Search

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Imbalance and unfairness in resource allocation and utilization can lead to a degraded network environment where wireless network users experience poor quality of service (QoS) within the wireless networks. In multi-user wireless radio networks with constrained wireless network resources, such as power and bandwidth resources, uniform allocation of power or bandwidth may lead to unfairness in network resource management. The cooperative cognitive radio relay networks' (CCRRN) radio resource allocation problem among multi-users is investigated in this paper through combined resource optimization. To facilitate fairness in the multi-user networks, the maxi–min criterion is employed. The max–min fairness ensures that when the wireless network attains max–min utility fairness, no user's utility can be improved without degrading the utility performance of any other user. To maximize the worst-case user (max–min) capacity in the CCRRN using the power and bandwidth allocation strategy, a combined optimal resource allocation (COPBA) scheme is proposed. The max–min resource allocation optimization problem developed is observed to be convex. Efficient computer-based numerical optimization solver packages, such as SNOPT, exist that can solve convex optimization problems efficiently. The MATLAB software provides a robust integrated environment where the implementation of the SNOPT solver can be executed. Using the uniform bandwidth optimal power allocation (UBOPA) scheme as a benchmark, the fairness metric of the COPBA is evaluated and compared. Furthermore, the results showed the COPBA outperforming the UBOPA scheme. [ABSTRACT FROM AUTHOR]

The number of wireless devices, as well as their traffic volumes, is constantly growing, which degrades the quality of service. To combat this problem, the new Wi-Fi 6 (IEEE 802.11ax) standard introduces the orthogonal frequency division multiple access (OFDMA) mechanism, which allows coordinated multiuser frequency division transmissions. In addition, there is another user multiplexing mechanism, non-orthogonal multiple access (NOMA), that allows transmitting on the same frequencies simultaneously and separating different signals by power level. This paper considers the joint usage of these mechanisms for uplink transmissions in Wi-Fi 6 networks and sets the problem of optimal radio resource allocation between the users to maximize some utility functions, for example, the geometric mean throughput. To solve it, we propose an algorithm that takes into account the channel frequency selectivity and uses OFMDA and NOMA simultaneously. It is shown that the joint usage of OFDMA and NOMA can significantly increase the network throughput and reduce delays. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Integrated sensing, communication, and computation (ISCC) will become a key enabler for automation applications. However, since the performance region of ISCC has a higher dimension than those of traditional wireless networks, existing schedulers typically fail to simultaneously meet the heterogeneous requests in ISCC. In this work, we propose a novel wireless scheduling architecture to explore the coordination gains of sensing, communication, and computation from a perspective of joint optimization. Specifically, we first construct an implementation framework of ISCC by combining the mobile edge computing paradigm with the integrated sensing and communication technology, where the inherent tradeoff between sensing, communication, and computation performance is characterized. Next, a joint device association and subchannel assignment problem is formulated to capture the network externalities induced by resource competition among mobile devices with multi-functional requirements. Due to its intractability, we then reformulate it in the matching theoretical manner. To obtain a mutually satisfactory solution under externalities, an iterative matching algorithm is developed by introducing pairwise stability and proved to be convergent and stable. The extensive simulations elucidate the significant superiority of our proposed scheme over those externality-unaware wireless schedulers. [ABSTRACT FROM AUTHOR]

Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely vehicle-to-everything (V2X) and machine-type communication in future cellular networks. D2D communication is envisaged to be the enabler to accomplish the requirements for the applications as mentioned earlier. Due to the scarcity of radio resources, a hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of underutilized radio resources from D2D communication to device-to-infrastructure communication, which are moving in a dynamic environment. The sub-granting assignment problem is cast as a maximization problem of the uplink cell throughput. Firstly, we evaluate the sub-granting signaling overhead due to mobility in a centralized sub-granting resource algorithm, dedicated sub-granting radio resource (DSGRR), and then a distributed heuristics algorithm, open sub-granting radio resource (OSGRR), is proposed and compared with the DSGRR algorithm and no sub-granting case. Simulation results show improved cell throughput for the OSGRR compared with other algorithms. Besides, it is observed that the overhead incurred by the OSGRR is less than the DSGRR while the achieved cell throughput is yet close to the maximum achievable uplink cell throughput. [ABSTRACT FROM AUTHOR]

In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5 G) wireless network. We formally state the problem as a multi-objective optimization one, aiming to minimize the number of updates on the actuators’ input and the power consumption in the downlink transmission. To address the problem, we propose a model-free hierarchical reinforcement learning approach with uniformly ultimate boundedness stability guarantee that learns four policies simultaneously. These policies contain an update time policy on the actuators’ input, a control policy, and energy-efficient sub-carrier and power allocation policies. Our simulation results show that the proposed approach can properly control a simulated ICPS and significantly decrease the number of updates on the actuators’ input as well as the downlink power consumption. [ABSTRACT FROM AUTHOR]

Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely vehicle-to-everything (V2X) and machine-type communication in future cellular networks. D2D communication is envisaged to be the enabler to accomplish the requirements for the applications as mentioned earlier. Due to the scarcity of radio resources, a hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of underutilized radio resources from D2D communication to device-to-infrastructure communication, which are moving in a dynamic environment. The sub-granting assignment problem is cast as a maximization problem of the uplink cell throughput. Firstly, we evaluate the sub-granting signaling overhead due to mobility in a centralized sub-granting resource algorithm, dedicated sub-granting radio resource (DSGRR), and then a distributed heuristics algorithm, open sub-granting radio resource (OSGRR), is proposed and compared with the DSGRR algorithm and no sub-granting case. Simulation results show improved cell throughput for the OSGRR compared with other algorithms. Besides, it is observed that the overhead incurred by the OSGRR is less than the DSGRR while the achieved cell throughput is yet close to the maximum achievable uplink cell throughput. [ABSTRACT FROM AUTHOR]

It is becoming increasingly clear that an important task for wireless networks is to minimize the age of information (AoI), i.e., the timeliness of information delivery. While mainstream approaches generally rely on the real-time observation of user AoI and channel state, there has been little attention to solve the problem in a complete (or partial) absence of such knowledge. In this article, we present a novel study to address the optimal blind radio resource scheduling problem in orthogonal frequency division multiplexing access (OFDMA) systems towards minimizing long-term average AoI, which is proven to be the composition of time-domain-fair clustered round-robin and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic solutions that are near-optimal as shown by simulation results are also proposed to effectively improve the performance upon presence of various degrees of extra knowledge, e.g., channel state and AoI. [ABSTRACT FROM AUTHOR]

Given a set I of symbols, a set J of positions of an array, a gain value ρij for allocating i∈I to j∈J, the connected assignment problem in arrays (CAPA) is the problem consisting in finding an assignment of one symbol i to each position j so as to maximize the sum of the gains under the constraint such that repeated symbols are adjacent in the array. CAPA has a direct application in radio resource allocation (RRA) in the uplink of the modern fourth‐generation (4G) wireless communication networks. We show that CAPA is NP‐hard. We present two integer linear programming formulations for CAPA and propose four heuristic solutions. They are compared using Monte Carlo simulations to generate instances compatible with a 4G system scenario as well as random instances based on a uniform distribution of the gains. [ABSTRACT FROM AUTHOR]

A channel and latency aware radio resource allocation algorithm based on deep reinforcement learning (DRL) is proposed and evaluated. The proposed scheme aims to optimize the uplink scheduling for service-oriented multi-user millimeter wave (mmWave) radio access networks (RAN) in the 5G era. In the DRL system, multiple application flows are implemented with various statistical models and the key function modules of the system are designed to reflect the operation and requirements of service-oriented RANs. In particular, the mmWave channel characteristics utilized in the system are collected experimentally and verified via a radio-over-fiber (RoF)-mmWave testbed with dynamic channel variations. Results show that the proposed DRL algorithm can operate adaptively to channel variations and achieve at least 12% average reward improvement compared to conventional single-rule schemes, providing joint improvement of bit error rate and latency performance. [ABSTRACT FROM AUTHOR]

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ultra-reliable and low-latency communication (URLLC) in mobile networks is still one of the core solutions that require thorough research in 5G and beyond. With the vigorous development of various emerging URLLC technologies, resource shortages will soon occur even in mmWave cells with rich spectrum resources. As a result of the large radio resource space of mmWave, traditional real-time resource scheduling decisions can cause serious delays. Consequently, we investigate a delay minimization problem with the spectrum and power constraints in the mmWave hybrid access network. To reduce the delay caused by high load and radio resource shortage, a hybrid spectrum and power resource allocation scheme based on reinforcement learning (RL) is proposed. We compress the state space and the action space by temporarily dumping and decomposing the action. The multipath deep neural network and policy gradient method are used, respectively, as the approximater and update method of the parameterized policy. The experimental results reveal that the RL-based hybrid spectrum and the power resource allocation scheme eventually converged after a limited number of iterative learnings. Compared with other schemes, the RL-based scheme can effectively guarantee the URLLC delay constraint when the load does not exceed 130%. [ABSTRACT FROM AUTHOR]

We envisage each person will wear an individual wireless body area network (WBAN) for healthcare monitoring. Two WBANs in proximity may collide. Such WBAN collisions can significantly degrade communication quality of service (QoS), in terms of throughput and latency of healthcare applications. To avoid collisions, we propose to use cellular-assisted device-to-device (D2D) communications to connect on-body sensors to a hub within WBAN. These D2D transmissions are performed concurrently with cellular transmissions and thus, interference is imposed on each other. A radio resource allocation scheme has been developed to manage the interference. The scheme uses a combination of difference of convex optimization and Hungarian algorithm to control transmit power and to assign communication channel to both cellular and D2D transmissions. The objective is to maximize the sum of cellular throughput while avoiding collisions among co-located WBANs and satisfying QoS requirements of healthcare applications. Evaluation results confirm that WBAN collisions can be avoided at a crowded pedestrian walkway, even when there are as many as 6 pedestrian arrivals per second. [ABSTRACT FROM AUTHOR]

