating its undesirable side effect is critical in the design of consensus protocol to control distributed MAS of social learning agents. Considering communication topology, we uniquely develop a robust cascading strategy for agents adapting to local network neighborhood. Our results show that the proposed strategy not only mitigates early cascading failures but also guarantees multiagent consensus in various complex network topologies.

Applying the concept of virtual cell together with open-loop communications and proactive network association has recently been shown to achieve ultra-reliable and low-latency communication (uRLLC) in vehicular networks. Nevertheless, losing perfect synchronization creates difficulty in tackling multiple access interference (MAI). Multiple carrier frequency offsets (CFOs) due to different oscillators at different access points (APs) incur serious intercarrier interference (ICI) to complicate downlink MAI, from which a more challenging scenario emerges. Asynchronous multiuser detection (MUD) with ICI-Whitening leads to satisfactory performance, but the whitening scheme needs the covariance matrix of ICI that is practically hard to obtain for downlink receivers. We therefore propose Pseudo-ICI-Whitening, which does not rely on the estimation of ICI covariance, is suitable for asynchronous downlink. In terms of post-processing signal-to-interference-plus-noise ratio (SINR) and bit-error rate (BER), our proposed mechanism can approach ICI-Whitening as a practical receiver design.

With tremendous computing power in the radio access network, mobile edge computing (MEC) that can support localized context awareness creates a new technological frontier for 5G and beyond. To efficiently exploit the networking and computing functionalities, Time Division Duplex Orthogonal Frequency Division Multiple Access (TDD-OFDMA) type has been considered in this paper. To take advantage of dynamic features in TDD, a model-free online TDD configuration scheme is proposed based on context analysis and Multi-Armed Bandit (MAB) optimization. The TDD configuration problem is therefore novelly modeled as a contextual MAB problem, and is solved by the contextual Upper-Confidence-Bound (C-UCB), which dynamically adjusts TDD configuration to network traffic since that the system cost can be reduced. To further reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is developed to select MDs and allocate resources. The simulations demonstrate that proper TDD configuration successfully reduces the system cost, and C-UCB technique approaches the ideal TDD configuration, with significant performance gain when the GRA effectively select and allocate, to strike simultaneous efficiency for mobile networking and MEC.

Artificial intelligence (AI) and big data analytics enable autonomous vehicles (AVs) to dramatically change future intelligent transportation in smart cities. AVs are envisaged to evolve to a service rather than a product in the future. To provide best user experience of such services, three primary factors, namely, waiting time, travel time, and supply of AV services, are taken into consideration in a multiobjective optimization. Conventional optimization of services relies on traffic flow analysis over a queuing network model. However, due to the mobility of vehicles and the transfer uncertainty of road networks, the queuing network analysis is too complicated and practically intractable. For accuracy and convenient processing, network calculus (NC) is extended to model the queueing problem in this paper. The optimal number of available AVs can be identified by guaranteeing the waiting time of customers. The satisfaction of AV services can be viewed as a supply and demand problem, and optimized by bipartite graph matching. In order to reduce the average travel time, especially for rush hours with heavy traffic, we further propose a new online AVs fleet management scheme with congestion control for smart cities. It is shown that the intelligent management of AV fleet can be efficiently achieved, outperforming the cases of traditional vehicles. NC-assisted AI enables an efficient intelligent transportation paradigm in smart cities, while achieving substantial energy saving.

This IEEE Journal on Selected Areas in Communications (JSAC) special issue (SI) aims to provide a comprehensive overview of the state-of-the-art advances and a view of emerging research challenges and opportunities forSpatial Modulation in Emerging Wireless Systems. This SI solicits high-quality original research papers regarding theoretical studies, and application-oriented contributions dealing with architectures, platforms, and multiple access schemes.

Spatial modulation (SM) is an innovative and promising digital modulation technology that strikes an appealing tradeoff between spectral efficiency and energy efficiency with a simple design philosophy. SM enjoys plenty of benefits and shows great potential to fulfill the requirements of future wireless communications. The key idea behind SM is to convey additional information typically through the ON/OFF states of transmit antennas and simultaneously save the implementation cost by reducing the number of radio-frequency chains. As a result, the SM concept can have widespread effects on diverse applications and can be applied in other signal domains, such as frequency/time/code/angle domain or even across multiple domains. This survey provides a comprehensive overview of the latest results and progresses in SM research. Specifically, the fundamental principles, variants of system design, and enhancements of SM are described in detail. Furthermore, the integration of the SM family with other promising techniques, applications to emerging communication systems, and extensions to new signal domains are also extensively studied.

Interactions of multiple smart agents serve a fundamental aspect of Internet of Things (IoT), or known as social IoT. Such smart agents are equipped with sophisticated machine learning for mobile operation, such as autonomous vehicles and robots. Although wireless networking is intuitively important in such scenarios, there lacks investigations to provide a holistic and in-depth understanding on wireless networked multi-agent systems. In this paper, we disruptively use reinforcement learning to model each agent of artificial intelligence, and explore the interplay between wireless communications and multi-agent systems. Autonomous vehicles navigating over Manhattan streets serve the illustrating system. The first new finding is the need to modify reinforcement learning of policy exchange due to getting information from other agents through wireless communication. The advantage of applying wireless communication is clearly observed. We also demonstrate the impacts of communication errors to result in penalty in system performance. Multi-agent systems equipped with direct vehicle- to-vehicle communication and vehicle-to-infrastructure communication are compared to initially conclude favorable using infrastructure of small cells. Finally, we explore multiple access communication over multi-agent systems by employing real- time ALOHA. Different from traditional thinking on reliable delivery of packets using re-transmit after collisions, real-time ALOHA discards re-transmission mechanism to ensure in-time contributions from wireless communication on the learning algorithm of a multi-agent system with satisfactory performance.

Byzantine General Problem (BGP) is well known to assist robust design of distributed multi-agent systems (MAS). Although agents of wireless networking capability are common in state-of-the-art Internet of Things or cyber-physical systems, wireless networking has been hardly considered into BGP research. In this paper, we re- visit the BGP by constructing a new game theoretic framework to model agents' interactions to achieve consensus strategy by taking network topology into consideration. Then we analyze the game strategies based on incomplete information, particularly partially connected network topology that is common in a large-scale wireless network, rather than system control of fully connected network topology in traditional BGP. We further demonstrate the application of BGP to the consensus operation in a group of intelligent agents. The numerical results show that our game theoretic approach is more effective and robust in each agent's decision performance, particularly advantageous in the MAS of large-scale and partially connected network such as a platoon of autonomous driving vehicles or robots.

Based on wireless network virtualization, radio access network (RAN) slicing is developed to provide services for the different users' requirements. Moreover, the users' sum data rate and delay are two significant metrics to guarantee quality of services. In this paper, we first establish an optimization problem to maximize the downlink sum rate while guaranteeing users' delay for RAN slices, where the base stations and user equipments are randomly distributed. Then we analyze the performance tradeoff between the sum rate maximization and delay tolerance. With the aid of Lyapunov optimization, the upper bounds of the achievable rate and delay are derived, through which the existence of tradeoff in performance is obvious and verified by numerical results.

Massive deployment of autonomous vehicles, un- manned aerial vehicles, and robots, brings in a new technology challenge to establish ultra-low end-to-end latency mobile networking to enable holistic computing mechanisms. With the aid of open-loop wireless communication and proactive network association in vehicle-centric heterogeneous network architecture, anticipatory mobility management relying on inference and learning from big vehicular data plays a key role to facilitate such a new technological paradigm. Anticipatory mobility management aims to predict APs to be connected in the next time instant and in a real-time manner, such that ultra-low latency downlink open-loop communication can be realized with proactive network association. In this paper, we successfully respond this technology challenge using big data analytics with location-based learning and inference tech- niques, to achieve satisfactory performance of predicting APs. Real vehicular movement data have been used to verify that the proposed prediction methods are effective for the purpose of anticipatory mobility management and thus ultra-low latency mobile networking.

Traditional pricing of a cellular network operator considers the bandwidth utilization for users and operational cost. In this paper, we re-examine this critical subject through a holistic engineering view taken energy efficiency into account. We treat this issue as a two-stage Stackelberg game between the operator and mobile users, where the operator determines the price of unit bandwidth, and accordingly the users choose the amount of requested bandwidth to maximize their payoff. To reach a general result, randomly deployed base stations in large scale have been taken into consideration. By stochastic geometry, we derive the operator's profit and the energy efficiency per unit area, while considering the actual bandwidth demand of users based on their acceptable price. Simulation results show that the operator's profit and the energy efficiency can be achieved their maximum at nearly the same price (i.e., optimal price), and the optimal pricing strategy varies with the deployment density of base stations.

Wireless-powered cellular networks (WPCNs) emerge as a promising technology to satisfy the sufficiency of available energy at mobile devices. In WPCNs, a mobile device is charged from energy stations called power beacons (PBs) by microwave radiation, and it can harvest energy from ambient radio frequency (RF) of base stations etc., which suggests an energy efficient way for communications. In this paper, we study an analytical model of the WPCNs with randomly deployed power beacons (PBs). The impacts of users' data rate requirements on both uplink and downlink transmissions are characterized, where uplink users capture energy from both PBs and BSs to maintain their transmit power. Considering the maximum allowable transmit power (MATP) of mobile device, we derive the successful transmission probability of an uplink cellular user who intends to harvest enough energy to transmit data meeting its quality of service (QoS) requirement. In addition, different modes of PBs are investigated in terms of the distributions of harvested energy, where a PB can either radiate energy isotropically or directionally towards users resorting to beamforming, called isotropic mode or directed mode, respectively. Numerical results validate our theoretical analysis and provide design insights to the WPCNs.

Forming small virtual cells by using open-loop radio communications and proactive network association has been recently shown effective to realize the ultra-low latency vehicular networking. However, multiple access emerges as another technical challenge, particularly in the downlink losing perfect synchronization for proactive radio access. Traditional beam-forming and interference alignment (IA) to suppress intercell interference could not satisfy ultra-low latency vehicular networking to enable autonomous vehicles due to the difficulty of obtaining channel state information (CSI) in open-loop communications. Multiuser detection (MUD) shall be employed in the downlink to accommodate interference from multiple co-existing virtual cells. By looking into bit-error rate (BER) in terms of power and modulation order of interference, we note that the performance gap between maximum likelihood-MUD (ML-MUD) and ideal single-user detection (SUD) heavily depends on the modulation schemes adopted by interferers. This modulation sensitivity can be easily relieved by introducing multi-antenna technique, which suggests robustness against multiple-access interference by diversity.

Ultra-low latency communication serves a pillar technology of 5G mobile communications and still requires breakthroughs to successfully facilitate. Very recent efforts to integrate virtual cell with open-loop wireless transmission, proactive network association, and anticipatory mobility management suggests a fundamentally new approach to achieve ultra-low latency vehicular networking. However, realistic multiple access in this configuration is very much wanted, particularly in the downlink. Using beam-forming or interference alignment to suppress inter-cell interference is not suitable for this scenario because channel state information (CSI) feedback is disadvantageous to ultra-low latency applications. Multiuser detection (MUD) is suggested to deal with downlink interference. A major concern in MUD is computational complexity especially for high-order modulation. Exploiting the characteristic of downlink transmission, we happily note the tradeoff between bit error rate (BER) performance and detection complexity to practically develop low-complexity MUD in the downlink receiver. Applying sphere decoding realizes further reliability and even lower latency in the downlink.

Recently, Internet-of-Things (IoT) applications have been moving toward a network of intelligent objects with social capabilities, defined as the Social IoT (SIoT). Different types of relationships exist among things, thus forming social connections such as parental-object relationship (POR), co-work-object relationship (CWOR), co-location-object relationship, ownership-object relationship, and so on.

The use of unmanned aerial vehicles (UAVs) serving as aerial base stations is expected to become predominant in the next decade. However, in order, for this technology, to unfold its full potential, it is necessary to develop a fundamental understanding of the distinctive features of air-to-ground (A2G) links. As a contribution in this direction, this paper proposes a generic framework for the analysis and optimization of the A2G systems. In contrast to the existing literature, this framework incorporates both height-dependent path loss exponent and small-scale fading, and unifies a widely used ground-to-ground channel model with that of A2G for the analysis of large-scale wireless networks. We derive analytical expressions for the optimal UAV height that minimizes the outage probability of an arbitrary A2G link. Moreover, our framework allows us to derive a height-dependent closed-form expression for the outage probability of an A2G cooperative communication network. Our results suggest that the optimal location of the UAVs with respect to the ground nodes does not change by the inclusion of ground relays. This enables interesting insights about the deployment of future A2G networks, as the system reliability could be adjusted dynamically by adding relaying nodes without requiring changes in the position of the corresponding UAVs. Finally, to optimize the network for multiple destinations, we derive an optimum altitude of the UAV for maximum coverage region by guaranteeing a minimum outage performance over the region.

Social networks have attracted billions of users and supported a wide range of interests and practices. Users of social networks can be connected with each other by different communities according to professions, living locations, and personal interests. With the development of diverse social network applications, academic researchers, and practicing engineers pay increasing attention to the related technology. As each user on the social network platforms typically stores and shares a large amount of personal data, the privacy of such user-related information raises serious concerns. Most research on privacy protection relies on specific information security techniques such as anonymization or access control. However, the protection of privacy depends heavily on the incentive mechanisms of social networks, like users' psychological decisions on security execution and socio-economic considerations. For example, the desire to influence the behaviors of other people may change a user's choice of security setting. In this paper, a game theoretic framework is established to model users' interactions that influence users' decisions as to whether to undertake privacy protection or not. To model the relationship of user communities, community-structured evolutionary dynamics are introduced, in which interactions of users can only happen among those users who have at least one community in common. Then the dynamics of the users' strategies to take a specific privacy protection or not is analyzed based on the proposed community structured evolutionary game theoretic framework. Experiments show that the proposed framework is effective in modeling the users' relationships and privacy protection behaviors. Moreover, results can also help social network managers to design appropriate security service and payment mechanisms to encourage their users to take the privacy protection, which can promote the spreading of privacy behavior throughout the network.

In the heterogeneous cloud radio access network (H-CRAN), which consists of multiple access points (APs) providing smaller coverage and a high power node (HPN) providing ubiquitous coverage, the mobile machines can connect to multiple APs and HPN by coordinated multi-point transmission (CoMP) concurrently to achieve ultra-reliable and low-latency communication. However, the current network association (or priorly known as handovers), which only focuses on switching between two base stations, may not be an efficient scheme in H-CRAN. In this paper, we innovate a proactive network association mechanism by taking CoMP into consideration under the H-CRAN architecture. We consider two scenarios under the H-CRAN architecture: with and without the assistance of HPN in the network. By regarding APs/HPN in H-CRAN as resources that allocated to mobile machines, a novel proactive network association concept is proposed, and then generalized from one-to-one to multiple-to-multiple case. With the assistance of Lyapunov optimization theory, effective bandwidth, and capacity theory, we can prove that this proactive network association scheme can guarantee that the queueing delay performance and the delay violation probability can be both smaller than a corresponding upper bound. That is, both low-latency and ultra-reliable communication can be guaranteed. We also conduct experiments by using real trace from taxis movement data to verify the analytical results. Our results suggest the guidelines to design the proactive network association scheme in H-CRAN.

Traffic offloading via device-to-device (D2D) communications has been proposed to alleviate the traffic burden on base stations and to improve the spectral and energy efficiency of cellular networks. The success of D2D communications relies on the willingness of users to share contents. In this paper, we study an economic aspect of traffic offloading via content sharing among multiple devices and propose an incentive framework for D2D assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency (ECE), by encouraging users to act as D2D transmitters (D2D-Txs) which broadcast their popular contents to nearby users. We analytically characterize D2D-assisted offloading in cellular networks for two operating modes: 1) underlay mode and 2) overlay mode. We model the optimization of network ECE as a two-stage Stackelberg game, considering the densities of cellular users and D2D-Txs, the operator's incentives, and the popularity of contents. The closed-form expressions of network ECE for both underlay and overlay modes of D2D communications are obtained. Numerical results show that the achievable network ECE of the proposed incentive D2D-assisted offloading network can be significantly improved with respect to the conventional cellular networks, where the D2D communications are disabled.

Modeling online opinion dynamics plays an important role toward in-depth comprehension of collective interactive behavior in modern human society and future digital society. Some statistical models based on network science or social networks have been known for years. In this paper, we propose a novel network model of online forums. Different from the existing models in which model selection is obscure, we accommodate reasoning in both mathematical and psychological contexts such that the basic parameters of the network model can be identified in an intuitive way. The proposed first-order-aging-with-fitness (FOAF) model extends from the well-known BA model, by adjusting weights for younger nodes, to better reflect the truth of Internet forums. We successfully verify the FOAF model and consequent entire social network model of wider applicability and better alignment with real online opinion data.

This paper presents a framework for pursuing lowlatency communication among V2V networks underlaying V2I networks. To achieve low-latency communication, solely relying on the improvement of the air-interface may not be enough. To cope with the highly dynamic environment of vehicular networks, a time dynamic optimization approach is proposed that improves the latency performance through not only optimization of spectrum resources but also by constraining the network switching rate. To further decrease the complexity of the time dynamic optimization problem, we convert the original problem to a deterministic optimization problem through the Lyapunov Optimization Theory. The proposed algorithm becomes a more suitable scheme for the vehicular network. Analytical results show that the proposed scheme can approach the best tradeoff between the latency performance and the network switching rate. Simulation results are provided to verify the proposed algorithm.

Traffic offloading via device-to-device (D2D) communications has been proposed to improve the network capacity and alleviate the increasing traffic burden on cellular base stations (BSs). However, the success of D2D communications largely relies on the D2D transmitters' (D2D-Txs) willingness of sharing contents (due to the energy consumption for transmission). In this paper, we model and analyze wireless powered D2D-assisted offloading (WPDO) in underlying cellular networks, where the D2D-Tx is allowed to receive power from the nearest BS as well as other interfering BSs, and then D2D-Tx broadcasts the popular contents to nearby users. The average received power at D2D-Tx and the success probability of D2D-Tx transmission are derived. Furthermore, based on the proposed model, we maximize the network energy efficiency while guaranteeing users' required data rates. Our results confirm that the maximum energy efficiency of the WPDO network can be achieved by jointly optimizing the fraction of time for wireless power transfer and the offloading range of D2D-Tx.

