Mobility management associating all user equipments (UEs) to proper base stations (BSs) (also known as handover, HO) to achieve the designated performance optimization is one of the most crucial functions in mobile networks. Although effective mobility management has received considerable research attentions, existing schemes follow event-driving operations, in which HO decisions are made based on events of performance degradation that BSs/UEs passively suffer from or proactively foresee. However, the performance and decisions of these schemes are highly subject to the identified events, to lose generalization and better performance under unidentified events. To address this issue, we propose a foundation model (FM) based mobility management for the sixth generation (6G) mobile networks inherently supporting artificial intelligence (AI) computing, in which the FM generates the HO decisions for all UEs to maximize the overall throughput under the constraints of ping-pong rate and HO failure (HOF) rate by implicitly taking moving trajectories, traffic demands, channel conditions of all UEs and available resources of BSs into account. To this end, a hierarchical model structure composed of Long Short-Term Memory (LSTM) networks with multi-head attention (MHA) is adopted, which is trained by emulated datasets with augmentation. The performance evaluation results show that the proposed scheme outperforms the state-of-the-art schemes in terms of the average throughput over all UEs while satisfying the required ping-pong rate and HOF rate, and justify the robustness of the proposed FM under different network deployment scenarios.

The rapid evolution of Industry 4.0 has led to the emergence of smart factories, where multirobot system autonomously operates to enhance productivity, reduce operational costs, and improve system adaptability. However, maintaining reliable and efficient network operations in these dynamic and complex environments requires advanced automation mechanisms. This study presents a zero-touch network platform that integrates a hierarchical Open Radio Access Network (O-RAN) architecture, enabling the seamless incorporation of advanced machine learning algorithms and dynamic management of communication and computational resources, while ensuring uninterrupted connectivity with multirobot system. Leveraging this adaptability, the platform utilizes federated deep reinforcement learning (FedDRL) to enable distributed decision-making across multiple learning agents, facilitating the adaptive parameter reconfiguration of transmitters (i.e., multirobot system) to optimize long-term system throughput and transmission energy efficiency. Simulation results demonstrate that within the proposed O-RAN-enabled zero-touch network platform, FedDRL achieves a 12% increase in system throughput, a 32% improvement in normalized average transmission energy efficiency, and a 28% reduction in average transmission energy consumption compared to baseline methods such as independent DRL.

Toward intelligent computations in the fifth generation (5G) mobile networks, the Open Radio Access Network (O-RAN) Alliance has introduced the O-RAN architecture, which enables two unprecedented platforms: Non-Real-Time (Non-RT) RAN Intelligent Controller (RIC) and Near-Real-Time (Near-RT) RIC connected via the A1 interface. Although the standards for the functions and procedures of the A1 interface has been provided by the O-RAN Alliance, the practical implementation of A1 still suffers significant challenges due to 1) incompleteness of existing standardization procedures, 2) lack of mechanisms to ensure the integrity of procedures, and 3) lack capability to support large data storage. To address these challenges, this paper therefore provides the design and implementation of the A1 Application Protocol (A1AP) with particular focus on the A1 Policy Management Service (A1-P). To support all the functions and procedures of A1-P, this paper provides the designs and implementations of the A1 Policy Management Service Component (A1 PMS) in the Non-RT RIC, and the A1 Mediator in the Near-RT RIC, which enable not only all the A1-P procedures, but also the management of policy types, policies, policy statuses, and association of the rAPPs in the Non-RT RIC and the xAPPs in the Near-RT RIC. To ensure the integrity of A1-P, we propose additional steps in existing procedures to handle abnormal cases. To further support large data storage particularly needed for intelligent computation, all the A1-P procedures are redesigned to support InfluxDB (a database allowing data storage on hard disk drive). Through practically establishing the Non-RT RIC and Near-RT RIC platforms connected with the proposed A1-P design, and connecting the Near-RT RIC with the RAN emulator through the E2 interface, we demonstrate the practicability of our design to support the use case of energy saving (ES).

Low Earth Orbit (LEO) satellites in the nonterrestrial networks (NTNs) are challenged by high satellite moving speeds and dynamic link conditions, which lead to frequent and suboptimal handovers. A handover mechanism based on a composite quality indicator (CQI) that balances the trade-off between the quality and longevity of a link is proposed. To capture the nonlinear behavior of the links between the LEO satellites and the user terminal (UT), two logistic functions (LFs) are employed to dynamically adjust the weight to generate the CQI and the value of the handover threshold, respectively. By using the Two-Line Element (TLE) data, a genetic algorithm (GA) is developed to find the optimal parameter values to facilitate the two LFs by searching a multidimensional solution space. Simulation results demonstrate that the proposed handover mechanism not only enhances link quality and longevity but also reduces the number of handovers compared to the elevation-based and max remaining visibility time (RVT)-based methods.