The rich mobile data and edge computing enabled wireless networks motivate to deploy artificial intelligence (AI) at network edge, known as edge AI, which integrates wireless communication and machine learning. In communication, data bits are equally important, while in machine learning some data bits are more important. Therefore we can allocate more radio resources to the more important data and allocate less radio resources to the less important data, so as to efficiently utilize the limited radio resources. To this end, how to define “more or less important” of data is the key problem. In this article, we propose two importance criteria to differentiate data’s importance based on their effects on machine learning, one for centralized edge machine learning and the other for distributed edge machine learning. Then, the corresponding radio resource allocation schemes are proposed to improve performance of machine learning. Extensive experiments are conducted for verifying the effectiveness of the proposed data-importance aware radio resource allocation schemes. [ABSTRACT FROM AUTHOR]

Community smart grid is formed by a group of neighboring households to share renewable generators and energy storage facilities. Within a community grid, all power grid entities are connected to power lines that are downstream to a common substation. In this paper, we use device-to-device communications to connect sensors that are installed at grid entities to their respective control units. The topology and node membership of such communication network depend on the topology of its power grid. As such, a communication node may not be assigned the best quality channel. This has imposed additional constraints in radio resource allocation. We exploit the feature that sensors and control units are static but cellular users are mobile, in proposing a two-stage radio resource allocation scheme. In the planning stage, we use the Hungarian method to find the optimal channel assignment for static sensors and control units. In the operation stage, we run recursively the Hungarian algorithm to allocate proportionally all remaining channels to cellular users. Given the channel assignment, the operation stage further performs transmit power allocation to all nodes. The power control problem is originally nonconvex. We transform the problem into a difference of convex structure, which can be solved successively. We have evaluated the proposed radio resource allocation scheme through extensive simulations. Results confirm that the scheme is indeed efficient in assigning channels and finding the minimum transmit power, to maximize sum-rate of cellular users while guaranteeing a minimum throughput to each sensor and control unit. [ABSTRACT FROM AUTHOR]

Integration of renewable energy into power distribution network is a challenge because renewable output is intermittent. This problem can be tackled through demand response management which controls consumption demand by adjusting energy prices in real-time to match the available time-varying power supply. Such real-time pricing (RTP) requires an advanced communication network to collect data and to disseminate price information. We use cellular-assisted underlay device-to-device (D2D) communications to connect energy management units (EMUs) directly to the RTP control center. Such D2D transmissions can be interfered by concurrent cellular transmissions. This interference can affect reliability of the RTP operation. We quantify the reliability requirements given a tolerable financial loss and a supply-demand gap. The cost in satisfying the reliability requirement appears in the form of a lower sum-rate for all cellular nodes, because cellular rate is a revenue generator for communication service provider. We formulate an optimization problem to allocate radio resources in terms of communication channel and transmit power, to maximize the cellular sum-rate without compromising the RTP reliability requirements. Since the optimization problem is non-convex, we use a pair of iterative algorithms to assign communication channel using the Hungarian algorithm and to control transmit power using the difference of convex (DC) optimization. The effectiveness of the combination of the Hungarian algorithm and the DC optimization has been verified through extensive simulations, with the results compared against a benchmark scheme that randomly assigns communication channels. [ABSTRACT FROM AUTHOR]

In this paper, we present an analytical framework for the performance evaluation of Radio Resource Allocation (RRA) in Orthogonal Frequency Division Multiple Access (OFDMA) networks. We focus on Quality of Service (QoS) guaranteed traffic whose capacity, in terms of the number of active user connections, depends on the users' QoS requirements and channel conditions, and the RRA algorithm. The required QoS is guaranteed by restricting the number of admitted calls, which in turn requires an accurate estimate of the QoS metrics and capacity supported by the RRA when a new call arrives. These estimates for OFDMA networks are variable and usually obtained through time-consuming offline computer simulations. Mathematical frameworks on the other hand yield timely and accurate results. However, earlier known works on analytical modelling of RRA either consider a single channel with random traffic arrivals or multiple channels with full buffer data traffic. In contrast, we develop a queueing theoretic framework considering randomly arriving QoS-guaranteed traffic in a variable-rate multi-channel multi-class OFDMA network. The framework can be used online leading to better dynamic Call Admission Control. We characterize the RRA algorithm using a scheduler control parameter which can regulate the call blocking probability while providing predefined QoS constraints. We model the RRA as a variable service rate, multi-server, multi-class, finite buffer queueing system and verify the derived QoS metrics using extensive Monte-Carlo discrete event simulations. [ABSTRACT FROM AUTHOR]

In device‐to‐device (D2D) communication underlaying cellular networks, the D2D user equipments (DUEs) can communicate by sharing the radio resources assigned to cellular user equipments. D2D communication is expected to improve the network throughput and save more energy for the user equipments. However, D2D communication causes severe interference to both the cellular users and other DUEs which is a challenge to radio resource allocation. The authors propose joint channel and power allocation algorithm to maximise the network achievable throughput, control the interference from the DUEs transmissions to cellular users and improve the energy efficiency of the user equipments. The authors formulate the problem and solve it using convex optimisation methods to select DUEs and set their powers in each channel. Simulation results show that the proposed algorithm can offer near‐optimal performance and outperforms the comparable algorithms especially in terms of achievable throughput even with markedly reduced complexity levels. [ABSTRACT FROM AUTHOR]