Recently, a revolutionary effort to seek fundamental improvement of 802.11, known as IEEE 802.11ax, has been approved to deliver high efficiency wireless local area network (HEW) technologies for dense scenario. The de-facto random access mechanism of IEEE 802.11ax is uplink OFDMA-based random access (UL OFDMA RA) mechanism. In this paper, we provide a simple but accurate analytical model to study the system efficiency and delay performance of UL OFDMA RA under saturated condition, in the assumption of a finite number of stations and ideal channel conditions in a single-hop WLAN. Simulations are conducted to validate analytical results. In addition, we use this analytical model to identify the appropriate OFDMA-based random access parameter set (RAPS) that can maximize the system performance, a feature which is essential for system design and configuration.

Cognitive radio ad hoc networks (CRAHNs) emerge as a spectrum efficient networking technology to enable autonomous machine-to-machine communication among massive number of IoT devices. However, spectrum sharing results in opportunistic links and CRAHN becomes a kind of opportunistic networks. To reduce latency in CRAHN and to achieve overall spectrum efficiency by avoiding tremendous feedback signaling, CRAHNs of open-loop physical layer transmission open a new avenue under massive operations. The new technology challenge associated with such new CRAHNs lies in error control with only local networking information without relying on feedback control over each opportunistic link. Path-time codes virtually realizing multi-input-multi-output over network layer have been innovated to resolve such a dilemma. However, effective multipath routing considering interference remains unclear. In this paper, be taking network topological factors and interference into account, we analytically derive SINR approximations to design power control and multi-path greedy routing. By stochastic geometry analysis, we also show that the resilient operation for large-scale CRAHNs can be facilitated with the aid of path-time codes.

LTE-Unlicensed (LTE-U) is considered as a groundbreaking technology to address the increasing scarcity of available spectrum by extending cellular communications to unlicensed band. In this paper, we investigate the performance of D2D communications in conjunction with LTE-U, which can alleviate traffic load of cellular networks. However, in the same unlicensed band, the coexistence of D2D and WiFi technologies should be carefully designed to satisfy user's quality of service (QoS) and to avoid severe interferences and contentions among devices using unlicensed spectrum. We model the transmissions in unlicensed band as hard core point processes (HCPPs) and thus the transmission probabilities of D2D and WiFi access points (APs) are obtained via the clear channel assessment (CCA) mechanism. Furthermore, by characterizing the intra-tier and inter-tier interferences in such complex communication networks, the average transmit power for the D2D link is investigated given that the user's QoS can be guaranteed. Moreover, the throughput of a typical WiFi AP in the large scale networks is theoretically analyzed, and the outage probability of a D2D link is characterized which results from insufficient transmit power under a pre-determined QoS requirement. Simulations justify successful D2D communications in the LTE-U operation and validate the accuracy of this analytical approach.

How to improve the information diffusion coverage rate in socially-aware mobile networks has drawn great attention. To address this issue, the concept of the tie strength, the partial strength and the value strength were proposed in order to achieve a superior criterion for information diffusion. However, the previous works did not consider the existence of various patterns among the nodes in socially-aware mobile networks. In this paper, we propose the asymmetric normalization forms of the partial strength as well as the value strength. Moreover, we explore the essence of asymmetry and its influence on information diffusion relying on analyzing the characteristics of graph structures as well as information local traps. Simulation results on the real-world social network and on the mobile network verify that our proposed asymmetric normalization forms are beneficial to promoting information diffusion.

Autonomous vehicles (AVs) and crowdsourcing big data analytics may dramatically change future intelligent transportation in smart cities. AVs may evolve more like a service than a product. To provide best user experience of such service, three primary factors including waiting time, travel time, and supply of AVs are taken into consideration to for multi-objective optimization facilitated in three steps. With the queueing network model and traffic flow analysis, optimal operation of service could be achieved with minimum average travel time. The optimal number of available AVs could be identified while guaranteeing the waiting time of customers. To manage the supply and demand of the service in each geographical area, bipartite graph matching is adopted to accomplish optimal resource allocation. It is further shown that the optimization of operation of autonomous vehicle fleet can be successfully achieved, to outperform what human- driving vehicles can possibly do. A new intelligent transportation paradigm of great energy saving emerges.

Aerial communication platforms have been recently recognized as an effective solution to provide wireless access to terrestrial users, which promise to increase reliability and throughput thanks to their superior coverage capabilities. In this paper, we explore the impact of the height of an Unmanned Aerial Vehicle (UAV) on the area over which it can provide wireless service. We investigate the problem by characterizing the coverage area for a target outage probability, showing that for the case of Rician fading there exist a unique optimum height that maximizes the coverage area. The optimum UAV height guarantees a beneficial trade-off between path loss and fading, which vary as function of distance and the elevation angle with respect to the ground terminals. Moreover, a closed-form approximated solution is provided, which is valid for any functional dependency between the elevation angle and the Rician factor.

Optimization of resource allocation is fundamental to implement orthogonal frequency division multiple access (OFDMA), which energy efficiency (EE) is very much desired. Wireless network virtualization recently emerges to flexibly enable high data rate services, but optimal cross-layer design with OFDMA remains open. While energy efficiency (EE) is a critical issue for OFDMA systems, the appropriate energy efficient design to enable emerging virtualization in wireless OFDMA networks remains open. In this paper, we focus on energy efficient subcarrier and power allocation with wireless network virtualization. To minimize power consumption and to maximize data rate, such virtual resource allocation can be modeled as a non-convex optimization of EE. Exploiting the non-convex fractional programming, we reformulate into a subtractive form as the equivalent optimization.We therefore obtain the iterative algorithm to achieve the optimality. In particular, subcarrier allocation, power allocation, and operator selection in each iteration can be derived by the Lagrange dual decomposition method. Simulations verify that the algorithms can significantly enhance EE in wireless network virtualization architecture.

Principal Component Analysis (PCA) is adopted in diverse areas including signal processing and machine leaning. However, the derived principal components, the linear combinations of the original variables, are hard to be interpreted in many applications especially the blind source separation. Therefore, we propose regularized PCA via hard thresholding such that the derived loadings are sparse and easier to be interpreted. The proposed method has advantages due to the adoption of hard thresholding. First, the proposed method can be implemented by linear operators and thus computational efficient even in p ≫ n or large p scenarios. Second, the threshold can be objectively selected based on statistical decision theory without domain knowledge. Moreover, simulations show the superiority of our method compared to the L1-penalized method. Therefore, our approach can be a strong competitor of the existing sparse PCA.

Statistical learning has attracted considerable recent research interest due to the wide-ranging demands of big data analytics. The recent introduction of communication theory and information coupling theory into this area suggests a new perspective on statistical learning and inference for data analytics. This paper investigates inference of one data variable from heterogeneous data variables, a problem that plays an increasingly important role in the emerging applications of big data analytics. To generalize the existing conceptual approach, information coupling filtering under hidden data structure or unknown knowledge of interactions among data variables is developed. A least-mean-squares (LMS) filtering approach for non-stationary data similar to an equalizer is suggested, while the training data gives the depth of the filter analogously to model selection in learning theory. The information combining in diversity communication is extended to fuse more data variables for even greater precision of inference. Extending from multiuser detection, an algorithm based on Multiple Signal Classification (MUSIC) is demonstrated to identify useful data variables for inference, as a novel solution to knowledge discovery. A series of examples illustrate the effectiveness of this framework, suggesting that statistical communication theory and statistical signal processing can substantially contribute to statistical learning theory.

Information cascade characterizes the situation where rational agents are overloaded with social observations and can make decisions ignoring their own personal evidence. If the social observations are misleading, the overall system performance can be subsequently compromised. Therefore, it is of interests to design a socially or interactively networked system falling into undesirable or desirable cascades. In this paper, we study the driving factors of steering information cascades in a socially networked system by considering a sequential Bayesian social learning model with arbitrary network topology and utility function. In particular, we analyze the impacts from different random network topologies on the overall system performance such as error probability, fraction of cascades, and total utility. Furthermore, we demonstrate two systematic approaches, selective rewiring and incentive seeding, which can change the information structure observed by agents and hence steer information cascades. Our results show that when agents restructure their observation network and provide proper incentives for cascades in the correct decisions, the overall social system performance can be well-steered.

To facilitate content distribution and diffusion, efficacious caching strategy plays an important role in content dissemination control, especially in dynamic mobile social networks (MSNs), where contents spreading and accessing rely mainly on opportunistic contacts in physical proximity. Since content dissemination much resembles epidemic dynamics, two caching control schemes, caching at external BS and cooperative innetwork caching, are investigated to assay the system behaviours and performance via epidemic dynamics. When time dynamic is considered, we provide a more realistic scenario where the cost of caching is related to the time duration of caching; hence optimal control theory are exploited to determine the optimal caching time for the content spreading. Moreover, we provide proactive caching analysis as a preventive system response to handle severe outbreak of the epidemic content, which would often cause instantaneous service burden in the system. Finally, virality is shown to be an important content feature when implementing caching. This research, from the aspect of system dynamics, paves novel avenues to content dissemination and caching utilization in mobile social networks.

The problems of extracting information from a data set via histogram queries or arithmetic mean queries are considered. We first show that the fundamental limit on the number of histogram queries, m, so that the entire data set of size n can be extracted losslessly, is m = Θ(n/log n), sub-linear in the size of the data set. For proving the lower bound (converse), we use standard arguments based on simple counting. For proving the upper bound (achievability), we proposed two query mechanisms. The first mechanism is random sampling, where in each query, the items to be included in the queried subset are uniformly randomly selected. With random sampling, it is shown that the entire data set can be extracted with vanishing error probability using Ω(n/log n) queries. The second one is a non-adaptive deterministic algorithm. With this algorithm, it is shown that the entire data set can be extracted exactly (no error) using Ω(n/log n) queries. We then extend the results to arithmetic mean queries, and show that for data sets taking values in a real-valued finite arithmetic progression, the fundamental limit on the number of arithmetic mean queries to extract the entire data set is also Θ(n/log n).

To achieve low delay for vehicular communication in cellular networks, the use of direct device-to-device (D2D) communication among vehicles is regarded as a key functional requirement. When D2D users share the spectrum with regular cellular users (D2D underlay), resource allocation schemes assure the coexistence between D2D and cellular users. Due to the high mobility of the vehicles, the resources need to be reallocated, but frequent updates cause high signaling overhead and degrade the delay performance. In this paper, we present a radio resource re-allocation scheme for vehicular D2D users in a platoon scenario. The scheme reduces the re-allocation rate and gives delay guarantees for each vehicle.With the help of Lyapunov optimization, a closed form of the upper delay bound and resource re-allocation rate is also derived. The simulation results show that the proposed scheme can provide an delay upper bound and simultaneously minimizes the resource re-allocation rate.

Device-to-Device (D2D) communication is expected to satisfy the rapidly increasing capacity, and it can also alleviate the burden of base stations (BSs) by offloading onto direct links in a 5G mobile system, which can support high-speed data rate for local users and provide power-saving services, while enhancing the energy efficiency (EE) of the global network. Therefore, we model the EE of global network underlaid or overlaid D2D direct communications, where the explicit relationships between EE and the offloading strategy radius are signified by quantifying various network parameters (i.e., density of BSs and users, data-rate and system bandwidth, etc). More importantly, we analytically comprehend the EE and user's average transmission power in both D2D modes, that is, underlay and overlay. Furthermore, offloading probability of cellular users and active probability of D2D transmitters are analytically obtained. Simulations are carried out and show that global network EE can be significantly improved by using D2D communication. Moreover, in overlay mode, when the D2D bandwidth is same with underlay mode, users consume less power for transmission, because the inter-tier interference is eliminated at the price of saving gain on the energy and spectrum as the total bandwidth becomes larger.

Machine-to-machine (M2M) communication enables many applications such as smart grid, vehicular safety, and health care among many others. To achieve ubiquitous data transportation among objects and the surrounding environment, deploying spectrum sharing M2M communications with existing wireless networks is a must. A general large-scale cognitive M2M network (CM2MN), adopting cognitive radio technology, consists of multiradio systems, the primary system (PS), and secondary system(s) with tremendous cooperative cognitive machines, under heterogeneous wireless architecture. For these CM2MNs, due to dynamic spectrum access (DSA) nature, there exists possibly unidirectional opportunistic wireless fading links and thus traditional flow control mechanisms at link level do not fit anymore. Furthermore, effective end-to-end quality-of-service (QoS) control is still required to provide a reliable transportation for such multihop CM2M communications. Facing the above challenges, we propose a novel statistical QoS control mechanism through cooperative relaying, realizing virtual multiple-input and multiple-output (MIMO) communicationsat session level. In particular, a probabilistic network coded routing algorithm and the statistical QoS guarantee are first proposed to coordinate and cooperate tremendous machines. Next, based on the proposed guarantee and routing algorithm, the statistical QoS control mechanism is designed to enable MIMO communications for the session traffic. Specifically,the diversity modeis used to deal with PS’s opportunistic nature and wireless fading, andthe spatial multiplexing modeis employed to obtain the maximum end-to-end throughput. Simulation results confirm that under our control solution, the great improvements of end-to-end delay violation probability are obtained, thus practically facilitating network coded multipath routing in large CM2MNs.

Networks formed by energy-harvesting devices impose new technological challenges on data transmission due to uncertainty of the amount of energy that can be harvested. This is even more challenging with multihop networks, in which transmission outage occurs when one single device along the relay path cannot harvest enough energy. A virtual multiple-input multiple-output (MIMO) model for multihop, multipath networks has recently been developed to facilitate reliable open-loop end-to-end transmissions, making networks robust to random transmission outages without the necessity of bandwidth-consuming and complicated end-to-end feedback and control. In this paper, a framework based on a virtual MIMO model of multihop, multipath opportunistic networks formed by energy-harvesting devices is developed. We propose rotated-algebraic path-time codes (RA-PTC), by which data are encoded using Givens rotation and cyclic division algebras. Without rate loss, a form of time diversity is exploited by repeatedly transmitting the RA-PTC-coded data. Extensive theoretical analyses are carried out using amount of fading and diversity as metrics. Both the performance enhancement due to the proposed RA-PTC and the performance loss due to energy shortage are quantified. Furthermore, a simple yet effective cyclic power control is proposed to improve transmission reliability. Numerical results demonstrate that RA-PTC and cyclic power control enable efficient and reliable end-to-end transmission in multipath, multihop opportunistic networks formed by energy-harvesting devices.

Deploying spectrum sharing machine-to-machine (M2M) communications with the existing wireless networks achieves ubiquitous data transportation among objects and the surrounding environment to benefit our daily life. However, the lack of schemes to completely characterize M2M network topology, to efficiently share radio resource, and to provide quality-of-service (QoS) guarantee regarding end-to-end delay creates challenges to practically facilitate M2M communications. Via mathematical derivations, the network connectivity, degree distribution, and average distance are provided for large M2M networks. To achieve reliable communications upon such M2M networks, inspired by cognitive radio technology and cooperative communications, acognitive and opportunistic relay(COR) scheme is proposed. Specifically, machines with the proposed COR autonomously sense the primary systems’ spectrum usage so as to mitigate detractive interference and adopt opportunistic forwarder selection for lower link delay of packet transmissions. Furthermore, by analytical deriving the effective capacity of the COR over connected M2M networks, the throughput under statistical QoS guarantee and the corresponding delay violation probability are proposed to specify the QoS guarantee capability of the networks and thus suggest the conditions of dependable end-to-end transmissions. Simulation results confirm that the proposed COR effectively achieves the delay guarantee performance, to yield a novel framework for facilitating reliable M2M communications in large machine networks.

Outlier detection is a frequently encountered technology challenge for many diverse applications in sensor networks, and remains an open problem in general. There are two major difficulties of developing outlier detectors with sensor data. One is the inevitable multi-source identification, the other is the effective inference when discovering information from unknown structured large-scale data. It is even more interesting and challenging with limited observation, since conventional data analysis requires many samples to achieve a satisfactory performance. In this paper, we systematically develop effective and efficient outlier identifiers in parametric and non-parametric ways using shrinkage methodology, like the James-Stein estimator, as the post-processor. We show the superiority of our approach, particularly for the large-scale situations. We further supply a water- filling type algorithm to obtain the asymptotic optimal method for a general class of shrinkage estimators, for wide applications of data analysis.

Prediction based on observed data is one of the major purposes in (big) data analytics, and has shown great impacts in many applications, including engineering, social science, and medical treatments. Statistical machine learning has been widely adopted to deal with such problem. In this paper, we analogize the relationship among data variables as a sort of generalized social network [1], that is, networked data. Consequently, a direct causal relationship from one data variable to another is thus equivalent to information transfer over a communication channel. Prediction based on data variables is consequently to maximize utilizations of information conveyed over communication channels. Therefore, we introduce the concept of adaptive equalization to data analytics in this paper, which allows us to select appropriate data variables and optimum depth of observations for prediction. We illustrate by finance market data to show surprisingly good performance using this simple methodology. This result not only indicates a new direction to knowledge discovery and inference in big networked data analytics based on communication theory, but also shows the consistency with the newly developed information coupling.

Systematically analyzing the dynamic behaviors of social networks is one of the central topic in understanding the structure of large networks. In particular, the information cascade [1] introduced by Banerjee provides great insights in characterizing the opinion exchanging between network agents. Traditionally studies of information cascades focus on the Bayesian models, which are often difficult to model real world situations. In this paper, we attempt to study the information cascades from a non-Bayesian point of view. In particular, we consider a sequential decision model but with an arbitrary decision rule. We show that the fraction of agents in a network making any specific decision will converge. Thus, the agents in the network reach a sort of consensus with high probability, which allows us to predict the herd behaviors. In addition, we also apply our non-Bayesian model to different network structures, such as ER model and network with communities, in which the affect of information cascades are quantified. Finally, we simulate the decision process for multiple communities, which justifies our proposed model to comprehend real world complex user behaviors and dynamics.