With promising benefits of splitting deep neural network (DNN) computation loads to the edge server, split computing has been a novel paradigm achieving high-quality artificial intelligence (AI) services for the energy-constrained user equipments (UEs). To satisfy the service demands of a large number of UEs, traditional edge-UE split computing evolves toward multitier split computing involving the edge and cloud servers with different capabilities, leading to a “complex” optimization involving communication and computation. To tackle this challenge, in this article, we propose a multitier deep reinforcement learning (DRL) decision-making scheme for distributed splitting point selection and computing resource allocation in the three-tier UE-edge-cloud split computing systems. With the proposed scheme, the high-dimensional optimization can be tackled by the UEs and an edge server with different control cycles through performing local decision-making tasks in a sequential manner. Based on the policies updated by the UEs and the edge server in successive stages, the overall performance of split computing can be continuously improved, which is justified through a theoretical convergence performance analysis. Comprehensive simulation studies show that the proposed multitier DRL decision-making scheme outperforms the conventional split computing schemes in terms of the overall latency, inference accuracy, and energy efficiency to practice multitier split computing.

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity.

Splitting the computation loads of a neural network (NN) training task to multiple stations, split computing has been the most promising technology to sustain high-accuracy model for resource-constrained user equipments (UEs) to empower real-time intelligent services. Nevertheless, different communication link variations and computation capabilities in different stations (including UE and servers) render the overall performance optimization in split computing a critical challenge. In this case, different stations should be able to infer the others' communication/computation capabilities to distributively decide the optimum splitting points of an NN. To this end, in this paper, we propose a multi-tier deep reinforcement learning (DRL) scheme for split computing, by which the UE and edge server can collaboratively and adaptively determine their splitting points and computation resources to optimize the long-term overall training latency through tackling different time-scale sub-optimizations in a sequential manner. With the image recognition task as experimental example, comprehensive simulations are conducted to justify the performances in terms of training latency, model accuracy and energy consumption of the proposed scheme for split computing.

Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services. Due to the rapid mobility of LEO satellite, inter-beam/satellite handovers happen frequently for a specific user equipment (UE). To tackle this issue, earth-fixed cell scenarios have been under studied, in which the LEO satellite adjusts its beam direction towards a fixed area within its dwell duration, to maintain stable transmission performance for the UE. Therefore, it is required that the LEO satellite performs real-time resource allocation, which however is unaffordable by the LEO satellite with limited computing capability. To address this issue, in this paper, we propose a two-time-scale collaborative deep reinforcement learning (DRL) scheme for beam management and resource allocation in NTNs, in which LEO satellite and UE with different control cycles update their decision-making policies through a sequential manner. Specifically, UE updates its policy subject to improving the value functions of both the agents. Furthermore, the LEO satellite only makes decisions through finite-step rollouts with a reference decision trajectory received from the UE. Simulation results show that the proposed scheme can effectively balance the throughput performance and computational complexity over traditional greedy-searching schemes.

To sustain ultra-reliable and low latency communication for the fifth generation (5G) networks, the latency of data forwarding over the core network is conventionally ignored. To significantly reduce the latency, a base station shall not permit to service a new session before the case of unacceptable latency taking place. To this end, the fundamental challenge turns out to proactively cognize that the requirements of reliability/latency are about to be violated. To address this challenge, in this article, a deep reinforcement learning based intelligent session management for the open radio access network is proposed to efficiently allocate the resources for the serving sessions and new sessions. The experimental testing results sufficiently show the practicability of our scheme for the 5G networks.

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.

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 objective of supporting next generation driving services, non-terrestrial networks (NTNs) with low earth orbit (LEO) satellites have been regarded as promising paradigms to implement global ubiquitous and high-capacity vehicular connections. However, due to the high moving speed, different satellites can only service a specific set of vehicles for few minutes. In such case, due to the limited computing capability of the satellite, machine learning (ML) based and non-ML based solutions cannot be performed within such a short duration. To address these issues, in this paper, we propose a multi-tier collaborative deep reinforcement learning (DRL) scheme for resource allocation in NTN empowered vehicular networks, in which ground vehicles and LEO satellites maintain DRL-based decision model to obtain resource allocation decisions cooperatively. Specifically, ground vehicles with powerful computing capabilities can assist the satellite to tackle resource allocation optimizations, and the satellite determines final decisions and model parameters by aggregating local calculated results of vehicles. Additionally, the parameters of DRL-based decision model can be transferred from the current satellite to its successor as the starting point for future resource allocation decision-makings. Comprehensive simulations have been conducted to show the effectiveness of our proposed scheme.