In smart grid, an advanced metering infrastructure (AMI) facilitates two-way communications between smart meters (SMs) and a meter data management unit (MDMU). We use cellular-assisted device-to-device (D2D) communications to connect a large number of SMs to the MDMU via cluster heads (CHs). Improper clustering can affect spectral efficiency. In addition, each SM and CH must be provided with a throughput guarantee for reliable smart grid operations. In this paper, we formulate a joint D2D cluster formation and radio resource allocation problem to maximize spectral efficiency in terms of uplink sum-rate, subject to constraints of guaranteeing a minimum throughput to each AMI entity. The optimization problem is a mixed-integer non-linear program, which is non-deterministic polynomial-time hard. For practical implementation and real-time decision making, we divide the original problem into four sub-problems: cluster formation, initial power allocation, channel allocation, and power allocation improvement, respectively. In the sub-problem for power allocation improvement, we exploit the properties of the difference of convex functions in finding the optimal transmission power. Simulation results confirm superiority of the proposed algorithm compared with other existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cache-aided non-orthogonal multiple access (NOMA) scheme for spectrally efficient downlink transmission. The proposed scheme not only reaps the benefits associated with NOMA and caching, but also exploits the data cached at the users for interference cancelation. As a consequence, caching can help to reduce the residual interference power, making multiple decoding orders at the users feasible. The resulting flexibility in decoding can be exploited for improved NOMA detection. We characterize the achievable rate region of cache-aided NOMA and derive the Pareto optimal rate tuples forming the boundary of the rate region. Moreover, we optimize cache-aided NOMA for minimization of the time required for completing file delivery. The optimal decoding order and the optimal transmit power and rate allocation are derived as functions of the cache status, the file sizes, and the channel conditions. Simulation results confirm that, compared to several baseline schemes, the proposed cache-aided NOMA scheme significantly expands the achievable rate region and increases the sum rate for downlink transmission, which translates into substantially reduced file delivery times. [ABSTRACT FROM AUTHOR]

Toward 5G cellular network, to achieve an extremely high data rate and Ultra Reliable Low Latency Communication by using a limited radio frequency spectrum bands becomes a big challenge. 3GPP LTE-A/LTE-A Pro thus specifies the technologies of Carrier Aggregation (CA) and Network Function Virtualization (NFV) to increase the frequency spectrum efficiency and to dynamically allocate the virtualized network component for different classes of requests, respectively. CA can aggregate multiple contiguous or non-contiguous Component Carriers (CCs) and to improve frequency spectrum utilization and signal quality. NFV can dynamically allocate network (virtualized) resource for different classes of services, e.g., human-driven Cloud Computing, machine-driven Internet of Vehicles (IoVs) (e.g., Autonomous Self Driving Vehicle) and sensing-based Internet of Things (IoTs), etc. However, different SINRs of different frequency spectrum bands suffer from the exiting radio nature of CCs. The CA effect and system capacity are thus limited obviously. Additionally, in CC accesses via IoV/IoT, human-driven and machine-driven types communications exhibit different arrival requests and traffic characteristics. Various QoS requirements and traffic distributions of Human and Machine transmissions are certainly different. This paper thus proposes the Cross-Layer scheduling with CC Aggregation (CLCA) toward 5G. CLCA consists of three mechanisms: (1) Markov Decision Process-based cost reward Packet Selection (MDP-PS), (2) Adaptive Packet Scheduling (APS) and (3) Adaptive Component Carrier scheduling (ACC). Numerical results demonstrate that the proposed CLCA approach outperform the compared approaches in system capacity, network reward and packet failure rate. [ABSTRACT FROM AUTHOR]

Network slicing in fog radio access networks is recognized as a cost-efficient solution to support future diverse use cases. However, with the number of user equipments (UE) fast increasing, the centralized resource allocation architecture for network slicing can put heavy burdens on the global radio resource manager (GRRM), and meanwhile slice customization is not easy to achieve. To overcome the two issues, a hierarchical radio resource allocation architecture is proposed in this paper, where the GRRM is responsible for allocating subchannels to local radio resource managers (LRRMs) in slices, which then allocate the assigned resources to their UEs. Under this architecture, a hierarchical resource allocation problem is formulated, and the problem is further modeled as a Stackelberg game with the GRRM as the leader and LRRMs as followers, considering the hierarchy between the GRRM and LRRMs. Due to the NP-hardness of the followers’ problems, a process based on exhaustive search is first proposed to achieve the Stackelberg equilibrium (SE). Nevertheless, when the network scale is large, achieving SE within limited decision making time is impractical for game players. Facing this challenge, the GRRM and LRRMs are seen as bounded rational players, and low complexity algorithms are developed to help them achieve local optimal solutions that lead to a weak version of SE. Simulation results show that there exists a tradeoff between the performance of slices, and the low complexity algorithms achieve close performance to that of exhaustive search and outperform other baselines significantly. [ABSTRACT FROM AUTHOR]