In the following decades, traffic volumes and the number of devices are projected to increase a ten to thousand-fold. Unfortunately, existing cellular networks adopting closed-loop communications impose too large spectrum overheads, which lead to an unacceptably low spectrum efficiency and unaffordable traffic burdens. To support the extremely challenging and unavoidable massive data exchanges in 2020 and beyond, we fundamentally re-consider an efficient scheme of the fully packet-switching radio access, that is, open-loop communications. To eliminate the concerns on the capabilities of enhancing the spectrum efficiency and reliability to support multimedia transmissions via open-loop communications, in this paper, we develop the optimum transmission repetition scheme to maximize the resource utilization while providing quality-of-service (QoS) guarantees. Our results confirm the efficiency, effectiveness, and practicability of the fully packet-switching radio access as compared with existing closed-loop communications, which suggest a revolutionary system design as the foundations for the fifth generation (5G) networks.

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data are modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data are used to demonstrate the advantages of this approach. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness of this formalism. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.

Providing direct communications among a rapidly growing number of wireless devices within the coverage area of a cellular system is an attractive way of exploiting the proximity among them to enhance coverage and spectral and energy efficiency. However, such device-to-device (D2D) communications create a new type of interference in cellular systems, calling for rigorous system analysis and design to both protect mobile users (MUs) and guarantee the connectivity of devices. Motivated by the potential advantages of cognitive radio (CR) technology in detecting and exploiting underutilized spectrum, we investigate CR-assisted D2D communications in a cellular network as a viable solution for D2D communications, in which devices access the network with mixed overlay-underlay spectrum sharing. Our comprehensive analysis reveals several engineering insights useful to system design. We first derive bounds of pivotal performance metrics. For a given collision probability constraint, as the prime spectrum-sharing criterion, we also derive the maximum allowable density of devices. This captures the density of MUs and that of active macro base stations. Limited in spatial density, devices may not have connectivity among them. Nevertheless, it is shown that for the derived maximum allowable density, one should judiciously push a portion of devices into receiving mode in order to preserve the connectivity and to keep the isolation probability low. Furthermore, upper bounds on the cellular coverage probability are obtained incorporating load-based power allocation for both path-loss and fading-based cell association mechanisms, which are fairly accurate and consistent with our in-depth simulation results. Finally, implementation issues are discussed.

The articles in this special issue focus on new and emerging technologies in the communications industry.

This paper considers a cognitive radio network where the licensed network, referred to as the primary user (PU) network, consists of a hierarchical structure in which multiple operators coexist in the same coverage area where each of the operators controls an exclusive set of frequency sub-bands. Unlicensed users, referred to as the secondary users (SUs), first send their requests to the operators, and can only access the sub-bands controlled by the operators that accept their requests. SUs are selfish and cannot exchange private information with each other. We model the dynamic spectrum access (DSA) problem of the SUs as a Bayesian game, referred to as the DSA game. We model the PU network as a forest where the roots represent the operators and the leaves represent the operators' sub-bands. We propose a novel forest matching market to model the interaction between the SUs and the PU network. In this market, a set of SUs can be first matched to a set of operators and the SUs matched to the same operator can then be matched to the corresponding sub-bands. We propose a distributed algorithm that results in a stable forest matching structure, which coincides with the optimal Bayesian Nash equilibrium of the DSA game. We prove that the Bayesian hierarchical mechanism associated with our proposed algorithm incentivizes truth-telling by SUs. Our algorithm does not require each SU to know the preference and conflict-solving rule of the PU network or the payoffs and actions of other SUs, and the complexity of each iteration in the worst case is given by O(L2N2K) where L is the number of operators, N is the maximum number of sub-bands of each operator, and K is the number of SUs.

Cognitive radios (CRs) improve the spectrum efficiency in wireless communications. Nonetheless, owing to the intrinsic randomness of ad hoc cognitive radio networks (CRNs), e.g., the opportunistic links, the traditional realization of ad hoc networking that demands the end-to-end control information, is unscalable and impractical. A virtual multiple-input multiple-output (MIMO) framework has been recently developed for the realization of error-resilient end-to-end transmission without the necessity of feedback information. In this paper, we propose an end-to-end path-permutation coded (PPC) transmission in which one relay path is accessed at a time and the transmission is hopped among multiple relay paths. The hopping order is specified by a permutation array that encodes the data, meaning that the data is conveyed by the order of indices of the accessed paths. With the PPC scheme, the control overhead and data processing complexity of the end-to-end transmission become relatively low. The PPC technique can also be utilized as a multiuser technique. At the destination node, a joint sphere decoder efficiently implements the maximum a posteriori (MAP) probability decoding that simultaneously identifies the order of accessed paths and erasures. Comprehensive theoretical analyses and simulations are conducted to demonstrate the superior performance of the PPC technique in ad hoc CRNs.

Cloud based machine-to-machine (M2M) communications have emerged to achieve ubiquitous and autonomous data transportation for future daily life in the cyber-physical world. In light of the need of network characterizations, we analyze the connected M2M network in the machine swarm of geometric random graph topology, including degree distribution, network diameter, and average distance (i.e., hops). Without the need of end-to-end information to escape catastrophic complexity, information dissemination appears an effective way in machine swarm. To fully understand practical data transportation, G/G/1 queuing network model is exploited to obtain average end-to-end delay and maximum achievable system throughput. Furthermore, as real applications may require dependable networking performance across the swarm, quality of service (QoS) along with large network diameter creates a new intellectual challenge. We extend the concept of small-world network to form shortcuts among data aggregators as infrastructure-swarm two-tier heterogeneous network architecture, then leverage the statistical concept of network control instead of precise network optimization, to innovatively achieve QoS guarantees. Simulation results further confirm the proposed heterogeneous network architecture to effectively control delay guarantees in a statistical way and to facilitate a new design paradigm in reliable M2M communications.

We consider the spectrum sharing problem between a set of device-to-device (D2D) links and multiple co-located cellular networks. Each cellular network is controlled by an operator which can provide service to a number of subscribers. Each D2D link can either access a sub-band occupied by a cellular subscriber or obtain an empty sub-band for its exclusive use. We introduce a new spectrum sharing mode for D2D communications in cellular networks by allowing two or more D2D links with exclusive use of sub-bands to share their sub-bands with each other without consulting the operators. We establish a new game theoretic model called Bayesian non-transferable utility overlapping coalition formation (BOCF) game. We show that our proposed game can be used to model and analyze the above spectrum sharing problem. However, we observe that the core of the BOCF game can be empty, and we derive a sufficient condition for which the core is non-empty. We propose a hierarchical matching algorithm which can detect whether the sufficient condition is satisfied and, if it is satisfied, achieve a stable and unique matching structure which coincides with the overlapping coalition agreement profile in the core of the BOCF game.

Cognitive radio ad hoc network (CRAHN) is considered as a key technology to enhance the spectrum efficiency for diverse applications. However, due to the opportunistic links, the intrinsic randomness of the CRAHN makes the traditional precise control of the end-to-end transmission unscalable and generally infeasible. The recently-proposed virtual multiple-input multiple-output (MIMO) framework exploits the multipath routing to create the diversity at the network layer. With only local information and no feedback control channel, the path-time code (PTC) of the virtual MIMO system is able to efficiently provide the error resilient end-to-end transmission. In particular, while the transmission latency should be minimized, more attempts for accessing the opportunistic links can be made if a larger latency is allowed, which improves the error rate performance of the end-to-end transmission. By theoretically analyzing the error rate performance and erasure statistics, we propose a design guideline to determine the waiting period as the limit of the transmission latency. This designed waiting period not only preserves the diversity gained by the PTC but also provides a low transmission latency, resulting in a good balance of the reliability-latency trade-off of the end-to-end PTC transmission in CRAHNs.

Random access has been widely studied in literature, but its time dynamics remains a pretty open research problem at this time, particularly for cognitive radio networks that are operating most in transient status but being investigated usually in steady-state. Modifying prey-predator model, we therefore consider radio resources as preys and users as predators to dynamically understand the network system behavior. We start from exploring ALOHA, then include the sensing mechanism into the scenario. Furthermore, we incorporate partially or randomly connected graph to practically represent realistic interactions among users and resources. By modeling sensing errors and delay, for the first time, the time dynamics of a cognitive radio network can be fully characterized, and consequently random access operating conditions can be practically understood and specified for network engineering design.

In this paper we address the problem of cognition (inference and decision) on data collected from a system which is characterized by a stochastic network topology. The decision problem is formulated as a data fusion problem and a data model that only requires stochastic network topology, marginal probability densities and pairwise correlations is proposed. Based on such model, we can mitigate the difficulties of inference resulted from random topological networks and simplify the decision making in many data cases. The numerical simulation justifies our idea and the performance is comparable to the case of decision making with complete information and decision making under conventional independent data cases when the size of network is not too large. Furthermore, an experiment of classification of real-world data is also conducted to illustrate the potential applications of cognition of networked data on social networks.

In this paper, we study the performance of the recently proposed class of path-time codes (PTCs), which exploit time and path diversity to increase the reliability of end-to-end transmission in ad hoc multihop networks. Using the central limit theorem, we prove that PTCs reshape the statistics of a relay path including multiple cascaded links. In particular, as the number K of relay paths becomes large, the cascaded channel statistic is transformed from K-product Rayleigh to Rayleigh. This is numerically demonstrated and analytically verified by using the concept of “amount of fading” (AF). Our results show that, by using PTCs, the end-to-end error-rate performance can be boosted in addition to the diversity and coding gains.

Recently, IEEE 802 started a task group to investigate and deliver next generation WLAN technologies for the scenarios of dense networks with a large number of stations and access point. The proposal is specified as the IEEE 802.11ax amendment. Due to the significant network capacity increase achieved by 802.11ax, the term high-efficiency WLAN (HEW) is also used in reference to this new amendment. This paper summarizes the IEEE 802.11ax standardization activities in progress and presents an overview of the most important features proposed in the 802.11ax amendment. Expected features and challenges for 802.11ax in the design of physical layer (PHY) and media access control sub-layer (MAC), toward a new era of wireless LANs, are also discussed.

Emerging multi-hop machine-to-machine (M2M) communications that likely support a large number of wireless devices create new challenges for spectrum scarcity and energy efficiency. In parallel to pursuing physical layer transmission efficiency, traffic compression to reduce required wireless transmissions suggests a new paradigm of wireless networks. Utilizing the natures of broadcasting and information collection in wireless sensor or machine networks, cognitive traffic compression can be facilitated by our proposed optimal fusion rules and topology compression algorithm. Therefore, only the necessary and connected sensors/machines in M2M networks are required to transmit, to achieve the desirable distortion of information collection (i.e. detection/estimation error). In other words, given the desirable distortion, the number of sensors to transmit, or equivalently the total energy consumption, serves our purpose of energy efficiency for end-to-end networking. Numerical results show successful compression of total network traffic to significantly enhance networking energy efficiency.

Femtocell base stations (FBSs) have been widely considered in the next generation cellular systems to enhance spectrum efficiency, but macro-femto and femto-femto interferences are still challenging issues to solve. In this paper, we consider the backhaul interface of FBSs to be home broadband connection (such as ADSL and optical fiber) with capacity lower than the air interface, and propose a novel resource optimization for FBSs to mitigate both the macro-femto and femto-femto interferences. First, the user rate requirements are normalized according to the backhaul capacity. Then, the total spectrum utilized by FBSs is minimized to alleviate the macro-femto interference. To reduce the femto-femto interference, we integrate the Gibbs sampler into the resource optimization to make each FBS select the frequency bands which have lower probability to introduce interference. Via sophisticated simulations based on the 3GPP femtocell evaluation scenario, the resource optimization is demonstrated to be effective and ready for LTE-A.

Robust network design against attacks is one of the most fundamental issues in Internet of Things (IoT) architecture as IoT operations highly rely on the support of the underlaying communication infrastructures. In this paper, the vulnerability of IoT infrastructure under intentional attacks is investigated by relating the network resilience to the percolation-based connectivity. Intentional attacks impose severe threats on the network operations as it can effectively disrupt a network by paralyzing a small fraction of nodes, and therefore deteriorating IoT operations. A fusion-based defense mechanism is proposed to mitigate the damage caused by such attacks, where each node feedbacks minimum (one-bit) local decision to the fusion center for attack inference. By formulating the attack and defense strategy as a zero-sum game, the outcome of the game equilibrium is used to evaluate the effectiveness of the proposed mechanism. The robustness of the Internet-oriented and the cyber-physical system (CPS)-oriented networks are specifically analyzed to illustrate the foundation of future IoT infrastructure. Both analytical and empirical results show that the proposed mechanism greatly enhances the robustness of IoT, even in the weak local detection capability and fragile network structure regime.

Molecular communication has been considered an emerging technology for both scientific and engineering interests, due to molecular biology and the need for nano-scale communication and computation systems. Molecular communication systems are widely analogue to electrical communication systems involving emitting molecules, diffused propagation of molecules, and reception of molecules, under stochastic characteristics of relatively slow propagation speed and short transmission range. To facilitate large-scale molecular information systems for the purpose of communication and computation, state-of-the-art explorations are generally assumed perfect alignment of timing among molecular devices and sub-systems. To reach synchronous condition in electronics, phase locked loop (PLL) is known to align timing or phase of waveforms. We extend PLL into molecular PLL (MPLL) consisting of basic elements such as molecular phase detector, molecular loop filter, and molecular voltage controlled oscillator. Due to stochastic nature of molecular diffusion, we further analyze MPLL in terms of the diffusion jitter, displacement, and the (particle or molecular) counting noise. Simulations verify the MPLL concept model, tracking performance, and robustness in operation.

Spectrum pooling has been shown to have a great potential to improve the spectrum utilization, especially when primary users (PUs) and secondary users (SUs) are allowed to utilize a common spectrum pool. This paper studies the joint optimization problem for a spectrum pooling system with both PUs and SUs. We develop a novel hierarchical game theoretic model which consists of an overlapped coalition formation game model to analyze the pricing cooperation/competition strategy among PUs and a non-cooperative game model to investigate the resource competition among SUs. These two game models are interrelated in a hierarchical game structure, in which we also study the interaction between SUs and PUs. Our model does not require SUs to have information about spectrum access scheduling of PUs. Furthermore, we propose a simple distributed joint optimization algorithm that can optimize the coalition formation of PUs as well as the sub-band allocation and transmit powers of SUs. To study different fairness criteria and their effects on the payoff divisions among PUs, we derive the optimal payoff division schemes of two popular fairness criteria, namely Nash bargaining solution and Shapley value fairness.

Information dissemination control is of crucial importance to facilitate reliable and efficient data delivery, especially in networks consisting of time-varying links or heterogeneous links. Since the abstraction of information dissemination much resembles the spread of epidemics, epidemic models are utilized to characterize the collective dynamics of information dissemination over networks. From a systematic point of view, we aim to explore the optimal control policy for information dissemination given that the control capability is a function of its distribution time, which is a more realistic model in many applications. The main contributions of this paper are to provide an analytically tractable model for information dissemination over networks, to solve the optimal control signal distribution time for minimizing the accumulated network cost via dynamic programming, and to establish a parametric plug-in model for information dissemination control. In particular, we evaluate its performance in mobile and generalized social networks as typical examples.

Ad hoc cognitive radio networks (CRNs) have the potential for meeting the challenge of increasing radio spectrum efficiency. However, traditional control of ad hoc networking demands end-to-end information channel feedback, whose feasibility is hard in ad hoc CRNs due to the opportunistic nature of spectrum access. In this paper, we propose a virtual multiple-input multiple-output (MIMO) approach to facilitate error-resilient end-to-end transmission with no need for feedback information. An erasure-channel model is used to describe the randomness of outage caused by opportunistic links. At source node, a discrete Fourier transform (DFT)-based path-time code (PTC) is used to exploit path diversity. The a priori erasure probability is analyzed, and a pipeline scheduling scheme with unequal waiting periods is designed to reduce such probability. At destination node, knowledge of the a priori erasure probability is exploited to overcome the decoding challenge raised by the presence of random erasures. A joint sphere decoder (JSD) with a minimum mean-squared error sorted QR decomposition (MMSE-SQRD) effectively implements maximum a posteriori (MAP) probability decoding. This decoder simultaneously performs erasure identification and data decoding. Numerical results show that the proposed virtual MIMO framework can pave the way to efficient and reliable end-to-end transmission in ad hoc CRNs.

To alleviate the spectrum shortage for sensor networks with tremendous sensors, cognitive radio technology enabling multi-hop opportunistic networking and concurrent transmissions overlaying the primary system suggests an attractive facilitation of large-scale wireless sensor networks (WSNs) and machine-to-machine communications. However, subsequent significant end-to-end delay in large WSN can prohibit practical applications. Leveraging the nature of traffic in sensor networks, we develop in-network computation to reduce requisite transmissions and to accommodate more concurrent transmissions under a given spectrum. Specifically, distributed source coding and broadcasting in wireless communication are exploited to build the computational framework and the achievable network capacity is examined. Furthermore, a greedy networking algorithm is adopted to justify significant improvement on end-to-end delay and further statistical QoS guarantee, while yielding considerable system throughput gain for practical deployment of WSNs. Performance evaluations confirm that we successfully demonstrate communication efficiency from in-network computations and facilitate a new paradigm for spectrum efficient cognitive radio networks, which shall be applicable in general multi-hop wireless networks and spectrum-sharing WSNs.

The spatial aspect of distributed dynamic spectrum access (DSA) in a wireless ad hoc network is usually overlooked in the literature. By using tools from stochastic geometry, we analytically characterized the spatial distribution of users and subsequent interference in the network. Provided with multiple channels, users in the network interact in the sense that the channel selections they make affects co-channel interference they impose on each other, forming a new and general scope of DSA. The study of strategic interactions among users sharing the spectrum are facilitated by a game-theoretic formulation, where the strategy at the Nash equilibrium and the corresponding performance are analytically obtained. Based on the model, we consider further the case when users have multi-channel sensing capability. Access strategies incorporating channel availability information are developed and the corresponding performance is analyzed. Finally, we show that channel (frequency) diversity can be exploited that enables effective design when users are capable of performing concurrent communications over multiple channels. The results of our work provides insights to and facilitates the design of medium access strategies for DSA.