Supporting ultra-reliable and low latency communication (URLLC) has been a mandatory function for the International Mobile Telecommunications 2020 (IMT-2020) systems and so as 3GPP New Radio (NR). Conventionally, methods for URLLC primarily focus on performance enhancement on the air interfaces, which ignore a fact that data transmissions through the core network (CN) and the backhaul data network (DN) may invoke considerable latency and such latency may not be addressed solely by a local base station (BS). In this case, before the event of unacceptable latency occur, a BS should not accept the request of a new session creation, so as not to violate the latency and reliability requirements of the existing serving sessions and the new session. For this purpose, the critical challenge lies in how to proactively detect/cognize that the latency/reliability requirement violation event is going to occur, which relies on an effective experience update and process. To tackle this challenge, we particularly note the feature of event prediction in machine learning (ML) methods through experience training, especially the capability of sequential decision making to interact with an unknown environment in reinforcement learning (RL). In this paper, an intelligent session management is therefore proposed. Based on the recent innovation of Open Radio Access Network (O-RAN) to sustain the proposed RL scheme for intelligent session management, an O-RAN based BS is able to effectively configure/admin the resources for each existing serving sessions and the new session. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC in O-RAN, to justify the potential of our approach in the design for 3GPP NR.

Non-Terrestrial Networks (NTNs) composed of space-borne (e.g., satellites) and airborne vehicles (e.g., drones and blimps) have recently been proposed by 3GPP as a new paradigm of infrastructures to enhance the capacity and coverage of existing terrestrial wireless networks. The mobility of non-terrestrial base stations (NT-BSs) however leads to a dynamic environment, which imposes unique challenges for handover and throughput optimization particularly in multi-user access control for NTNs. To achieve performance optimization, each terrestrial user equipment (UE) should autonomously estimate the dynamics of moving NT-BSs, which is different from the existing user access control schemes in terrestrial wireless networks. Consequently, new learning schemes for optimum multi-user access control are desired. In this article, we therefore propose a UE-driven deep reinforcement learning (DRL) based scheme, in which a centralized agent deployed at the backhaul side of NT-BSs is responsible for training the parameter of a deep Q-network (DQN), and each UE independently makes its own access decisions based on the parameter from the trained DQN. With the proposed scheme, each UE is able to access a proper NT-BS intelligently to enhance the long-term system throughput and avoid frequent handovers among NT-BSs. Through comprehensive simulation studies, we justify the performance of the proposed scheme, and show its effectiveness in addressing the fundamental issues in the NTNs deployment.

The next generation vehicular applications substantially shifting the paradigm of human activity have been projected to empower intelligent transportation systems. Targeting at supporting vehicle-to-everything connections, conventional mobile network architectures mandatorily requiring data routing through the core network, however, induce unacceptable costs both in end-to-end latency and backhaul resource consumption. The technical merit of moving computation and storage resources along with mobile vehicles consequently renders the mobile edge computing (MEC) a promising remedy to relieve the burden at the core network. To practice MEC, 3GPP has launched the normative works of a new paradigm known as the local area data network (LADN). Through performing in-network cache to store popular information at LADNs, a vehicle locating within the service area of an LADN is able to access particular location-based wireless application and information. Avoiding data routing through the core network, LADNs, however, encounter two critical challenges in downlink radio access to induce additional latency issues: 1) resource starvation at fronthaul links and 2) discrimination of quality-of-service requirements of vehicles with distinct capabilities. To tackle these challenges, through formulating the Lyapunov function, a stochastic optimization maximizing the utilization of fronthaul resources while stabilizing the queue (and thus latency) of each vehicle is proposed to address the resource starvation. Subsequently, a reinforcement learning-based multiarmed bandit algorithm is further proposed to achieve optimum harmonization of feedback-based and feedbackless transmissions, so as to strikes the tradeoff among energy efficiency, latency, and reliability. The performance evaluation results full demonstrate the effectiveness of the proposed design, to serve urgent needs in the deployment of LADNs.

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.