It is expected that the emerging 5G networks will not only support diverse use cases, but also enable unprecedented applications such as haptic communications. Therefore, network slicing will provide the required design flexibility. Radio resource slicing would be an indispensable component of any network slicing solution. This paper proposes $\mathsf{Hap-SliceR}$ , which is a novel radio resource slicing framework for 5G networks with haptic communications. First, $\mathsf{Hap-SliceR}$ derives a network-wide radio resource slicing strategy for 5G networks. The optimal slicing strategy, which is based on a reinforcement learning approach, allocates radio resources to different slices while accounting for the dynamics and utility requirements of different slices. Second, $\mathsf{Hap-SliceR}$ provides customization of radio resources for haptic communications over 5G networks. The radio resource allocation requirements of haptic communications have been translated into a unique radio resource allocation problem. A low-complexity heuristic algorithm has been developed for resource allocation. Finally, a comprehensive performance evaluation of $\mathsf{Hap-SliceR}$ has been conducted based on a recently proposed 5G air-interface design. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communications enable user equipments (UEs) in proximity to each other to exchange information by taking advantage of high data-rate and low energy consumption. When D2D transmissions share the radio resources with the cellular UEs, efficient admission control (AC) and radio resource allocation (RRA) strategies play a key-role in controlling the co-channel interference and allowing quality of service (QoS) provision to UEs. This paper proposes a novel joint AC and RRA strategy that provides long-term QoS support to cellular and D2D communications. The AC algorithm derives the best set of cellular and D2D links by maximizing the revenues of the service provider under QoS constraints. The RRA algorithm assigns the available channels and transmits powers to admitted users on the short-term, in order to maximize an average weighted sum-rate under the same QoS constraints of the AC. Due to the NP-hard nature of the optimization problem, we propose an AC greedy algorithm that achieves near-optimal results for reasonable numbers of D2D links. Then, we propose a low-complexity RRA algorithm that decouples channel and power allocation. Numerical results show that the proposed joint AC and RRA strategy outperforms existing frameworks by increasing up to 40% the number of satisfied cellular and D2D links and by reducing energy consumption by more than 50%. [ABSTRACT FROM AUTHOR]

The exchange of haptic information in remote operation systems allows humans to physically interact in remote environments with complete immersion. Such human-in-the-loop networks have been realized through wired solutions in the past. It is expected that the emerging 5G mobile communications networks will be able to meet the stringent requirements of real-time haptic interaction. By enabling haptic communications over 5G networks, human-in-the-loop mobile networks will emerge, that will support unprecedented applications. The main objective of this paper is to investigate the problem of radio resource allocation for haptic communications over 5G networks. The key requirements of haptic communications from a radio resource allocation perspective have been identified. These requirements have been translated into two distinct optimization problems for radio resource allocation, pertaining to perceptual coding and symmetric design cases. To reduce complexity, a two-step solution has been provided after decomposing the original optimization problems. In both cases, low-complexity greedy heuristic algorithms and near-optimal solutions have been proposed for resource allocation. Performance evaluation has been conducted based on a 5G air-interface design. [ABSTRACT FROM AUTHOR]

In order to capture and maintain a representative share of the wireless communication market, effective ways to manage the scarce physical resources of cellular networks are fundamental for cellular network operators. In this context, this paper proposes an adaptive Radio Resource Allocation algorithm that targets the user satisfaction maximization in cellular networks with multiple services. The proposed algorithm is mathematically derived from a utility-based cross-layer optimization framework and employs user weights as well as an innovative service weight that is adapted to meet the satisfaction target of the most prioritized service. Furthermore, the proposed algorithm is scalable to several services classes and can be employed in the current and future generations of wireless systems that guarantee orthogonality among the allocable resources. The performance evaluation is conducted in realistic scenarios of the downlink of an Orthogonal Frequency Division Multiple Access based cellular network serving video and Constant Bit Rate flows, where we assume imperfect Channel State Information at the transmitter. Significant gains in the joint system capacity were obtained, demonstrating that the adaptability and service prioritization allow the accomplishment of simultaneously maximizing the user satisfaction for distinct services. [ABSTRACT FROM AUTHOR]

Novel networking paradigms, such as software‐defined networking (SDN) and network function virtualisation (NFV), introduce new opportunities in the design of next‐generation mobile networks. The present work investigates the benefits of the emerging SDN and NFV technologies on the radio resource management (RRM) in mobile cellular networks. In particular, the aim of the proposed RRM scheme is to enable an efficient and flexible radio resource allocation in order to assure quality of experience of mobile users. The authors consider the orthogonal frequency division multiple access scheme and the complete radio resource sharing policy. To enable time‐ and space‐efficient resource allocation, the authors investigate the applicability of the well‐known Kaufman–Roberts recursion in the context of new architectural and functional changes of SDN/NFV based mobile environments. Finally, they discuss the applicability of the proposed approach for more complicated resource sharing policies. [ABSTRACT FROM AUTHOR]