Cooperative spectrum sensing is widely known to collect sensing information from cooperative sensors to assist cognitive radio transmitter to better judge the transmission opportunities, with appearance of hidden primary system terminals. However, it is usually ignored that a successful transmission can go through by requiring not only the spectrum availability at cognitive radio transmitter, but also spectrum availability at cognitive radio receiver. Using this special observation, we use Poisson distributed nodes and Boolean random network model to analyze the geometric region allowing CR transmission with the help of cooperative sensors. Contrary to intuition, we find that cooperative sensing is not always helpful and the region allowing CR transmission is no longer circular anymore. Using this geometric property, we further find that transmission link can remain bidirectional only under certain geometric conditions. Due to these observations, we develop a complete methodology so that a secondary transmitter can select helpful cooperative sensors corresponding to different receivers.

Due to the broadcasting nature of wireless transmission, the cochannel interference in heterogeneous networks among the cells can be a potential limiting factor in cellular systems. To overcome this limitation, many interference mitigation techniques for femto-cells have been proposed, which mainly focus on power control and spectrum allocation. In this paper, through the technique of dirty-paper coding, we are motivated to develop an interference cancellation (IC) method that operate independently in each femto-cell. It is worth to mention that the proposed method can be implemented without further feedback information between macro-cell and femto-cells. Such less coordinated feature of the proposed solution is critical in 3GPP scenarios since it has been aligned that there is no backhaul coordinations among macro-cell and femto-cells in Rel-10 baseline solutions. The simulations show that the proposed method can further reduce the bit error rate (BER) and increase the throughput. Both the significant performance improvements and backward compatibility implies the proposed method has immediate potential in 3GPP systems.

Cloud-based machine-to-machine communications emerge to facilitate services through linkage between cyber and physical worlds. In addition to great challenges in a large network of machine/sensor swarm, effective network architecture involving interconnection of wireless infrastructure and multi-hop ad hoc networking in the machine swarm remains open. Inspired by the small-world phenomenon in social networks, we may establish a short-cut path under a heterogeneous network architecture through wireless infrastructure and cloud, by connecting to data aggregators or access points in the machine swarm, such that end-to-end delay can be significantly reduced. Our mathematical analysis on network diameter and average delay, along with verifications by simulations, demonstrate spectral and energy efficiency of our proposed heterogeneous network architecture in large machine-to-machine communication networks.

Machine-to-machine (M2M) communications are bringing new challenges to congestion control in the Internet of Things. One key issue is to facilitate the proper functioning of a wide range of M2M applications with drastically different throughput demands. Traditional Internet congestion control algorithms aim at sharing bandwidth among traffic flows equally, limiting their suitability for M2M communications. To maintain comparable levels of QoS of heterogeneous M2M applications when congestion is present, we propose a distributed congestion control algorithm which allocates transmission rates to M2M flows in proportion to their demands, through the use of a simple technique which we call “proportional additive increase”. To ease M2M application development, we make a further attempt to stabilize the throughputs of M2M flows controlled by the algorithm. We present simulation results to illustrate the effectiveness of the algorithm in achieving the desired rate allocation, as well as the challenge we face in stabilizing throughputs.

Recently, machine-to-machine (M2M) communication becomes an attractive topic due to the rapid evolution of wireless communication systems and the potential market size of Internet of Things. In this paper, we investigate a M2M network with typical M2M features. Three key parameters, throughput capacity, delay and energy consumption, are analyzed to evaluate the performance of the M2M network under multi-hop and single-hop manner. By scrutinizing these parameters, we find that as r grows from √logn over πn to 1, delay increases progressively from order √nlogn to order n. Meanwhile, throughput keeps the same at order W/n. And there exists an optimal transmission range to minimize average energy consumption. Our results state that multi-hop manner is effective to decrease the delay and energy consumption of M2M network, without benefit on the throughput. To answer the question “to hop or not to hop in massive machine-to-machine communications”, we provide guidelines based on different optimal transmission ranges for the M2M network with different M2M features respectively.

Social networks overlaid on technological networks account for a significant fraction of Internet use. Through graph theoretic and functionality models, this paper examines social network analysis and potential implications for the design of technological networks, and vice versa. Such interplay between social networks and technological networks suggests new directions for future research in networking.

Molecular communications emerges as a promising scheme for communications between nanoscale devices. In diffusion-based molecular communications, molecules as information symbols diffusing in the fluid environments suffer from molecule crossovers, i.e., the arriving order of molecules is different from their transmission order, leading to intersymbol interference (ISI). In this paper, we introduce a new family of channel codes, called ISI-free codes, which improve the communication reliability while keeping the decoding complexity fairly low in the diffusion environment modeled by the Brownian motion. We propose general encoding/decoding schemes for the ISI-free codes, working upon the modulation schemes of transmitting a fixed number of identical molecules at a time. In addition, the bit error rate (BER) approximation function of the ISI-free codes is derived mathematically as an analytical tool to decide key factors in the BER performance. Compared with the uncoded systems, the proposed ISI-free codes offer good performance with reasonably low complexity for diffusion-based molecular communication systems.

Local (single-hop) broadcasting is widely employed in distributed protocols (e.g., neighbor discovery, local information exchange in distributed network optimization and gossip-based algorithms) in wireless ad hoc networks. The performance of local broadcasting is characterized by the mean number of neighbors and the probability distribution of the number of neighbors of a broadcasting node. In this paper, we study the performance of local broadcasting in heterogeneous wireless ad hoc networks in which inter-system interference dominates signal reception quality, considering general fading distributions of both the desired signal and the interfering signals. In addition, we investigate the impacts of different error control techniques (e.g., simple retransmission, Chase combining, and incremental redundancy) on the performance of local broadcasting. The increase in the mean number of neighbors of a broadcasting node with respect to the number of retransmissions of a message is clarified, facilitating QoS provisioning in reliable local broadcasting in interference-limited heterogeneous wireless ad hoc networks.

The emerging diffusion-based molecular communications is a promising scheme for nano-machine communications. Nevertheless, the Brownian motion model, which describes the behavior of molecules, makes the physical channel different from the channel in conventional wireless communications. In particular, the crossovers in time caused by the late arrivals of the molecules severely affect the communication reliability. In traditional communications, channel coding has long been used to enhance the reliability. Through our explorations, it is shown that the commonly used Hamming distance is no longer a good metric for the channel decoding in diffusion-based molecular communications. The conventional concepts of the channel code design cannot be straightforwardly applied. In this paper, the molecular coding (MoCo) distance function has been proposed, and shown to approach the optimum performance beyond the capability of using Hamming distance. This suggests that new paradigms can be developed upon the MoCo distance.

Inter-operator spectrum sharing has been proposed to exploit spectrum efficiency in future communication system. By using game theory and learning algorithm, we design a partially distributed implementation method. Such a method is proven to reach the system equilibrium which improves spectrum efficiency between operators. Furthermore, by queueing analysis and robust potential game, we show that the convergence rate can be accelerated by incorporating more asynchronous operator systems. This distributed implementation method creates a low complexity solution for operators to realize inter-operator spectrum sharing for 3GPP LTE-A systems or similar.

Communications based on diffusion refers to the transfer of information using molecules as message carriers whose propagation is governed by the laws of Brownian motion. Molecular communication is considered to be one of the most promising approaches for the end-to-end communication between nanoscale devices. In this paper, both an optimal and a suboptimal receiver detection scheme are proposed for a diffusionbased binary digital communication system in the presence of ISI. The transmission of binary information is accomplished by using On-Off Keying (OOK) with only one molecule. The proposed system can serve as the theoretical basis for end-to-end communication in molecular nanonetworks where molecules of different types are used by different nanoscale devices. The effect of channel memory resulting from the residual molecule diffusion from previous transmissions is treated analytically in the formulation of the detection schemes. Numerical results show that the proposed detection schemes can maximize the mutual information over a practical range of the parameter of signaling interval without a priori information. A channel capacity of 1 bit per channel utilization during a signaling interval can be ultimately achieved by extending the duration of the signaling interval, even with infinite channel memory.

Diffusion-based communication refers to the transfer of information using molecules as message carriers whose propagation is based on the law of molecular diffusion. Path loss can have a major impact on the link quality in molecular communication as the signal strength is shown inversely proportional to the cube of the communication distance. In this paper, various diversity techniques for Multi-Input Multi-Output (MIMO) transmissions based on molecular diffusion are proposed to improve the communication performance in nanonetworks in the presence of Multi-User Interference (MUI). Analogous to radio communication, the concept of diversity and Spatial Multiplexing (SM) can be successfully applied in molecular communication. To the best of our knowledge, our paper is the first which investigates the aspects of MIMO transmissions for molecular communication. Numerical results show that the proposed diversity techniques can successfully lower the error rate. Further performance improvement can be obtained by properly allocating molecules among the transmission nodes if the Channel State Information (CSI) is available at the transmitter end. To optimize the system throughput, a dynamic switching mechanism between the diversity mode and the Spatial Multiplexing (SM) mode can be employed.

Molecular communications emerge as a promising paradigm for the nano-scale communication in nanotechnology. Though still at an early stage, some research efforts have been made and various molecular communication systems have been proposed. However, existing works mainly focus on the synchronous communication schemes where the synchronous assumption is made for simplifying analyses. In this paper, we propose an asynchronous communication scheme for molecular communications. For the proposed scheme, we make extensive analyses and develop an approximation for channel capacity as a general performance measure which also applies to synchronous systems. Beyond the theoretical analysis, we also examine a specific asynchronous system and compare its performance with synchronous systems to confirm the feasibility of asynchronous molecular communications.

Diffusion-based communications refers to the transfer of information using particles as message carriers whose propagation is based on the law of particle diffusion. Though still at an early stage, there have been growing interests and research efforts dedicated to this communication technology. It has been identified that diffusion-based communications is one of the most promising approaches for end-to-end communication between nanoscale devices in the near future. In this paper, the design of a binary digital communication system is proposed based on particle diffusion. Stochastic signaling through On-Off Keying (OOK) for random particle emission and a diffusion channel with memory is considered. The diffusion is considered in the cases of one, two, and three dimensions. The receiver detection problem is formulated by using an information-theoretic approach. The optimal decision threshold for the receiver detection is derived through mutual information maximization for two cases, namely, when the a priori probability of bit transmission is fixed and known to the receiver and when this probability is unknown to the receiver. Numerical results indicate that in the case of diffusion in one or two dimensions, the information of a priori probability plays a key role in optimizing the system performance, while it does not when considering the diffusion in three dimensions.

Carrier aggregation has been adopted as a key technology in 3GPP LTE-Advanced Release 10 to enable extremely high data rate services. However, due to imperfect implementation, adjacent channel interference (ACI) potentially induces inter-system interference among co-existing systems which can greatly degrade performance. In this paper, we develop an equivalent adjacent channel interference ratio (ACIR) model to simplify the general evaluation of all possible CA coexistence scenarios beyond investigations in Release 10, and our methodology can be applied to wider scenarios including considering both FDD and TDD at the same time. By using this method, our simulation results assure that introducing Carrier Aggregation (CA) technique does not significantly influence the system performance in newest IMT band, 3.5GHz under the current regulations.

In addition to theoretical study in statistical physics, synchronization in large heterogeneous networks has always been a technology challenge, due to lacking of common infrastructure and interface. However, misalignment of timing can significantly degrade the performance. In light of existing algorithms failing to effectively respond to node failure in timing, we develop an improved voter algorithm for network time synchronization enabling reliable synchronization in heterogeneous wireless networks. We further apply this improved algorithm to femtocells in LTE-A systems to successfully confine the timing errors in a more effective way.

Cognitive radio (CR) enables dynamic spectrum access to provide universal connectivity across different types of radio access technologies, realizing seamless content delivery in the next generation wireless networking. As the most general cognitive radio network scenario, multiple ad hoc and infrastructure networks sharing the same spectrum are allowed to cooperate with each other by using other networks' nodes as relays to carry and forward traffic, forming an interconnected heterogeneous network. Previous research is limited to the connectivity analysis of hybrid wireless networks with path loss channel model. In this paper, we analyze the connectivity of the interconnected heterogeneous network consisting of multiple ad hoc networks and multiple infrastructure networks from a percolation-based perspective, considering both noise-limited and interference-limited environment with general fading channel model. The benefit of network-based cooperation is quantified in terms of the percolation threshold, facilitating proper network control and deployment. For example, using such quantification, we can specify the amount of infrastructure deployment or reliable connections to control the connectivity of existing ad hoc networks, and vice versa. Finally, we apply the analysis to the coexistence of primary and secondary networks, as the common CR scenario.

In cognitive radio networks, the signal reception quality of a secondary user degrades due to the interference from multiple heterogeneous primary networks, and also the transmission activity of a secondary user is constrained by its interference to the primary networks. It is difficult to ensure the connectivity of the secondary network. However, since there may exist multiple heterogeneous secondary networks with different radio access technologies, such secondary networks may be treated as one secondary network via proper cooperation, to improve connectivity. In this paper, we investigate the connectivity of such a cooperative secondary network from a percolation-based perspective, in which each secondary network's user may have other secondary networks' users acting as relays. The connectivity of this cooperative secondary network is characterized in terms of percolation threshold, from which the benefit of cooperation is justified. For example, while a noncooperative secondary network does not percolate, percolation may occur in the cooperative secondary network; or when a noncooperative secondary network percolates, less power would be required to sustain the same level of connectivity in the cooperative secondary network.

The recent deployment of Cyber-Physical Systems (CPS) has emerged as a promising approach to provide extensive interaction between computational and physical worlds. For a large-scale distributed CPS comprising of numerous machines, sharing radio resource efficiently with the existing wireless networks while maintaining sufficient quality of service (QoS) for machine-to-machine (M2M) communications becomes an essential and challenging requirement. By clustering CPS machines as a swarm with the cluster head managing radio resources inside the swarm, spectrum sharing among numerous machines can be achieved in a distributed and scalable fashion. Specifically, we apply the recent innovation, cognitive radio, and a special mode in cognitive radio, interweave coexistence, to leverage machines to collect radio resource usage information for autonomous and interference-free radio resource management in the CPS. To reduce the communication overheads of channel sensing feed backing from machines, we apply compressive sensing to construct a spectrum map indicating the radio resource availability on any given locations within the CPS coverage. Such spectrum map resource management (SMRM) only utilizes a small portion of machines to perform channel sensing but enables distributed cluster-based spectrum sharing in an efficient way. Through the concept of effective capacity, the SMRM controls available resources to guarantee the QoS for communications of CPS. By evaluating the performance of the proposed SMRM in the most promising realization of CPS based on LTE-Advanced machine-type communications coexisting with LTE-Advanced Macrocells to utilize identical spectrum, the simulation results show effective QoS guarantees of CPS by SMRM in the realistic environments.

Supporting trillions of devices is the critical challenge in machine-to-machine (M2M) communications, which results in severe congestions in random access channels of cellular systems that have been recognized as promising scenarios enabling M2M communications. 3GPP thus developed the access class barring (ACB) for individual stabilization in each base station (BS). However, without cooperations among BSs, devices within dense areas suffer severe access delays. To facilitate devices escaping from continuous congestions, we propose the cooperative ACB for global stabilization and access load sharing to eliminate substantial defects in the ordinary ACB, thus significantly improving access delays.

This paper provides an extensive analysis on interferences due to different interfering sources within cognitive-empowered femtocell (CEF) networks. Based on the interference analysis and formulations, a stochastic dual control (SDC) approach is introduced for dynamic sensing coordination, aiming to achieving efficient interference mitigation without involving global and centralized control efforts. Simulation results show that the proposed SDC approach can effectively reduce interferences under highly dynamic environments within CEF networks when compared with other spectrum sensing strategies.

ABSTRACT Cooperative relay enables multihop cognitive radio networks of cognitive radios (CRs) and nodes of primary system. However, successful CR networking functions such as routing prefer on the knowledge of radio resource availability associated with location, which is a challenge of traditional spectrum sensing. We introduce the concept of spectrum map encompassing spectrum and location information to facilitate multihop routing in highly dynamic environment, in addition to utilizing statistics of opportunistic links. Because traditional spectrum sensing only knows local information of CR transmitter, it is difficult and resource consuming to construct the entire spectrum map. We therefore extend the compressed sensing technique requiring very limited local sensing to establish spectrum map. We analyze the communication overhead and practically construct the spectrum map via expander graph. Finally, we demonstrate using spectrum map to reliably route packets of CRs in an end‐to‐end way, under the guaranteed outage for nodes in primary system and maximizing the throughput among cooperative CRs. Copyright © 2012 John Wiley & Sons, Ltd.

The scope of this paper is two-fold. First, we extend the concept of transmission capacity of a wireless network to the broadcast scenario and consequently define the broadcast transmission capacity of a wireless network as the product of the spatial density of broadcasting nodes, the average number of neighbors of a broadcasting node, and the information rate, considering both intra-system interference and inter-system interference from multiple coexisting heterogeneous wireless networks. The broadcast transmission capacity is obtained under general fading of desired signal and interfering signals. Second, we characterize the impact of eavesdroppers on the broadcast transmission capacity of a wireless network with secrecy outage constraint. In this case, the information rate is replaced by the secrecy rate. Our results illustrate performance limits of a network coexisting with other heterogeneous networks (and/or eavesdroppers) and trade-offs between different system parameters, and thus suggest network design (e.g., optimal medium access probability) of protocols that require local broadcasting, such as local information exchange in distributed network optimization and gossip-based algorithms.

In cognitive radio networking, the signal reception quality of a secondary user degrades due to the interference from multiple heterogeneous primary networks, and also the transmission activity of a secondary user is constrained by its interference to the primary networks. It is difficult to ensure the 1-connectivity of the secondary network, so we here characterize the connectivity of the secondary network from a percolation-based perspective. On the other hand, since there may exist multiple heterogeneous secondary networks with different radio access technologies, such secondary networks may be treated as one secondary network via proper cooperation. In this paper, we investigate the connectivity of such a cooperative secondary network, under which each secondary network's user may have other secondary networks' users acting as relays. The connectivity of this cooperative secondary network is characterized in terms of percolation threshold, from which the benefits of cooperation are justified. For example, while a noncooperative secondary network does not percolate, percolation may occur in the cooperative secondary network; or when a noncooperative secondary network percolates, less energy may be used to sustain the same level of connectivity in the cooperative secondary network.