Unmanned technologies facilitating human activities have been regarded as the most promising innovation to empower fully automatic and intelligent ecosystems. Targeting at extending the processing capabilities of humans, unmanned mobile machines (UMMs) are designated to optimally process the action under varying operating conditions, which relies on prompt information provisioning through existing cellular infrastructures, and renders latency control to information acquisition an inevitable challenge. For this purpose, caching information at network edges has been a remedy for substantial latency reduction, which however ignores practical cell deployment inducing imbalanced wireless services to each UMM in hotspot and rural areas. In this paper, through formulating the Lyapunov function, an algorithm optimizing the utilization of fronthaul resources while stabilizing each UMM's queue is proposed for edge information cache and dissemination in hotspot areas. Furthermore, through formulating the cost measurement as the Cobb-Douglas production function, the optimal beginning time of cache is also derived for UMMs in rural areas. With the provided analytical foundations and simulation studies, the effectiveness of our latency control scheme is fully demonstrated.

Different from the International Mobile Telecommunications Advanced (IMT-Advanced) system solely enhancing the transmission data rates regardless the variety of emerging wireless traffic, the IMT-2020 system supports enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low latency communications (URLCC) to fully capture diverse wireless services in 2020. To satisfactorily gratify the scope of IMT-2020, 3GPP has launched the standardization activity of the fifth generation (5G) New Radio (NR) to deploy the first phase (Release 15) system in 2018 and the ready (Release 16) system in 2020. As eMBB is a legacy system from IMT- Advanced, URLLC jointly demanding low latency and high reliability, and mMTC emphasizes on high reliability may consequently induce significant impacts on the designs of NR air interface. On the advert of the conventional feedback based transmission in LTE/LTE-A designed for eMBB imposing potential inefficiency in the support of URLLC and mMTC, in this paper, we revisit the feedbackless transmission framework, and reveal a tradeoff between these two transmission frameworks. A multi-armed bandit (MAB) based reinforcement learning approach is therefore proposed to achieve the optimum harmonization of feedback and feedbackless transmissions. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC and mMTC, to justify the potential of our approach in the design of 5G NR.

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.

The recent deployment of Cyber-Physical Systems (CPS) has emerged as the most promising approach to provide an extensive computational capability for processing and controlling physical entities, which relies on reliable data exchanges among machines in CPS. However, for CPS exploiting public network infrastructures, links in CPS may suffer from a variety of vulnerabilities to harm real-time data exchanges. Providing network resilience for real-time communications consequently becomes the most critical requirement in CPS. Considering the support of multiple communication paths in state-of-the-art network infrastructures, in this paper, we develop a mathematical resource-optimal network resilience design for CPS. In our design, duplicates of timing sensitive data are simultaneously forwarded via multiple communication paths. Therefore, timing constraints are violated only if all communication paths fail to forward data to the destination on time. By analyzing the relationship among the probability of timing constraint violation, the time domain resource allocation, and the number of communication paths (the spatial domain resource allocation), our design leads to the minimum resource usage to support real-time data exchanges in CPS. Our mathematical resource-optimal design solves the most challenging issue of unreliable data exchanges in CPS in the most efficient fashion, to consequently support the maximum number of machines in CPS.

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.

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.

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.

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.

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.

The IEEE 802.15.3 medium access control (MAC) protocol is an emerging standard for high-rate wireless personal area networks (WPANs), especially for supporting high-quality real-time multimedia applications. Despite defining quality of service (QoS) signalling mechanisms for interoperability between devices, IEEE 802.15.3 does not specify resource allocation algorithms that are left to manufacturers. To guarantee the QoS of real-time variable bit rate (VBR) videos and utilise the radio resource efficiently, the authors propose a dynamic resource allocation algorithm. The proposed bandwidth allocation algorithm is based on a novel traffic predictor. Recently, the variable step-size normalised least mean square (VSSNLMS) algorithm was employed for on-line traffic prediction of VBR videos. However, the performance of the VSSNLMS algorithm significantly degrades due to the abrupt traffic variation occurring at the scene boundary. To tackle this problem, the authors design a novel traffic predictor based on a simple scene detection algorithm and the VSSNLMS algorithm. Analyses using real-life MPEG video traces indicate that the proposed traffic predictor significantly outperforms the VSSNLMS algorithm with respect to the prediction error. The performance of the proposed bandwidth allocation algorithm is also investigated by comparing several existing algorithms. Simulation results demonstrate that the proposed bandwidth allocation algorithm surpasses other mechanisms in terms of channel utilisation, buffer usage and packet loss rate.

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.

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. We also demonstrate how the rate-distance concept can be applied to the rate adaptation of the MAC protocol in the cognitive radio.

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.