This paper investigates the energy-efficient radio resource allocation problem of the uplink smallcell networks. Different from the existing literatures which focus on improving the energy efficiency (EE) or providing fairness measured by data rates, this paper aims to provide fairness guarantee in terms of EE and achieve EE-based proportional fairness among all users in smallcell networks. Specifically, EE-based global proportional fairness utility optimization problem is formulated, taking into account each user’s quality of service, and the cross-tier interference limitation to ensure the macrocell transmission. Instead of dealing with the problem in forms of sum of logarithms directly, the problem is transformed into a form of sum of ratios firstly. Then, a two-step scheme which solves the subchannel and power allocation separately is adopted, and the corresponding subchannel allocation algorithm and power allocation algorithm are devised, respectively. The subchannel allocation algorithm is heuristic, but can achieve close-to-optimal performance with much lower complexity. The power allocation scheme is optimal, and is derived based on a novel method which can solve the sum of ratios problems efficiently. Numerical results verify the effectiveness of the proposed algorithms, especially the capability of EE fairness provisioning. Specifically, it is suggested that the proposed algorithms can improve the fairness level among smallcell users by 150-400 % compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

Energy efficiency and quality of service (QoS) guarantees are the key design goals for the 5G wireless communication systems. In this context, we discuss a multiuser scheduling scheme over fading channels for loss tolerant applications. The loss tolerance of the application is characterized in terms of different parameters that contribute to quality of experience (QoE) for the application. The mobile users are scheduled opportunistically such that a minimum QoS is guaranteed. We propose an opportunistic scheduling scheme and address the cross-layer design framework when channel state information (CSI) is not perfectly available at the transmitter and the receiver. We characterize the system energy as a function of different QoS and channel state estimation error parameters. The optimization problem is formulated using Markov chain framework and solved using stochastic optimization techniques. The results demonstrate that the parameters characterizing the packet loss are tightly coupled and relaxation of one parameter does not benefit the system much if the other constraints are tight. We evaluate the energy-performance tradeoff numerically and show the effect of channel uncertainty on the packet scheduler design. [ABSTRACT FROM PUBLISHER]

Personalized content retrieval service has become a major information service that consumes a large portion of mobile Internet traffic. Joint content recommendation and delivery is a promising design philosophy that could effectively improve the overall user experience with personalized content retrieval services. Existing research mostly focused on a push-type design paradigm called proactive caching, which, however, has multiple inherent drawbacks such as high device cost and low energy efficiency. This paper proposes a novel, interactive joint content recommendation and delivery system as an alternative to overcome the drawbacks of proactive caching systems. We present several optimal and heuristic algorithms for the proposed system and analyze the system performance in terms of user interest and transmission outage probability. Some theoretical performance bounds of the system are also derived. The effectiveness of the proposed system and algorithms is validated by simulation results. [ABSTRACT FROM AUTHOR]

In the full-duplex wireless cellular system, co-channel interference may limit the achieved cell throughput, and proper radio resource allocation can help to reduce the co-channel interference. In this paper, joint resource allocation on user pairs and resource blocks (RBs) in full-duplex OFDMA cellular system is investigated for benefiting system throughput or user fairness. Firstly, a binary-integer programming optimization problem for maximizing cell throughput is formulated and the optimal solutions are given by exhaustive search. Then, two suboptimal heuristic algorithms are proposed to ease the implementation in practical wireless cellular system. The effectiveness of the proposed algorithms are verified by system level simulations in a full-duplex single cellular system. [ABSTRACT FROM AUTHOR]

As a promising paradigm for the 5G wireless communication system, a new evolution of the cloud radio access networks has been proposed, named as fog radio access networks (F-RANs). It is an advanced socially aware mobile networking architecture to provide a high spectral and energy efficiency while reducing backhaul burden. In particular, F-RANs take full advantages of social information and edge computing to efficiently alleviate the end-to-end latency. Based on the benefit of edge and cloud processing, key issues of F-RAN technique are radio resource allocation, caching, and service admission control. In this paper, we develop a novel F-RAN system control scheme based on the embedded game model. In the proposed scheme, spectrum allocation, cache placement, and service admission algorithms are jointly designed to maximize system efficiency. By developing a new embedded game methodology, our approach can capture the dynamics of F-RAN system and effectively compromises the centralized optimality with decentralized distribution intelligence for the faster and less complex decision making process. Through simulations, we compare the performance of our scheme to the existing studies and show how we can achieve a better performance under dynamic F-RAN system environments. [ABSTRACT FROM AUTHOR]