Intentional attack incurs fatal threats on modern networks by paralyzing a small fraction of nodes with highest degrees to disrupt the network. To enhance the network robustness, in this paper we propose a fusion-based defense mechanism where each node performs local detection and feedbacks minimum (one-bit) information to the fusion center in order to infer the presence of attack. By formulating the attack and defense strategy as a zero-sum game, we leverage the expected payoff as a benchmark to evaluate the effectiveness of the fusion-based defense mechanism. Both analytic results and empirical data support that fusion-based defense is able to prevent the network from disruption, even under poor detection capability and fragile nature of complex networks.

To facilitate reliable communications, efficacious signaling is of crucial importance for information dissemination control, especially in dynamic and infrastructureless networking paradigms such as mobile ad hoc networks (MANETs). Since data transportation much resembles the spread of epidemics, two signal distribution schemes, self healing and vaccine spreading, are proposed to analyze the information dynamics via epidemic modeling. We consider a more realistic scenario where the effect of signaling is proportional to the distribution time. Intuitively, early distribution leads to slight cure while late distribution fails to prevent the epidemic from outbreak. Optimal control theory and early-stage analysis are exploited to determine the optimal distribution time for timely control. Moreover, we demonstrate how signal distribution aids to minimize buffer occupancy in epidemic routing. This research therefore paves novel avenues to network defense and information dissemination.

This paper introduces the general framework of statistical information fusion on social network, which plays an important role on understanding the human interaction and cooperation in the society. On many existing information platforms on the social network, people's relationship affects their decision on both static and dynamic way. We consider the scenario that agents refer others decisions which are made earlier as an information for taking any action. Thus how agents on the information platform connect to each other has great impact on the information fusion. With the help of percolation theory, we provide a minimax robust decision scheme for information fusion due to often the lack of complete information of social network structure. The simulations demonstrate that our proposed information fusion rule retains fine performance compared with classical one on different network structures, and thus great potential for social network applications.

Deploying picocells overlaying Macrocells as the second tier has been regarded as a promising approach to enhance the spectrum efficiency and thus high data rate services, but only if cross-tier interference can be effectively controlled. Since both tiers shall support considerable numbers of users, interference mitigation by centralized/collaborated radio resource allocation among two tiers is infeasible. The cognitive radio is consequently a potential technology for autonomous interference mitigation. However, the obstacle of applying cognitive radio to the picocell is an unacceptable amount of feedback of channel sensing results from users. To tackle this critical challenge, we propose the spectrum map resource management (SMRM) for picocells. By compressive sensing, the SMRM constructs the spectrum map indicating the radio resource availability on any given location by only leveraging a small portion of users to perform channel sensing. Through the concept of effective capacity, the SMRM then controls available resource of the picocell to practice the critical guarantees of quality-of-service in picocells, and thus future multi-tier cellular networks.

We study the degree distribution of a wireless ad hoc network node sustaining inter-system interference from heterogeneous wireless ad hoc networks. The relationship between the average number of neighbors of a wireless node and the number of retransmissions is clarified under different broadcasting schemes. The degree distribution is then generalized to the multi-channel case. Furthermore, the connection between the degree distribution of a wireless node and the connectivity of the wireless network in percolation sense is established by extending the methods of complex network. Using these tools, the benefit of cooperation among heterogeneous wireless networks is analytically justified in terms of percolation threshold. Supplemented with numerical results, our analysis gives insight into fundamental limits of coexistence of heterogeneous wireless networks, advantages of different broadcasting schemes and benefits of cooperation among heterogeneous networks.

Cognitive Radio (CR) techniques have been developed to allow ad hoc users to communicate with each other by exploiting the licensed bands of primary systems without disturbing the entrenched users. In this work, we take current approaches one step ahead and combine Topology Control (TC) techniques with CR technology, in order to improve operation and performance, even under the stringent and non-uniform arrangements of CR networks (CRNs). We propose a novel node-degree based Topology Control approach, denoted by Enhanced Cognitive Nearest Random Neighbor (e-CNRN), for multi-channel CRNs, aiming at maintaining network connectivity and adapting to environmental changes such as primary user activity and channel conditions. Compared with TC protocols in conventional ad hoc networks, e-CNRN requires only minimal local information and is specially designed to perform under non-uniform node arrangements, rendering e-CNRN a generic and robust distributed TC approach, especially suitable for multichannel CRNs as well. In addition, we leverage e-CNRN for distributively establishing a virtual common control channel for multi-channel CRNs. Through analysis and simulations we validate that e-CNRN guarantees network connectivity, while achieving efficient power control.

We study minimization of overall energy consumption of OFDMA systems, which are widely adopted in future wireless communications. Based on the existing resource allocation to minimize transmission energy, the computation energy increases due to the complexity of allocating algorithms. Since high performance algorithms usually have high complexity, there exist trade-offs between transmission energy and computation energy. To systematically minimize overall energy consumption comprised of transmission and computation, we propose to minimize transmission energy by resource allocation while keeping the computation energy low via reduced-complexity algorithm. We further extend this idea to relay since it is well known to reduce transmission energy but only a few of works to minimize total energy consumption. In our numerical results, we demonstrate that in the scenario without relay, our algorithm can achieve near-minimum transmission energy (80% savings) under quite computation energy. In the scenario with relay, our algorithm can do even better by saving more transmission energy (88% savings) under a minor increase of computation energy. Both scenarios suggest that the global energy minimum can be achieved by proper trade-off between communication and computation energy consumption.

Although energy efficiency of communication systems has been studied for years, minimizing overall energy consumption of OFDMA downlink systems is still a challenge. In view of usual implementations, in this paper the energy consumption of control processor, specific circuit and analog front-end are jointly minimized, with algorithm complexity, resource scheduling, coding rate and modulation order as optimization variables. Due to the optimization algorithm executed in control processor determines energy consumption in other components, a way based on iterative algorithms is proposed to track the minimum of entire system energy consumption. In the case study, we design a genetic algorithm and demonstrate the effective tracking based on practical parameters, which implies the proposed iterative tracking is applicable to and ready for advanced cellular systems such 3GPP LTE.

Terabit wireless potential of Wavelength Division Multiplexing (WDM) is explored in this paper, through a novel detection algorithm over single-slit receiver. Different from current solutions that separates signals on different wavelengths by optical components (e.g. lens), our solution is based on simpler architecture and signal processing. The receiving signal is first passed through single-slit to be summation of a set of square-sinc functions, where each square-sinc corresponds to signal on one wavelength. Then, we apply a nonlinear differential operator on the received signal to convert it to a fractional function, where each square-sinc becomes a pole. By identifying the existence of each pole in parallel, we can detect signal on each wavelengths efficiently. Along with our numerical results, we demonstrate the possibility of terabit wireless by WDM and could be good start for future researches on ultra-high data rate communications.

Interference mitigation between macro-cell and femto-cells are studied in this paper. Via demonstrating the communications between various transmitter-receiver pairs on different spatial channels could be insulated via proper channel de-correlation at receivers, we propose to separate the transmissions of macro-cell and of femto-cell in spatial domain. Induced challenges such as spatial channel estimation and codeword-to channel mappings are investigated, and we further adopt the Gibbs sampler to achieve co-channel selection of femto-cells. The proposed solutions can be realized without macro-femto coordinations and additional feedback, and are demonstrated aligned with current 3GPP LTE standardization status. We evaluate the proposed schemes by sophisticated simulations based on the dual-strip model in 3GPP femto-cell evaluation scenarios. It has been shown the SINR of users attached to macro-cell can be improved 60 dB while the SINR of users attached to femto-cells can be maintained about 10 dB. Both the significant performance improvements and backward compatibility implies the proposed scheme is valuable and could be contributed to 3GPP instantly.

Cooperative relay enables general multi-hop cognitive radio networks (CRN) over cognitive radios (CR) and nodes of primary system (PS). However, successful CR networking such as routing relies on the knowledge of radio resource availability associated with location, which is a challenge of traditional spectrum sensing. We introduce the concept of spectrum map encompassing spectrum and location information to deterministically assist multi-hop routing in highly dynamic environment, while common routing of CRN paying attention to statistical nature of opportunistic links. Since it is not feasible to construct the entire spectrum map as traditional spectrum sensing only knows local information of CR transmitter, we adopt the novel compressed sensing technique to establish spectrum map based on a small number of available local sensing results, and then to reliably route packets of CRs in an end-to-end way, under the guaranteed outage for nodes in PS and no dedicated bandwidth for CRs. In other words, we enlarge the scope of cooperative sensing to effectively facilitate routing of CRN, beyond traditional CR scope, which is justified by numerical results.

With the help of prioritised polling token and a local synchronisation scheme, this study proposes a quality of service (QoS)-guaranteed scheduling algorithm. It works based on distributed intelligence and schedules both intra-cluster and inter-cluster connections in a cluster-based multihop wireless ad hoc sensor network. Theoretical analyses are employed to derive the bounds for the jitter and delay of each intra-cluster and inter-cluster connection. Based on the obtained jitter bounds and delay bounds, a connection admission control scheme is proposed as the admission criterion for each newly requested connection. Both numerical and simulation results show that by taking the advantages of proposed polling-based QoS scheduling algorithm and the connection admission control jointly, the QoS requirements of all admitted intra-cluster and inter-cluster connections are guaranteed.

To increase spectrum utilization, cognitive radio allows concurrent secondary and primary transmissions as long as interference to primary users is constrained under a threshold. This research proposes an enhanced opportunistic interference mitigation scheme utilizing both successfully and unsuccessfully decoded primary packets to improve data rate of secondary transmission. Moreover, we propose an analytical model to investigate characteristic changes of the spectrum usage affected by the coexisting secondary transmission in terms of overall spectral efficiency. The interference mitigation scheme can be applied to realistic two-tier femtocell networks to enable robust communication against cross-tier interference thereby obtaining a substantial spectrum reuse gain.

To successfully deploy femtocells overlaying the Macrocell as a two-tier that had been shown greatly benefiting communications quality in various manners, it requires to mitigate cross-tier interference between the Macrocell and femtocells, and intra-tier interference among femtocells, as well as to provide Quality-of-Service (QoS) guarantees. Existing solutions therefore assign orthogonal radio resources in frequency and spatial domains to each network, however, infeasible for dense femtocells deployments. It is also difficult to apply centralized resource managements facing challenges of scalability to the two-tier. Considering the infeasibility of imposing any modification on existing infrastructures, we leverage the cognitive radio technology to propose the cognitive radio resource management scheme for femtocells to mitigate cross-tier interference. Under such cognitive framework, a strategic game is further developed for the intra-tier interference mitigation. Through the concept of effective capacity, proposed radio resource management schemes are appropriately controlled to achieve required statistical delay guarantees while yielding an efficient radio resources utilization in femtocells. Performance evaluation results show that a considerable performance improvement can be generally achieved by our solution, as compared with that of state-of-the-art techniques, to facilitate the deployment of femtocells.

Abstract Mesh networks with universal frequency reuse is expected to play an important role for future wireless system architecture, to provide ultra high throughput and seamless access via cooperative base stations (BSs), relays, and users. Different from traditional networks with central control, BSs should operate in a self‐organizing way (i) to support stable high‐throughput green communications (ii) to avoid inter/intra cell interference, and (iii) to implement in an ease and backward compatible way; and resource management is thus a critical step to satisfy (i), (ii), and (iii). We first systematically present radio resource management based on extensive literature survey. To optimize system performance, we develop the cognitive resource management for wireless mesh networks with self‐organizing BSs to utilize entire available spectrum, while BSs collect channel state information of each user by spectrum sensing. From numerical results, our resource management can significantly improve network throughput over the benchmark system, which suggests good potential for many wireless systems such as LTE‐A and WiMAX 2 that are adopting mesh network architecture, to achieve universal frequency reuse. Copyright © 2010 John Wiley & Sons, Ltd.

Welcome to this special issue of the Wiley's Wireless Communications and Mobile Computing Journal

Consumer communication networking allows connections among consumer devices for possible Ad Hoc or heterogeneous networking on licensed and unlicensed bands. To support maximum flexibility of consumer communication networks, cognitive radio (CR) technology by spectrum sharing can well serve the purpose. Realistic spectrum sharing for cognitive radio networks (CRN) shall be distributed and based on partially available information of spectrum sensing, due to a possible good number of cognitive radios and impossible to perfectly exchange channel availability information. However, existing study assumes either centralized or perfect spectrum sensing. In this paper, we pioneer explore spectrum sensing as a side information and take imperfection of spectrum sensing results into consideration. We therefore develop spectrum sharing algorithms for spectrum access strategy of CRs under two general scenarios: public spectrum information broadcast from the base station and private spectrum information via individual spectrum sensing. Under the public spectrum in-formation, CRs are aware of the strategy of its opponents and therefore game theory model reaches Nash equilibrium as a solution. On the other hand, CRs have to follow maximin criterion with only local spectrum information. Difference in the behaviors of CRs between the two scenarios is identified and the optimal spectrum access strategy is proposed accordingly for both cases. Numerical results demonstrate that the proposed algorithms work effectively and prevent the system from collision even in a large network.

Characterizing the topology and therefore fundamental limits is a must to establish effective end-to-end cognitive radio networking (CRN). However, there lacks complete understanding of the relationship among connectivity, interference, latency and other system parameters of the CRN. To clarify this complication, by employing tools from both percolation theory and stochastic geometry, we thus provide a novel parametrization of underlay secondary ad hoc CRN wherein the secondary network is regarded as an operating point in the phase space. Coexisting with a primary ad hoc network, the secondary network undergoes a phase transition due to avoiding interference to primary receivers, while being interfered by primary transmitters. Furthermore, transmit power allocation of secondary users is represented by a Pareto contour in the phase space, and the impact of interference on connectivity is captured by the latency-to- percolate. Finally, with the cognitive capability of CR, performance improvement of importing an SU- avoidance region around primary receivers is analyzed, and CRNs can be therefore successfully supplied.

Wireless networks, especially mobile ad hoc networks (MANET) and cognitive radio networks (CRN), are facing two new challenges beyond traditional random network model: opportunistic links with random nature due to fading and dynamic channel access, and dynamic network topology due to mobility and time varying. For a more general networking of Internet users as social complex networks, such challenges of further importance remain open. A generic and reliable model is therefore required to capture the information dissemination dynamics (IDD) of machine- to-machine communications or interactions in relation-based social networks. Inspired from epidemiology, we investigate the IDD in well-known complex networks by modified Susceptible-Infected (SI) model, which is surprisingly suitable for above multiple scenarios. Systematically categorizing such networks and examining conditions to adopt SI model for IDD in complex networks, the fundamental properties including mixing type, vertex connectivity and giant component size provide valid insights for quantitative analysis. We also investigate the IDD in networks with time-varying topology and show that all individuals receive the information in dynamic sense, even if the giant component size is not compatible with the number of individuals in static sense. Consequently, we successfully establish such analytical model for characterizing the IDD in complex networks consisting of opportunistic links and time-varying topology, feasible for various random wireless networks and social networks.

Cognitive radio (CR) emerges as a key technology to enhance spectrum efficiency and thus creates opportunistic transmissions over links. Supporting the routing function on top of numerous opportunistic links is a must to route packets in a general cognitive radio network (CRN) consisting of multi-radio systems. However, there lacks complete understanding of these highly dynamic available links and a reliable end-to-end transportation mechanism over CRN. Aspiring to meet this need, we propose novel spectrum aware opportunistic routing (SAOR) algorithm suited for the CRN under wireless fading channels. With innovative establishment of the spectrum map from local sensing information and the derivation of the routing metric for opportunistic links known as opportunistic link transmission (OLT), the opportunistic path metrics, and the CR node metrics, the promising SAOR employs a cooperative scheme to enable multi-path transmissions and maintains the statistical QoS guaranteed throughput for practical applications. Numerical results confirm that SAOR enjoys less delay with guaranteed throughput, not only in CRN, but also in general wireless network.

Deploying femtocell networks embedded in the Macrocell coverage greatly benefits communication quality in variety manners. However, the lack of schemes to effectively mitigate detractive interference, fully utilize radio resources and provide quality-of-service (QoS) guarantee (in terms of delay) creates challenges to practically facilitate the concept of femtocell. To tackle these challenges to achieve a successful dense femtocell deployment, this paper proposes a cognitive radio resource management (CRRM) scheme which is inspired by the spirit of cognitive radio technology. Instead of the need of a centralized manner, the femtocell with the proposed CRRM can autonomously sense the radio resource usage of the Macrocell so as to mitigate interference. By analytical deriving the effective capacity of the CRRM that specifies the QoS guarantee capability of the system, the optimum sensing period and radio resource allocation are proposed for the CRRM to achieve a fully radio resource utilization while statistically guaranteeing the QoS of the femtocell. Numerical results demonstrate that the proposed CRRM outperforms the randomized scheme (without CRRM) in terms of the radio resource utilization efficiency. Simulation results also support the effectiveness on the delay guarantee performance.

To network cognitive radios that require spectrum sensing to identify transmission opportunities emerges as a critical technology to facilitate spectrum efficient wireless communications. We propose to apply synthetic aperture radar technology to construct spectrum map so that efficient cognitive radio networking operation is possible in this paper. We demonstrate such an approach indeed realizing spectrum map.

Cognitive radio (CR) technology has been considered promising to enhance spectrum efficiency via opportunistic transmission at link level. To make CR useful, networking CRs to form a cognitive radio network (CRN) is able to support end-to-end transmission from CR source to CR destination. However, the opportunistic nature of CR link for interference avoidance to primary users degrades the quality-of-service (QoS) of end-to-end CR transmission and challenges the CRN toward a completely successful operation. Through queueing analysis, we propose a statistical control mechanism to deal with such opportunistic links in CRN by cooperative relaying the same packet flows into several opportunistic paths simultaneously. The availability and reliability of redundant transmission over the end-to-end paths in the same group is enhanced. By maximizing the number of groups with bounded statistical delay, the spectrum efficiency is enhanced. This optimized grouping problem is mathematically equivalent to the bin covering problem with NP-hard complexity. By the proposed Round-Robin algorithm, simulation results show that the optimal performance can be achieved in the case that the statistical availabilities of all opportunistic links are the same. This work therefore provides an essential viewpoint via cooperative relay among CRs in CRN, the QoS (i.e., average delay) can be guaranteed and spectrum can be efficiently utilized.

In two-tier networks consisting of a macrocell overlaid with femtocells in co-channel deployment and closed-access policy, spatial reuse is achieved at the price of severe cross-tier interference from concurrent transmissions. The lack of direct coordination between the macro and femtocells makes interference control as a challenging issue. Cognitive radio (CR) becomes a promising solution, where femtocells with cognitive information accomplish concurrent transmissions while meeting a per-tier outage constraint. Several interference-aware allocation approaches are proposed to enhance spatial reuse according to cognitive capabilities of femtocell. By employing stochastic geometry model, bounds on the distribution of aggregated interference from two-tier spatial point processes are successfully analyzed. The maximum number of simultaneously transmitting femtocells and overall downlink capacity of two-tier networks meeting a per-tier outage requirement in each approach are theoretically derived. This paper proves that with stronger cognitive capability (i.e., more knowledge interpreted) at femtocell, more spatial reuse gain can be found.

Dynamic spectrum access (DSA) supporting opportunistic transmission without extra spectrum bandwidth is attractive for future wireless communication. To facilitate such DSA system, a power-efficient (green) communication processor is needed to support extremely high speed operation on a power-limited mobile device. Traditional general-purpose and digital signal processors are unable to simultaneously satisfy the processing speed and power efficiency for DSA systems. By exploiting critical parallelism and data exchange patterns based on communication algorithms inside a green processor, we successfully develop green SDR based communications processor for DSA that can be reconfigured to multiple system operation. We verify this novel architecture in TSMC 0.13µm CMOS process and demonstrate advantages simultaneously in performance and power efficiency.

This paper presents a new quadratic-time algorithm for joint Tx and Rx antenna selection (AS). The proposed approximate optimum executes much faster than the semidefinite-relaxation method, and hence becomes more robust in preserving full AS diversity for space-time coded systems over Rayleigh fading channels. Numerical results confirm the achieved approximation ratios of maximum mutual information and Frobenius norm of channel close to one.

Cognitive radio networks (CRN) may greatly enhance the throughput based on a given bandwidth. CRN has a unique feature, consisting of uni-directional opportunistic wireless links, and packets may be cooperatively relayed from source node to destination node through one or multiple paths, while each path consists of multi-hop opportunistic wireless fading links. Effective end-to-end error control is therefore a must to complete transportation of packets in CRN, though not being touched in open literatures. Traditional ARQ cannot generally function over uni-directional opportunistic links. We consequently develop a novel hybrid ARQ based on amplify-and-forward cooperative relay of CRN to reach the purpose of end-to-end error control at session level. Multiple coded sub- packets are sent through multiple paths, and the destination node decodes these received coded sub- packets from different paths. This HARQ for CRN works even missing some coded sub-packets. By the concept of outage, we also study allocation of coded sub-packets on multiple paths in an information theoretical view and develop a coding rate adaptation scheme for CRN such that end-to-end error control is possible.

Abstract Networking cognitive radios and nodes from primary system (PS) results in a heterogeneous coexisting multi‐radio wireless network, so that significant network throughput gain can be achieved. However, by investigating cognitive radio network (CRN) architecture, the links in CRNs are unlikely to support complete security check due to link dynamics, opportunistic availability, and uni‐directional in available time window. We therefore introduce trusted cognitive radio networking (TCRN) concept to facilitate network functions such as association in dynamic spectrum access and routing. First of all, we explore the mathematical framework for trust in CRNs. We then show successful association of node to CRN based on the mathematical structure of trust from statistical decision theory. Furthermore, we modify the machine‐learning algorithm to update the trust measure for each node, and develop rules of thumbs to facilitate TCRN with learning capability, based on numerical simulations. Trusted CRN can greatly alleviate heterogeneous challenge for CRN operation. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Cognitive radio (CR) has been considered a key technology to enhance overall spectrum utilization by opportunistic transmissions in CR transmitter–receiver link(s). However, CRs must form a cognitive radio network (CRN) so that the messages can be forwarded from source to destination, on top of a number of opportunistic links from co‐existing multi‐radio systems. Unfortunately, appropriate routing in CRN of coexisting multi‐radio systems remains an open problem. We explore the fundamental behaviors of CR links to conclude three major challenges, and thus decompose general CRN into cognitive radio relay network (CRRN), CR uplink relay network, CR downlink relay network, and tunneling (or core) network. Due to extremely dynamic nature of CR links, traditional routing to maintain end‐to‐end routing table for ad hoc networks is not feasible. We locally build up one‐step forward table at each CR to proceed based on spectrum sensing to determine trend of paths from source to destination, while primary systems (PSs) follow original ways to forward packets like tunneling. From simulations over ad hoc with infrastructure network topology and random network topology, we demonstrate such simple routing concept known as CRN local on‐demand (CLOD) routing to be realistic at reasonable routing delay to route packets through. Copyright © 2009 John Wiley & Sons, Ltd.

It is well known that a cognitive radio network (CRN) may further enhance the sum spectral efficiency using temporarily idle spectrum. However, we usually ignore the spatial domain behaviors to fully utilize the radio resource to approach available system capacity. When a signal is transmitted, we usually treat its occupancy of spectrum along all propagation directions. However, up to certain range (distance), the signal strength is weak and possibly allow another co-existing transmission happening with a satisfactory signal to interference-and-noise ratio (SINR) at both transmit-receive (T-R) pairs. In this paper, we realize this concept for a CRN consisting of a primary (PR) T-R pair and a cognitive radio (CR) T-R pair; furthermore, we consider to what condition concurrent transmission can happen. We use the Lagrange multiplier method to determine the optimal power allocation scheme for such a CRN and then determine to what extent can the sum spectral efficiency be enhanced. It is found that the CR transmitter (CR-TX) may either transmit at its maximum allowable power or just turn off with no transmission power. Concurrent transmission of the PR T-R and CR T-R pairs with distance as a new dimension of multiplexing can be considered as a new version of CRN.

Cognitive radio (CR) technology is considered as an effective solution to enhance overall spectrum efficiency, especially primary radio network (PRN) typically having relatively low spectrum utilization. However, to realize CR concept, it is essential to provide enough incentives to PRN and extra profit to the service provider so that cognitive radio mobile stations (CR-MSs) may utilize PRN spectrum accordingly, which provides a new challenge for spectrum management. In this paper, we consider a PRN consisting of a primary system base station (PS-BS) and multiple primary system mobile stations (PS-MSs), and we therefore construct a cognitive radio network (CRN) consisting of a PRN with multiple CR-MSs. We propose a spectrum management policy framework such that CR-MSs can compete in utilization of the PRN spectrum bands available to opportunistic transmission of CR-MSs by Vickrey auction. PRN is granted incentives for being compensated for possible operating interference from CR-MSs in this scenario. Once CR-MSs are awarded the spectrum bands, they can be guaranteed a superframe time to utilize the spectrum band without any spectrum handoff. Consequently, in addition to incentives to the PRN, the overall spectrum utilization, the profit of the service provider, the spectrum access opportunity of the CR-MSs are increased to achieve co- win situation for every party in cognitive radio networks.

Most of the modern intelligent mobile devices such as intelligent vehicles or robots rely on sensor fusion to perceive the environment and make the decision on direction by traditional maximum likelihood (ML) criterion and possible direct decision feedback. To optimally fuse the sensor observation, we propose a novel approach called decision-prediction fusion (DP fusion). It further includes the previous decision as well as the previous state in the state transition concept of Kalman filter to derive the a prior probability of the current state. Thus traditional sensor ML fusion is converted to maximum a posteriori probability (MAP) detection by this approach. In this paper, we investigate service/rescue robot navigation problem to illustrate DP fusion theory and its application. The robot fuses sensors' observation to decide the direction of its destination. To derive the a prior probability for DP fusion, we establish the relationship of the current direction of destination with the previous decision: the angle of destination's current direction is nearly the same as the angle between the previous decision on direction and the true direction. Combining with sensor observation model, we formulate the sensor fusion problem as a prediction problem in the form of state space model. Then we derive DP fusion algorithm based on MAP detection. Simulations show that the proposed DP fusion outperforms the traditional scheme and is more robust to parameter variations such as observation SNR.

Cognitive radio (CR) is promising toward future wireless communication. By incorporating co-existing primary system(s) and CRs into a cognitive radio network (CRN), it is possible to significantly enhance overall spectrum efficiency, provided that CRs can precisely explore appropriate spectrum and network activities, which we call CRN tomography by borrowing the terminology from medical imaging. CRN tomography can actively or passively measure or infer CRN parameters and traffic patterns. Conventional CR spectrum sensing to find the spectrum holes falls into a simplified scenario of passive CRN tomography. Like medical tomography, we propose active CRN tomography by a realistic probing signal with cooperative adaptive modulation and coding (AMC) according to SINR. We successfully demonstrate that the active radio resource tomography can support real-time adaptation to dynamic environments without any a priori information from radio channels.

Cognitive radio (CR) dynamically accessing inactive radio spectrum of the primary system (PS) at link level has attracted a lot of research interests. The cognitive radio network (CRN) organized by multiple CRs has been considered as an emerging wireless communication technology. In order to efficiently utilize the radio spectrum, the multiple access schemes of the CRN shall be considered together with physical layer (PHY) transmission schemes. In this paper, we propose a novel class of carrier sense multiple access (CSMA) based MAC protocols for the CRN while the PS is also operating with widely-applied carrier sensing protocols. Different from conventional CR either to transmit packets or not, our protocols with a feasible adaptive PHY transmission scheme allow possible transmission(s) for a CR even when the PS is actively transmitting. We analyze the proposed class of CSMA based MAC protocols and further propose the transmission strategy for each CR to improve the throughput of the CRN. Numerical results show that our proposed scheme improves the throughput of the CRN more than 36% and 100% as compared with the conventional CSMA and conventional CR operations, respectively.

In wireless communication systems, the signal strength/quality generally varies with the distances between transmitters and receivers. As a consequence, the throughput in the MAC layer (or the transmission data rate in the PHY layer) also varies accordingly. In this paper, we call this phenomenon as the rate-distance nature of the wireless communications. Inspired by our success in the study of the random distances between nodes in the wireless ad hoc networks, we pioneer employing the distance-related concept to study the rate-distance nature of the multiple access protocols. By selecting slotted Aloha as a pilot multiple access protocol, we successfully analyze and demonstrate the rate-distance adaptability of slotted Aloha with finite and infinite populations. We also demonstrate how the rate- distance concept can be applied to the rate adaptation of the MAC protocol in the cognitive radio.

The Authentication, Authorization, Accounting (AAA) infrastructure in Mobile IP network is designed to distribute keys to network entities for signaling message protection. In Mobile IP network, Regional Registration is employed to migrate the high signaling delay when a mobile user moves between network agents within the same visited domain. How to distribute keys in Mobile IP Regional Registration is still an open issue and adopting AAA infrastructure may be a suitable solution. However, in the literature, no work has a sound analytical study on Regional Registration in Mobile IP network with AAA infrastructure. In this paper, we develop a complete analytical model to investigate handover delay of Mobile IP network with and without Regional Registration. The accuracy of this model is validated by the developed simulations. From the proposed model, this paper precisely justifies performance improvement of Regional Registration in Mobile IP network.

Radio resource allocation has been widely considered in recent years to further improve communication system performance, while MIMO-OFDMA (multiple input multiple output - orthogonal frequency division multiple access) is one of key technologies in future wireless systems. In this paper we propose an algorithm to adaptively allocate power, bandwidth and antennas among mobile users in MIMO-OFDMA system; the channel equalization was integrated into our signal model to cancel the inter-antenna and inter-carrier interference. By the QR decomposition, we can eliminate the interference and simplified the mobile channel condition to nomadic case. Our numerical results demonstrate that, the proposed algorithm can achieve near-optimal performance while maintain linear complexity simultaneously. Furthermore, the proposed algorithm still can improve system capacity even when we include practical channel estimation and prediction bias into consideration.

Cognitive radios (CRs) with capability of spectrum sensing to fully utilize radio spectrum have been considered as a key technology toward future wireless communications. We can further leverage CRs to form cognitive radio networks (CRNs). When we consider sensing for CRNs, it is critical to identify the potential primary and secondary systems for construction of time-varying CRNs. In this paper we propose a general sensing algorithm to sense the spectrum usage and identify active systems under a multiple-systems coexisting environment over Rayleigh fading channel. We exploit unique characteristics of systems consisting of fundamental frequency, power spectrum density and four-order cumulant to accomplish the multiple systems sensing. Our algorithm can also be applied by detect and avoid (DAA) for UWB with a priori knowledge of system parameters in active systems.

3GPP 33.246 proposes key management mechanism (KMM) to distribute security keys for universal mobile telecommunications system (UMTS) multimedia broadcast and multicast service (MBMS). KMM introduces extra communication overhead to UMTS. The previous study, key-tree scheme (KTS), resolves this issue for the IP multicast network. However, this scheme may not be so efficient while applied in UMTS MBMS due to lots of storage space and heavy multicast traffic introduced, which may decrease the QoS of UMTS MBMS. In this paper, we propose a more efficient scheme, hash function scheme (HFS), to release both storage and communication overhead for KMM in UMTS MBMS. We first modify the KTS applied in the UMTS MBMS and then detail the execution of HFS, which is proven to be correct. We conduct an analytical model and simulation experiments to compare the performance between the UMTS KMM with KTS and with HFS. Our study shows that the proposed HFS can reduce both communication and storage overhead without damaging QoS of UMTS MBMS.

We propose a novel two-stage non-coherent SFH/BFSK detection scheme when the environment is interfered by an uncoordinated FH system. The first stage detects user number and power level of uncoordinated FH signals. Based on knowledge of detected side information from the first stage, the second stage performs SFH/BFSK maximum likelihood detection. When user number and power level of uncoordinated FH signals are unknown, the proposed two-stage detection scheme outperforms existing multilevel FSK conventional detectors and two-user multiuser detectors. In this scenario, simulation results demonstrate a critical role of the first-stage detector in the overall scheme when it provides the above side information. On the other hand, when knowledge of uncoordinated user number and power level is available to the desired user's receiver, simulation results also show superiority of the proposed second- stage detector over equal-gain receivers and self-normalized receivers.

Reducing the waste of the limited battery power in exchanging cluster maintenance messages is one of the important issues in designing clustering algorithm for the wireless ad hoc networks. Analyses show that this can be achieved by reducing the number of generated clusters and the variance of the number of cluster members. By assigning critical node (the only neighbor of boundary node) the highest weight (or priority) to be selected as a clusterhead, we show that the number of cluster maintenance overheads is reduced by the proposed distributed clustering algorithm with critical node first (DCA/CNF) based approaches. As a consequence, the limited battery power is conserved and the organized network architecture is power efficient.

Cognitive radio has been recently introduced to enhance spectrum efficiency, while orthogonal frequency division multiple access (OFDMA) as a multi-carrier technology is widely considered for future wireless communications. In this paper, we consider practical OFDMA cognitive radio operating environments at physical layer by a composite hypothesis test to identify channel status. Lloyd-Max Algorithm is used in channel identification function to cluster observed data and generate a threshold for hypothesis test. By incorporating such information from cognitive radio physical layer, we develop a novel three-step cross-layer optimization of OFDMA radio resource allocation, while keep fairness among users and maximization of total capacity in such time varying environment. The power, time slots and sub-carriers are jointly optimized by two separate optimal algorithms in three steps. Our results show that capacity of OFDMA can be maximized and fairness can also be maintained well by such a pioneer optimization in dynamic and time-varying communication environment.

Cognitive radio has been considered as a key technology toward future wireless communications due to better spectrum efficiency by accommodating secondary user system(s). We observe modern wireless communication systems widely applying adaptive modulation and coding (AMC) based on signal strength/quality, which has been ignored in earlier wireless networking research, and we call it as rate-distance nature. By pioneer introducing the rate-distance properties to slotted ALOHA multiple access (as an example of multiple access protocols) to mitigate interferences from the secondary system(s) to primary systems, we successfully demonstrate optimization of the secondary system's throughput, while maintain the primary system unchanged. Consequently, we optimize the spectrum throughput of the entire cognitive radio networks via joint physical and medium access control, rather than just spectrum utilization/efficiency at the physical layer.

In order to fully exploit wireless radio resource and then increase spectrum efficiency, cognitive radios shall sense and cognize environments so that the secondary system(s) may coexist with primary communication systems. In this paper, we acquire an effective set of cognitive information under a proposed spectrum sensing cycle for orthogonal frequency division multiple access (OFDMA) systems. To avoid interfering with primary systems, detecting existence of the primary system by tracking fundamental symbol rate of the primary system and measuring RSSI is the first step in coexistence. Furthermore, using cyclic property of OFDMA signal, we can precisely determine existence of a specific OFDMA system and its frequency band, via Neyman-Pearson criterion. Simulation results show that near perfection detection is achieved even at low SINR. With a priori knowledge of frame structure in potential primary systems, communication parameters, such as data transmission rate and resource allocation, are extracted by decoding frame header. Finally, we can determine available radio resource with respect to cognitive radios using rate-distance relationship, while guarantee interference to primary systems. Moreover, by parametric adjustment, our sensing procedure can be applied to any state-of-the-art OFDMA systems nowadays.

Organizing wireless ad hoc network into a cluster-based network architecture that requires the minimum number of cluster maintenance overheads not only reduces the waste of precious bandwidth but also conserves the consumption of the limited battery power. Analytical results show that the number of cluster maintenance overheads can be reduced by reducing the number of generated clusters and the variance of the number of the cluster members. By modifying the Quine-McCluskey algorithm, our results show that the required number of overheads to maintain the organized cluster-based network architectures is reduced by reducing the number of generated clusters and the variance of the number of cluster members. Furthermore, we also show that the reachability of the wireless ad hoc networks is also improved by the proposed MQM clustering algorithm.