This paper presents resource allocation algorithms for secondary user (SU) transmitters of single-carrier frequency division multiple access (SC-FDMA)-based cognitive radio systems. In addition to the conventional per-SU transmit power constraints, we also consider two types of interference constraints on SUs to protect the primary users links and thereby formulate two resource allocation problems. The first constraint relates to individual per-subcarrier interference power, whereas the second constraint corresponds to joint sum of interference power across all the subcarriers and SUs. In SC-FDMA system, a subcarrier can be allocated to one SU at a time (exclusivity constraint), and the multiple subcarriers allocated to an SU must be adjacent to each other (adjacency constraint). These constraints make the resource allocation problem prohibitively difficult to solve. We derive efficient power and subcarrier allocation algorithms via convex optimization techniques for both problems. We compare the results of our proposed algorithm with the existing schemes. Numerical results show that our algorithms can improve the system performance significantly. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multicarrier low-density-spreading multiple access (MC-LDSMA) is a promising multiple access technique that enables near-optimal multiuser detection. In MC-LDSMA, each user's symbol is spread over a small set of subcarriers, and each subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes the radio resource allocation more challenging compared with some well-known multiple access techniques. In this paper, we study the radio resource allocation for a single-cell MC-LDSMA system. First, we consider the single-user case and derive the optimal power allocation and subcarriers partitioning schemes. Then, by capitalizing on the optimal power allocation of the Gaussian multiple-access channel, we provide an optimal solution for MC-LDSMA that maximizes the users' weighted sum rate under relaxed constraints. Due to the prohibitive complexity of the optimal solution, suboptimal algorithms are proposed based on the guidelines inferred by the optimal solution. The performance of the proposed algorithms and the effect of subcarrier loading and spreading are evaluated through Monte Carlo simulations. Numerical results show that the proposed algorithms significantly outperform conventional static resource allocation, and MC-LDSMA can improve the system performance in terms of spectral efficiency and fairness in comparison with orthogonal frequency-division multiple access. [ABSTRACT FROM PUBLISHER]

Due to the high density of vehicles and various types of vehicular services, it is challenging to guarantee the quality of vehicular services in current Long-Term Evolution (LTE) networks in a cost-efficient manner. Fortunately, with the development of fifth-generation (5G) technology, the installation of a large number of small cells is foreseen as one of the practical ways to achieve the low-delay requirement in vehicular environments. However, it may cause a huge operating expense and capital expenditure to mobile network operators due to the limited backhaul capacity and the explosion of signaling. In this paper, we integrate software-defined networking and radio resource virtualization into an LTE system for vehicular networks, i.e., software-defined heterogeneous vehicular network (SERVICE) . Based on this proposed system framework, a delay-optimal virtualized radio resource scheduling scheme is proposed via stochastic learning. The delay optimal problem is formulated as an infinite-horizon average-cost partially observed Markov decision process (POMDP). Then, an equivalent Bellman equation is derived to solve it. The proposed scheme can be divided into two stages, i.e., macro virtualization resource allocation (MaVRA) and micro virtualization resource allocation (MiVRA). The former is executed based on large timescale variables (traffic density), whereas the latter is operated according to short timescale variables (channel state and queue state). Simulation results show that the proposed scheme outperforms traditional schemes. [ABSTRACT FROM PUBLISHER]

The existing works on resource allocation for OFDMA based cognitive radio networks are based on the assumption of Gaussian inputs whereas in practical systems the inputs are taken from a set of finite symbol alphabets. This paper considers a system with arbitrarily distributed finite power inputs and solve the resource allocation problem by employing the relationship between mutual information and minimum mean-square error. To protect the primary users' links, constraint on interference power of the secondary users (SUs) is imposed. In OFDMA based CR networks, a tone can be assigned to one SU at most (exclusivity constraint), due to which the resource allocation problem becomes combinatorial and its solution becomes prohibitively difficult.In this paper, first, the exclusivity constraint on tones allocation is relaxed, the problem is convexified and an optimal solution is derived that provides an upper bound on the system performance. Then, an integer tone allocation and optimal power allocation (ITA-OPA) algorithm is developed that guarantees the assignment of each tone to a single SU with close-to-optimal performance. Finally, keeping in view the complexity of the optimal solution and ITA-OPA algorithm, a low-complexity suboptimal algorithm is devised that accounts for exclusive tone assignment. Simulation results show that the suboptimal algorithm also achieves near-optimal performance. The proposed algorithms outperforms the algorithms that assume Gaussian inputs. [ABSTRACT FROM AUTHOR]

To enable machine type communications (MTCs), MTC gateway (MTCG) is deployed in LTE-A cellular networks. An MTCG can collect data from a group of MTC devices (MTCDs) and forward the data to the base station (BS) via cellular uplinks. This letter proposes a nonorthogonal radio resource allocation (RRA) scheme for MTC-enabled cellular networks, in which an MTCG is configured as a full duplex (FD) relay to assist the uplink transmissions of a group of MTCDs. Through coordinating the transmit powers at the MTCG and MTCDs, the aim of the proposed scheme is to minimize the aggregate energy consumption of the MTCDs while satisfying their delay constraints. The simulation results show that most of the channel reuse gain of FD relaying can be achieved if the self-interference at the MTCG is controlled below a certain level. [ABSTRACT FROM PUBLISHER]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the existed works on the radio resource allocation (RRA) problem commonly assume the channel-state information (CSI) can be perfectly obtained by the transmission source. However, such assumption is not practical in the realistic wireless systems. In this work, we consider the practical implementation issues of resource allocation in orthogonal frequency division multiple access (OFDMA) two-way relay networks: the inaccuracy of channel-state information (CSI) available to the source. Instead, only the estimated channel status is known by the source. In this context, a joint optimization of subcarrier pairing and allocation, relay selection, and transmit power allocation is formulated in OFDMA two-way amplify-and-forward relay networks. Moreover, the objective of this work is to minimize the energy consumption of the overall system. Further, to ensure the quality of service (QoS) or data rate requirement, the energy consumption must be minimized without compromising the QoS. Therefore, by applying convex optimization techniques, energy-efficient algorithms are developed with the objective to minimize the total transmit power with guaranteeing the required data rates. Through simulation studies, energy consumption performance of the systems under the proposed schemes is investigated. It can be observed that our proposed scheme can improve the energy consumption performance of the considered system. [ABSTRACT FROM AUTHOR]