Performance degradation due to Wiener phase noise which causes both common phase error (CPE) and inter-carrier interference (ICI) is a crucial challenge to the implementation of multi-carrier communication systems. In uplink OFDMA transmissions, simultaneous transmitted user signals give rise to multiple phase noise where conventional phase noise correction methods can not be directly applied since they target at single phase noise. To solve this problem, a unified phase noise corrupted uplink OFDMA signal model which can apply to any sub-carrier assignment schemes is derived. According to this model, two algorithms based on least-square and maximum likelihood criteria are proposed respectively. Performance simulations are given to illustrate the effectiveness of the proposed algorithms for uplink OFDMA systems. Our study also shows that interleaved scheme suffers more ICI than subband based scheme under phase noise environment.

Multi-carrier code-division-multiple-access (MC-CDMA) is a promising transmission technique for high-speed wireless multimedia communication in frequency-selective fading channels. In this letter, allocations of physical transmission rate, sub-carrier, and power are proposed for a MC-CDMA system that applies multi-code (MC)/variable-spreading-length (VSL) multi-rate access to minimize total transmitted power, where we consider users have different data rate and BER.requirements and LMMSE multiuser detection is used in the receivers. We derive transmission rate capacity and propose a simple admission control criterion that linearly relates the code length, data rate and BER requests of all users. The proposed iterative allocation algorithm jointly allocates the sub-carrier power of each user and solves the sub-carrier allocation problem

Orthogonal frequency division multiple access (OFDMA) is a promising technique which can provide high capacity in future communication systems. The total capacity of OFDMA can be maximized by dynamically allocating subcarriers among users according to channel condition. However, it is quite challenging to properly allocate subcarriers in mobile channels due to the time varying property. Existing approach designed for static users assigned the subcarriers with the best SNR to increase the total capacity but to lose fairness. Fairness can be restored by using max-min criterion or constraint limiting the ratios of user data rates to maintain some balance among users. But when users are mobile, the SNR considered should be replaced by the carrier to interference ratio (CINR) because of the presented intercarrier interference (ICI) due to Doppler spread. In this paper we successfully incorporate the ICI into our radio resource allocation algorithm to simultaneously optimize the total capacity and fairness for mobile users. The fairness and priority of user traffic are jointly considered in our adaptive algorithm. The algorithm is demonstrated outperforms the existing algorithm designed for static users, and very robust in realistic operation

We consider and investigate a generalized signaling method for non-coherent orthogonal FH/BFSK in band multitone jamming (BMJ). We let symbol tones not be restricted within one common band, but be pre-designed or randomly selected from all available tones. We present hopping assignments that are more resistant to jamming and multipath fading. Simulation results show that in n = 1 BMJ and AWGN, performances of all assignments have similar asymptotic behaviors in high signal-to- jammer-ratio (SJR) conditions, whereas in n = 2 BMJ and AWGN, there exists around 6 dB performance gain in the proposed assignments over the conventional FH/BFSK when the bit error probability (BEP) level is 10-2. In 2-path Rayleigh fading channel corrupted by n = 1 BMJ, the performance gain is at least 15 dB. The orthogonality between two contiguous symbol tones in conventional FH/BFSK is severely destroyed by the delayed second path. This kind of multipath interference, however, can be greatly alleviated by the generalized signaling presented in this paper.

Separation distance between nodes is an important index in characterizing the optimum transmission range, the most probable Euclidean distance between two random selected nodes, the node degree and the network connectivity of wireless ad hoc networks. However, because nodes are randomly deployed, the separation distances between nodes in the wireless ad hoc networks are also random. Thus, in this paper, we present methodologies to analyze three distance-related probability distributions: the distribution of the distance to the k-th nearest neighbor, the distribution of the distance between two random selected nodes and the joint distribution of the distances between nodes and a common reference node.

Phase noise resulting in common phase error (CPE) and inter-carrier interference (ICI) is a critical challenge to the implementation of OFDM systems. Modeling phase noise as a stationary Gaussian random process with the specified power spectrum density, different from conventional approaches which mostly relay on pilots to provide CPE estimation, we explore the statistical characteristics of the sufficient statistics then propose a pilot-aided decision-directed approach according to maximum-likelihood criterion. Numerical results demonstrate that the proposed algorithm enjoys 2 dB gain at moderate SNR and is quite robust against possible model mismatch

Allocations of physical transmission rate, sub-carrier, and power in MC-CDMA systems are proposed to minimize total uplink power consumptions for users of different data rate and bit-error-rate (BER) requirements with the receiver using linear minimum-mean-square-error (LMMSE) MUD. Based on multi-code (MC) and variable-spreading-length (VSL) strategies for multi-rate transmissions, we derive the discrete-rate transmission capacity of MC-CDMA and propose a simple admission control criterion for users of individual quality-of-service (QoS) demands. This criterion linearly relates the code length with the transmission rate and BER requirements of all users. An iterative power allocation algorithm is proposed after deciding all supportable transmission rates to allocate the sub-carrier power of each user and jointly solve the sub-carrier allocation problem. The results show that in either MC MC-CDMA or VSL MC-CDMA, the most power-efficient allocation is to distribute the power of each user over several sub-carriers of better quality and even concentrate on the best one, if it is exclusive.

Performance degradation due to Wiener phase noise, which causes both common phase error (CPE) and inter-carrier interference (ICI), is a crucial challenge to the implementation of OFDM systems. In this paper, we theoretically employ the Lorentzian model to investigate the autocorrelation function of the ICI weighting function, which is the discrete Fourier transform of the exponential phase noise process. This autocorrelation function can be shown to be the kernel of the covariance of the ICI. Based on this kernel, a pilot-aided decision-directed CPE estimator is proposed according to maximum-likelihood criterion. Different from conventional maximum likelihood approach which ideally assumes the ICI observed on different subcarriers to be independent identically distributed, we systematically derive the covariances among carriers and practically utilize them to enhance the estimation. Finally, three conventional CPE estimators are compared with the proposed scheme by computer simulation, the numerical results illustrate the effectiveness of the proposed algorithm.

We propose a multiuser detection scheme for channelized fast frequency hopping with binary frequency shift keying in uncoordinated, narrow-band frequency hopping systems. "Multiuser" here means uncoordinated users and the desired user. This scheme consists of channel state detector and multiuser detector. Both detectors are Gaussian approximated energy combiners and are sub-optimal in the maximum a posteriori probability sense. In a symbol interval, the channel state detector first detects the existence of narrow-band frequency hopping signals for each chip, and then the multiuser detector properly combines the received chip energies to make the decision. Simulation results show the superiority of this scheme over other existing approaches.

We investigate trellis coding with multiuser detection (MUD) applied in direct-sequence code-division multiple-access (DS/CDMA) communications. A generalized model called multi-sequence model is applied to systematically design the transceivers. In multi-sequence model, each user can be assigned more than one spreading sequence and the coding, modulation, and spreading sequences are jointly considered based on the extended Ungerboeck's set partitioning principle to maximize free distance. We derive the optimum detector under the maximum likelihood criterion. To measure the influence on the bit-error-rate due to coding and multiple-access interference, asymptotic multiuser coding gain (AMCG) is defined to provide lower and upper bounds for transceivers. The numerical experiments justify the precision of AMCG to evaluate system performance. Verified by numerical analysis, a transmitter designed by multi-sequence model can achieve significant coding gain over uncoded systems while applying optimum detector, and the performance of coded systems is severely degraded without MUD. Therefore, these facts not only demonstrate the value to jointly design coding, modulation, and spreading sequences in trellis-coded DS/CDMA communications but also emphasis the needs of MUD to optimize the system performance.

In the wireless ad hoc networks, for prolonging the communication duration of the nodes, the transmission power is required to be minimized to conserve the limited battery life. In addition, for the wireless ad hoc networks to be applicable, the networks are required to he connected. Unfortunately, the two requirements are against each other. In this paper, by analyzing the probability of isolated node, we obtain the relationships between transmission range, service area and network connectedness. Furthermore, we propose a generalized (N,B) wireless ad hoc network model to investigate the impact of boundary nodes on the network connectivity, equivalent service area and average node degree.

Distinct from conventional approaches, we apply a generalized data-aided signal model which can jointly consider the cyclic prefix with the preamble/pilot to propose three data-aided frequency offset estimation algorithms based on the maximum likelihood criterion. The proposed algorithms differ from each other in the processing domain and the observation space. Not only can they systematically estimate both the integer part and the fractional part of the frequency offset, but they can also possess the maximum acquisition range of a discrete time system. The effectiveness of the proposed schemes is verified by mathematical analysis and computer simulation.

We study two multi-rate access methods of multicarrier CDMA systems, based on transforming the concepts of multi-code (MC) and variable-spreading-length (VSL) to frequency domain. A general and uniform system model accommodating both access methods is constructed, providing a framework of programmability for system integration. We thereof develop multiuser detections (MUD) applicable to both access methods. Chip-based filtering in frequency domain replaces the conventional match-filter band and yield novel detection behaviors. The signal properties and detection performance in multi-rate traffic are analyzed and demonstrated with numerical simulations, which manifests the multi-rate approaches and the superiority of MC access over VSL access in uplink MC-CDMA. The compared study shows the domination of spreading length over the interference pattern in MUD performance.

We study the continuous-time deterministic traffic regulation problem. We propose a regulation form shown to be the optimal deterministic traffic regulator in the sense that it outputs the most packets while satisfying the constraint on the output process. We further investigate the subtle relation between continuous-time and discrete-time optimal deterministic regulators, and reduce our general regulation form to the known discrete-time optimal deterministic regulator when restricting arrival (departure) instants to integers and packet size to unity. Therefore, by extending traffic-regulation theory to continuous time, our work provides a fundamental framework for future research regarding quality-of-service (QoS) guaranteed network design/analysis in continuous-time.

To enable multimedia real-time applications over next-generation wireless code-division multiple access (CDMA) packet-switching networks, previous efforts show a proper scheduling policy is the key to provide delay-guaranteed access services to various traffic types with different bit error rate (BER) requirements. Considering the support of the prevailing Internet protocol (IP) with variable-length packets in future mobile networks, we develop a mathematically delay-optimal medium access control (MAC) protocol over multicode CDMA (MC-CDMA) environments under the continuous-time assumption. From our investigations, we suggest that a good MAC protocol should be designed by using a proper single-server scheduling policy to guarantee packet-delay, and controlling the maximal number of simultaneous spreading-code transmissions to maintain the required BER. We further evaluate the performance of some MC-CDMA MAC protocols supporting QoS on BER and packet-delay, and show that MAC schemes conforming to our design rules give better performance on packet-delay when maintaining acceptable BER of various traffic types.

By extending our frequency-domain approach (see Cha'o-Ming Chang and Kwang-Cheng Chen, IEEE Communications Letters, vol.4, no.11, p.331-3, 2000) to practical scenarios such as frequency selective slowly fading channels, we justify the value of our proposed frequency-domain approach to multiuser detection in DS-CDMA communications systems. With this frequency-domain approach, which maintains frequency-domain orthogonality between different users, DS-CDMA communication is MAI-free even in frequency-selective slowly fading asynchronous channels. This frequency-domain approach not only saves the cost of maintaining synchronous channels as in practical communications systems employing time-domain orthogonal codes (e.g. IS-95 and cdma2000), but also enjoys low complexity properties as in conventional point-to-point communications; it therefore holds many desirable properties attractive to practical communications systems.

The multi-tone CDMA is a spectrum-efficient multiuser transmission technique, and regarded as a promising platform for multimedia communications. We study the multi-rate physical-layer transmissions, based on multi-code (MC) and variable-spreading-length (VSL) access. A general and systematic system model accommodating both access methods with multiuser detection (MUD) is constructed, which is useful for programmable system integration. We investigate the system characteristics under multi-rate transmission, including multiple access interference (MAI) and intercarrier interference (ICI), and the performance comparisons between MC and VSL schemes in frequency-selective Rayleigh fading channels are studied.

In this paper we propose a novel multiuser detector for the synchronous CDMA system. Its BER performance approaches the optimum MMSE multiuser detector while the complexity only polynomially increases to the number of active users. We also demonstrate its complexity and performance against the optimum MMSE multiuser detector and decorrelator. In addition to the synchronous AWGN channel case, we also provide the mechanism for the asynchronous case, Rayleigh fading channel case, and frequency selective channel case.

In this paper, we study multi-rate transmission for MC-DS-CDMA systems based on the concepts of multi-code (MC) access and variable-spreading-length (VSL) code access. The general system model is derived for multi-rate MC-DS-CDMA systems of either multi-rate access and multiuser detection (MUD) are proposed thereof. We analyze the characteristics of multi-rate MC-DS-CDMA signals and investigate the behavior of the MUD. Compared studies and simulations among the proposed MUD and access methods suggest VSL-code access with the proposed LMMSE detection to multi-rate MC-DS-CDMA systems, especially for transceiving high-rate users.

Both direct sequence and frequency hopping spread spectrum communication systems co-exist in the unlicensed band such as 2.4 GHz ISM band. We propose a novel structure to obtain the channel information so that more robust communication is possible. Both theoretical and numerical results are presented to demonstrate effectiveness.

Being confronted with numerous MAC protocols designed under a variety of networking considerations, we envision a software-defined MAC controller that can be "re-configured" to different MAC protocols. This paper presents a unified algorithm for wireless MAC protocols - a pioneer trial of this vision that benefits future wireless networks. This unified algorithm is based on the concept of MULCAR, a generalized model for MAC protocols proposed by Chen and Sun. We observed that most protocols operate in a cycling fashion and identified major differences between each algorithm. Combining this with the concept of MULCAR, we unified several representative wireless MAC protocols into one parameterized algorithm. Among them are ALOHA with geometric backoff, binary exponential backoff and Q-ary collision resolution algorithm, p-persistent CSMA, CSMA/CA, and GRAP. One can thus have the unified algorithm operate as different MAC algorithms with proper parameter setting, which enables the development of the software-defined MAC controller.

In this paper, a new traffic source, priority data, is considered to access the channel with the highest priority among conventional CBR, VBR and ABR sources. In order to achieve and maintain QoS guarantee for admitted CBR and VBR sources, a QoS guaranteed wireless access protocol, the priority polling with reservation (PPR) protocol that consists of priority polling scheme (PPS) and randomly addressed polling (RAP) is proposed. We provided the jitter and delay constraints for CBR and VBR sources by reserving bandwidth for the mean PD sources that are active during the time to transmit CBR and VBR sources. The results show that under the preemption of PD sources, the QoS constraints for CBR and VBR sources are guaranteed.

In multimedia networks, certain applications (e.g., voice and video sessions) can tolerate some delay violations and packet losses. For these applications, we proposed a novel, more flexible traffic characterization approach-L/sup p/-sense constraint functions-as a generalization of deterministic constraint function. Intuitively, feeding more packets than provided resources will degrade the quality-of-service (QoS) of multimedia applications. However, if we can control a multimedia application's output to conform to constraint functions in L/sup p/ sense, then the portion of time prone to performance-bound violations has a normalized tail bound decaying with x/sup -p/. With the notion of L/sup p/-sense traffic characterization, we investigate algorithms to shape an arbitrary arrival trace to conform to a given function f in L/sup p/ sense, i.e., L/sup p/-sense regulation algorithms. It can be shown that under certain conditions LP-sense regulation departure times converge to deterministic regulation departure times when p/spl rarr//spl infin/. Hence L/sup p/-sense regulators can be regarded as generalizations of the continuous-time optimal deterministic regulators. By properly choosing p, more packets of a multimedia source can pass through L/sup p/-sense regulators while its normalized bound-violation ratio decays fast enough to meet its QoS requirements. The basic "leaky bucket" type L/sup p/-sense regulators are also studied as an illustration.

This paper proposes new multiuser detectors for frequency-hopped spread spectrum multiple-access (FH-SSMA) based on binary frequency shift keying (BFSK) modulation and channelized frequency hopping. With knowledge of hopping sequences and envelopes of active users, the proposed scheme is a sub-optimal detector under maximum likelihood test. Diversity combining is employed as an anti-multiple-access interference technique which improves the performance significantly. In slow frequency-hopped systems, we demonstrate that the proposed multiuser detector combined with diversity is robust to multiple-access interference. In fast frequency-hopped systems with heavy load of multiple-access, the detector can afford more simultaneous users than the multiuser detector with M-ary frequency shift keying (MFSK) modulation.

Different from multiuser detection, synchronization in multiuser communications systems has its unique features. We revisit effective multiuser synchronizers in a systematic way and classify them into two categories: linear multiuser synchronizers and the multiuser synchronizers based on the frequency domain approach. Both categories of estimators have their special advantages according to their characteristics and are applicable to practical DS/CDMA communications systems.

By generalizing and extending the effective user data model for multi-rate access, we precisely study the general performance of various proposed multi-rate schemes for wideband CDMA systems. A linear multiuser receiver structure with antenna arrays that can fit all the considered multi-rate schemes is therefore presented. With the extended data model and the proposed receiver structure, each multi-rate scheme is only distinguished by the bandwidth requirement and the spreading codes used. The numerical results suggest that the decision on employing which multi-rate scheme can be viewed as a trade-off between the power consumption and the hardware complexity of the receivers, as well as operating scenarios.

Previous work on multiuser synchronization assumed that either timing recovery or carrier phase recovery schemes were available. Therefore, a generalized joint linear timing and carrier-phase estimation scheme to effectively estimate both the received timings and carrier phases at the same time is of paramount interest. Based on the idea to linearly combine the sufficient statistics of timings and carrier phases, we introduce the generalized joint linear timing and carrier phase estimation scheme with commensurate complexity of the generalized linear timing estimator. To avoid the difficulty of the design by minimizing the mean-square error (MSE), a simple and novel design criterion for this generalized joint linear estimation is proposed. With a discrete timing assumption, we not only mathematically prove that the MSE of the proposed estimator converges to zero as noise vanishes for an arbitrary number of users, but also conduct simulations to verify it. On the other hand, for the general case with a continuous timing assumption, arguments and simulations are provided to confirm the effectiveness of our proposed estimator for an arbitrary number of users. We further show that such an efficient linear-complexity joint estimation scheme is effective in the near-far environment. Consequently, the nonpolynomial (NP) hard multiuser synchronization over additive white Gaussian noise (AWGN) channel is successfully solved.

Previous works on multiuser synchronization assumed that either timing recovery or carrier phase recovery scheme is available. Based on the ideas to linearly combine the sufficient statistics of timings and carrier phases, we introduce the generalized joint linear timing and carrier phase estimation scheme which is of commensurate complexity with the generalized linear timing estimator. To avoid the difficulties of the design by minimizing the mean square error, a simple and novel design criterion for this generalized joint linear timing and carrier phase estimation is proposed. With he discrete timing assumption, we not only mathematically prove that the mean square error of the proposed estimator converges to zero as the noise vanishes for an arbitrary number of users, but also numerically verify it. For the general case with continuous timing assumption, we also confirm the effectiveness of our proposed estimator for an arbitrary number of users. With this effective linear complexity joint timing and carrier phase estimate, the non-polynomial (NP) hard multiuser synchronization over AWGN channel is completely solved.

To serve traffic with different characteristics and service requirements in multimedia wireless packet networks, we propose a multiaccess methodology: packet by packet generalized processor sharing/random addressed polling (PGPS/RAP) with guaranteed quality-of-service (QoS) to serve constant-bit-rate (CBR), variable-bit-rate (VBR), and available bit rate (ABR) traffic sources. This multiaccess methodology without any specific frame concept can guarantee worst-case delay of CBR and VBR traffic sources and therefore can guarantee the QoS of delay-sensitive, jitter-sensitive CBR and VBR traffic sources. The derived delay bound of specific traffic only depends on its own traffic parameters. This impressive characteristic benefits bandwidth allocation and consequently results in a simple call admission control policy. Other important issues, including a simple link control mechanism for CBR and VBR over unreliable channels, are also suggested.

A general multi-layer collision avoidance/resolution (MULCAR) random access protocol has been developed by the concept of tree expansion to comprehend the classification of multiple access algorithms/protocols in the literature. Applied to delay-sensitive traffic control, the dynamic behavior of MULCAR is analyzed in different operation modes assuming blocked traffic returns inactive. The system in each mode is shown to deliver its ultimate throughput and a have regular correspondence between the resulting average delay and the maximum number of granted subsets. We also highlight the distinct effect of each mode on the system performance.

We study the multiple access protocols over the CATV (cable TV)/HFC (hybrid fiber coaxial) networks. The core of the medium access control in such an environment is the multiple access protocol since many subscribers share the same cable (or fiber) for transmissions. We then outline a general form of almost all historical multiple access protocols and reveal some common properties among these miscellaneous algorithms. Based on these observations, we describe the draft protocol (Ternary Tree Collision Resolution Algorithm with Variable Entry Persistence, P-Tree for short) for the IEEE P802.14 and use simulations to depict the dominant factors for designing efficient protocols over CATV/HFC networks.

In this paper the PGPS/RAP multiaccess proposed in Chia-Sheng Chang and Kwang-Cheng Chen (1998) is reinvestigated for realistic channel characteristics such as unreliable transmission and propagation delay effect. With our proposed link control scheme, the packet error rate of PGPS/RAP in an unreliable channel can be effectively improved while the packet delay for constant-bit-rate (CBR), variable-bit-rate (VBR) traffic is still guaranteed. Furthermore, with the technique of pipelining, we modify the PGPS/RAP to adapt the long propagation delay effect for guaranteed QoS access to CBR and VER traffic of the end-users. This modified PGPS/RAP without any specific frame concept can still guarantee worst-case jitter of CBR and VER traffic sources and therefore can guarantee the QoS of jitter-sensitive CBR and VER sources.

Timing estimation has been shown an important issue in multiuser communication (Buehrer et al. 1996). Chang and Chen (1997) proposed the optimum timing estimation and confirmed the need of two-stage timing recovery policy: acquisition and tracking. While the complexity of the tracking scheme applying the gradient search algorithm increases linearly to the number of users, the timing acquisition still has non-polynomial (NP) complexity. Timing acquisition with linear complexity, as an M-ary hypothesis testing problem, is therefore needed in practical multiuser communications. This paper not only points out the difference between the design of a linear timing acquisition scheme and the linear detector, but also proposes a novel, simple, and effective criterion for linear timing acquisition design. With a discrete timing assumption, we prove that the estimation error of the linear timing acquisition scheme based on this new criterion converges to zero in L/sup 2/ as the SNR increases to infinity for an arbitrary number of users. In the general case of continuous timing, explanations and simulation results are provided to demonstrate the effectiveness of the linear acquisition scheme by the new criterion. We demonstrate such a linear scheme existing in interference limited operation for an arbitrary number of users.

A series of study to design guaranteed quality-of-service wireless access to broadband networks is presented, based on randomly addressed polling and its generalized expansion multiple access protocol.

The level of multiple access interference (MAI) in code division multiple access (CDMA) communication systems is a time-varying parameter related to the number of active users. Almost all existing multiuser detection schemes were designed based on a priori information of the active users. In many situations, however, the multiuser receiver does not know the number of active users, and the receiver designed for the detection of all users may lead to poor performance. To develop a more efficient detection scheme in practical applications, we propose a two-stage detection structure consisting of preprocessing (identification) and postprocessing (detection). In the preprocessing, we apply the subspace concept and a method based on the multiple signal classification (MUSIC) algorithm to identify the active users while requiring only a priori knowledge of all of the users' signature sequences. The proposed preprocessor is shown to be asymptotically near-far resistant, and to have the ability to identify the active users in a simple and reliable way. While in the detection process, as we efficiently use the active users' information in every observation interval, the performance is clearly improved compared to the conventional structure without identification. Moreover, the effect of imperfect identification on the decorrelating detector is also extensively analyzed. Though the decorrelating detector's inherent near-far resistant characteristic is impaired by imperfect identification, the proposed structure still outperforms the conventional structure in the general near-far environment.

The randomly addressed polling (RAP) protocol together with the generalized multi-layer collision resolution (MCR) protocol have been proposed and demonstrated as efficient, flexible and robust MAC protocols over the wireless network. We present a comprehensive study on the adaptive control schemes and the stability issues of this family of protocols over some centralized controlled network topologies, such as wireless, CATV (cable television), HFC (hybrid fiber/coaxial), and even satellite networks. Under the assumption of an infinite number of stations, we show that the proposed adaptive algorithm actually helps in stabilizing this family of protocols, and we also show that each packet only experiences a finite delay when the adaptive control scheme is employed.

A general multi-layer collision avoidance/resolution (MULCAR) protocol for multiple access in packet switching networks is proposed. Such a scheme possesses great flexibility and can be widely applied in wireless or even wire-based networks, by adequate tailoring of the layers and adjustment of the parameters accordingly. It is originated from the savvy of a classification of historical multi-access protocols by the tree-expansion point of view, which includes collision anticipation tree expansion (CATE) and collision resolution tree expansion (CRTE). Therefore, MULCAR basically possesses the competence of exploring the insight of general multiple access problem.

We study the problem of wireless access to asynchronous transfer modes (ATMs). We consider three classes of ATM sources: constant bit rate (CBR), variable bit rate (VBR), and available bit rate (ABR). We propose a polling scheme with nonpreemptive priority. Under such a scheme, we derive sufficient conditions such that all the CBR sources satisfy their jitter constraints and all the VBR sources satisfy their delay constraints. The remaining bandwidth is used by the ABR sources, for which we adapt a random access scheme proposed by Chen and Lee (1994). For this random access scheme, we derive the throughput-offer load characteristic, and thus the capacity. Based on this, we propose adaptive random access schemes that track the offer load to its optimal value. Our simulations show that our adaptive schemes maintain a high throughput with respect to the whole range of system load.

A sample-correlate-choose-largest (SCCL) algorithm is generalized to design a family of efficient baseband digital signal processing (DSP) bit synchronizers. The common feature among maximal likelihood, minimal likelihood, and zero crossing in designing SCCL type DSP bit synchronizers gives us a possible unified point of view in the general design of synchronizers. Optimal signal waveform of "+---" and "-+++" has been derived for this family of bit synchronizers under the signal bandwidth constraint of four times bit rate along with the performance analysis.

The performance of synchronous spread-spectrum multiple-access (SSMA) communications based on direct-sequence (DS), slow frequency-hopped (SFH), and fast frequency-hopped (FFH) systems for wireless local communications of micro-cellular personal communications is analyzed. Using an indoor multipath fading channel model with clusters of arriving rays, we investigate multiuser DS systems with RAKE and diversity reception by selection combining (SC), multiuser SFH systems with equal-gain (EG) diversity reception, and multiuser FFH systems with correlated EG and self-normalization (SN) combining techniques. Reed-Solomon codes are considered to further improve the system performance. Given a fixed available bandwidth with narrow band interference (NBI), capacities and packet error rates are determined under various system configurations. Total capacities of hybrid frequency-division multiple-access (FDMA)/SSMA (DS and SFH) systems are compared with those of wide-band SSMA systems. For high data rate communications, wide-band DS-SSMA systems have larger capacities than hybrid FDMA/DS-SSMA systems. For low data rate communications, a capacity comparison between wide-band DS-SSMA and hybrid FDMA/DS-SSMA systems depends on fading statistics. Hybrid FDMA/SFH-SSMA systems have larger capacities than wide-band DS-SSMA systems, FFH-SSMA systems could not provide satisfactory performance due to correlation among hopping bands.

For conventional authentication protocols, distribution of session keys and maintenance of large databases are serious problems especially for large-scale wireless networks. ID-based authentication protocol eliminates the problem while it contributes the heavy computation load. We propose a hybrid end-to-end authentication and key agreement (AKA) protocol which provides authentication and key exchange between both end entities. It not only eliminates the drawbacks of conventional protocols but also reduces the computation load. Services of message confidentiality, caller ID confidentiality, service request intractability, and fraud control are provided. Roaming and handover, are also taken into consideration here.

IEEE 802.11 is the new wireless network standard. By use of two-dimensional Markovian analysis, we derive the throughput and average delay of the MAC part of IEEE 802.11 to demonstrate the efficiency of the CA (collision avoidance) mechanism. Furthermore, we also focus on its performance with the existence of hidden terminals.

Past research on trellis-coded modulation (TCM) mostly deals with additive white Gaussian noise (AWGN) or homogeneous fading statistics. A multicarrier scheme over TCM is proposed over nonuniformly distorted (ND) channels. We consider quadrature amplitude modulation (QAM) to analyze the proposed TCM, and show the effectiveness of the proposed scheme. The multicarrier approach effectively mitigates nonuniform distortion problem by dividing the entire band into groups of labeled carriers (subchannels with quality indexes) and weights the metrics separately according to the frequency response of the individual carrier. The optimum weight taps are shown to be inversely proportional to the noise level of the respective carrier. Furthermore, the weights are given to make the uneven noise response "flat" so that the weighting function results in a "whitening filter".

We analyze a class of suboptimum linear multiuser detectors for synchronous code division multiple access (CDMA) transmission over Rayleigh fading channels. The general structure of the detectors consists of a bank of matched filters, whose output vector is then linearly processed, and then a set of threshold devices make the final decision. Based on the simple linear structure, the performance of linear minimum mean square error (LMMSE) estimator that has the minimum Bayesian MSE among linear estimators is analyzed. We then propose a modified LMMSE estimator to enhance the robustness in the near-far environment at the sacrifice of slightly larger average error probability compared to the earlier LMMSE estimator.

Coexisting analog frequency modulation (FM) based on frequency division multiple access (FDMA) and narrow-band code division multiple access (N-CDMA) in adjacent spectrum is studied. The allocation on different spectrum can avoid the serious performance degradation as transitioning from fully loaded analog FM/FDMA to N-CDMA. The guard-band needed between the adjacent spectrum to maintain acceptable performance degradation is derived and evaluated. Numerical results indicate that at least a 255 kHz bandwidth to separate the two systems is necessary. The worst case of the interference from FDMA degrades the performance of N-CDMA, even under appropriate guard-band separation. This study demonstrates the capacity reduction of N-CDMA as coexisting analog FM/FDMA in adjacent spectrum.

We generalize the multiuser (CDMA spread spectrum) communication systems to the fading environments. We first extend Verdu's (1986) conventional optimum receiver to Rayleigh-fading environments and then evaluate its performance. Having no knowledge of the received power at the receiving end, we therefore need an estimator to efficiently estimate the received signal strength of each user in fading environments. A linear minimum mean square error (LMMSE) unbiased estimator is proposed to attain this goal. By using the minimum mean square error (MMSE) Bayesian estimation, we further propose the LMMSE bit estimator for efficient demodulation. Its performance is close to the optimum multiuser receiver but with a much simpler polynomial complexity. To further reduce the complexity, we extend the LMMSE estimator to the sequential LMMSE estimator. In sequential estimation, we do not need to implement the matched filter banks and to perform the matrix inversion when estimating. In addition, it converges after approximately 2k iterations, where k is the number of users. With this fast convergence property and the simple structure, the sequential LMMSE estimator provides an attractive alternative to the implementation of a multiuser system.

Group randomly addressed polling (GRAP) protocol has been demonstrated as an efficient and reliable multiple access protocol for wireless data networks. To serve time-bounded or integrated service traffic, a reservation technique is proposed in this paper to modify GRAP as reservation-group randomly addressed polling (R-GRAP) protocol. We first evaluate its performance for voice wireless networks to highlight the adequacy of adopting R-GRAP for future personal communications. Based on our evaluations to adopt R-GRAP to serve integrated voice and data traffic, we demonstrate the superiority of R-GRAP for wireless integrated service networks.

In multi-cell wireless networks such as wireless LANs under standardization (IEEE 802.11 and ETSI's HIPERLAN), we must consider interference interaction among different cells as there are a very limited number of channels for operation. We introduce a theoretical analysis to evaluate CSMA in multicell wireless networks by taking hidden terminals, interference, and cell-overlapping into account. Both nonpersistent CSMA and CSMA with a 4-way handshaking as the IEEE 802.11 draft are considered in this paper.

High speed indoor diffused infrared wireless transmission is greatly needed for data and multimedia applications. We investigated the error rate performance of OOK (on off keying), NRBI (non-return-to zero with bit insertion), M-ary PPM (pulse position modulation), and two novel coded direct detect modulations, DCGPPM (differentially coded and guarded PPM) and MRLC (modified run length code), by adopting representative channel modeling from measurements and simulations. Practical implementation issues have been further considered. From our results, we suggest DCGPPM at 10 M bps, 4-ary PPM or DCGPPM at 20 M bps, OOK or NRBI at 40 M bps; and MRLC at 100 M bps, for the baseband direct detect transmission of indoor wireless optical transmission.

In addition to PN code acquisition, PN code tracking is an important issue in direct sequence spread spectrum communication which is popular in personal communications and cellular communications. Tracking performance to maintain reliable continuous communications in multipath fading channels has not been carefully investigated. The authors analyze the noncoherent delay locked loop and tau dither loop in the two-ray Rayleigh fading channels. With numerical results, the authors observe that the mean time to lose lock is generally increased in strong fading compared with the AWGN case. However, such fading results in the degradation of tracking error performance, which may have very significant impacts in certain circumstances, especially the second ray being comparable with the first ray.

A combined polling and random access protocol (PRAP) with centralized control for integrated voice and data wireless networks is introduced in this paper. PRAP uses a centralized polling strategy to control network traffic and a distributed random access mechanism to enhance the services to mobile users. Since different data types have different characteristics of the channel usage, PRAP also gives the base station the control ability to fit those characteristics and improves the channel utilization dynamically.

A low complexity slow frequency hopped wireless data communication system with rapid synchronization is proposed, designed, and implemented. Simulations to support the study of this approach has been conducted and reported. With only a knowledge of the set of hopping patterns, such an architecture can quickly collect the exact hopping pattern at the initial starting band to achieve reliable data communication in the sequel. Such an approach should be useful in many personal communication systems with mobile consumer electronics applications such as personal data assistants.>

This paper presents a MAC (medium access control) layer protocol GRAPO (optimized group randomly addressed polling) for wireless (local area) data network. GRAPO statically optimizes the original version of GRAP proposed by Kwang-Cheng Chen (1992) to deliver smooth convergence and better throughput/delay performance. It is a good candidate of the MAC protocol for wireless LANs and other wireless network applications.>

SCCL, a new bit tracking loop based on the sample-correlate-choose-largest design principle, was shown to be an effective bit synchronizer. However, the analysis common in analyzing digital phase locked loops, proceeds on the assumption that the noise samples are independent which is not necessarily true in practice. The performance of SCCL, is analyzed without the independence assumption by considering the second-order statistics to complete the analysis. This approach is also useful to analyze other digital phase-locked loops or baseband digital signal processing schemes in digital communication systems.>

We introduce the application of SCCL that is based on sample-correlate-choose-largest procedure as a coherent baseband PN-code tracking loop applying biphase-level signals. Three adjacent estimates are formed by correlating the samples of the baseband waveform for each bit. We choose the corresponding timing (phase) of the estimate with the largest magnitude as the current correct timing (phase) and update it for each bit. Only one summation circuit is required due to the digital realization of the SCCL. The correlation properties of the samples from maximum length codes using the biphase-level signal set are investigated. Tracking performance is theoretically analyzed in both steady-state and transient conditions via a finite-state Markov chain model. The numerical results demonstrate strong PN-code tracking characteristics of SCCL.>

In wireless networks, multiple-cell structure to cover a wider area is a common approach. As the channels are usually hard to predict due to fast, deep, and dynamic fading, many traditional protocols like token passing, carrier sense multiple access, etc. are not appropriate to maintain successful performance. Slotted ALOHA and its variations are still attractive approaches. Traditional analysis on slotted-ALOHA is based on single-cell structure which is not the best model in future wireless personal communication networks. The authors study the performance of the slotted-ALOHA in joint cells of wireless networks. The impact of joint cells is well analyzed and numerical results are provided.>

The authors propose a new bit tracking loop for biphase signals which is implemented like the maximum a posteriori probability (MAP) optimal bit synchronizer by the sample-correlate choose-largest algorithm except that the estimator is sampled and moved at most one sample each bit time. A mathematical Markovian model for analysis is used. The performance of the bit tracking loop, the mean square error of the jitter and the average acquisition time, are theoretically derived. The numerical results of performance analysis for various signal-to-noise ratios are found through computer evaluations. The data obtained illustrate that this new structure is a very effective bit synchronizer for digital communications systems applying digital signal processing techniques.>