In this paper, we study radio resource allocation for multicasting in OFDMA based high altitude platforms (HAPs). We formulate and solve an optimization problem that finds the best allocation of HAP resources such as radio power, sub-channels, and time slots. The problem also finds the best possible frequency reuse across the cells that constitute the service area of the HAP. The objective is to maximize the number of user terminals that receive the requested multicast streams in the HAP service area in a given OFDMA frame. A bounding subroutine in a branch and bound algorithm can be obtained by decomposing it into two easier subproblems, due to its high complexity, and solving them iteratively. Subproblem 1 turns out to be a binary integer linear program of no explicitly noticeable structure and therefore Lagrangian relaxation is used to dualize some constraints to get a structure that is easy to solve. Subproblem 2 turns out to be a linear program with a continuous knapsack problem structure. Hence a greedy algorithm is proposed to solve subproblem 2 to optimality. The subgradient method is used to solve for the dual variables in the dual problem to get the tightest bounds. [ABSTRACT FROM PUBLISHER]

Quality of service (QoS) for a network is characterized in terms of various parameters specifying packet delay and loss tolerance requirements for the application. The unpredictable nature of the wireless channel demands for application of certain mechanisms to meet the QoS requirements. Traditionally, medium access control (MAC) and network layers perform these tasks. However, these mechanisms do not take (fading) channel conditions into account. In this paper, we investigate the problem using cross layer techniques where information flow and joint optimization of higher and physical layer is permitted. We propose a scheduling scheme to optimize the energy consumption of a multiuser multi-access system such that QoS constraints in terms of packet loss are fulfilled while the system is able to maximize the advantages emerging from multiuser diversity. Specifically, this work focuses on modeling and analyzing the effects of packet buffering capabilities of the transmitter on the system energy for a packet loss tolerant application. We discuss low complexity schemes which show comparable performance to the proposed scheme. The numerical evaluation reveals useful insights about the coupling effects of different QoS parameters on the system energy consumption and validates our analytical results. [ABSTRACT FROM PUBLISHER]

Orthogonal Frequency Division Multiple Access based 4G cellular systems are designed to support different types of traffic through packet switching only. Hence, packet schedulers are important in increasing Real-Time (RT) traffic capacity as well as non Real-Time (nRT) data throughput of these networks without violating control channel and Quality of Service (QoS) constraints. Increasing capacity and maintaining QoS being conflicting design objectives, a desired Mixed Traffic Scheduler (MTS) algorithm is expected to yield a suitable trade-off between them. In order to identify such an algorithm for any ratio of RT and nRT users in a cell, in this paper, we compare the performance of certain MTS algorithms in terms of number of supportable users, data throughput, control channel usage, and resource usage. We propose two MTS algorithms which treat voice and video as a single traffic class. These are: (1) dynamic packet scheduling and (2) a new limited Joint Time Frequency (JTF) scheduling. These algorithms are compared with known ones like Modified Largest Weighted Delay First (Andrews et al. in IEEE Commun Mag 39(2):150-154, ) and SP-Dynamic (SemiPersistent for VoIP and Dynamic for Video) (Muntean and Otesteanu ). Impact of bundling voice and video packets on the performance of the algorithms is also investigated. Extensive system level simulations show that in addition to 14 video users per MHz, the proposed JTF scheduling with bundled VoIP and video packets can support 57.5 % more VoIP users per MHZ than the 3GPP requirement for only VoIP traffic (ITU-R ). [ABSTRACT FROM AUTHOR]

In future virtual network (VNet) environments, where heterogeneous networks will coexist and new business roles and models are expected, VNets cooperation for radio resource management is one of the key issues in order to achieve an efficient integration of different wireless technologies, while providing the required quality of service. In this paper, an algorithm for Virtual Radio Resource Allocation (VRRA), called OnDemandVRRA, in multiple and heterogeneous virtual networks environment is proposed and discussed. OnDemandVRRA manages the radio resources allocation to the VNets in order to satisfy the VNet contracted capacity. The algorithm has been assessed for two different Virtual scenarios by comparison with a Standard one, in which several network operators have their own networks, each being shared among several mobile virtual network operators. It is shown that the application of OnDemandVRRA to virtualise the wireless access allows achieving better performance from a cluster of several base stations of different radio access technologies, supporting the provision of the contracted capacity for guaranteed VNets, i.e., VNets with a minimum contracted capacity. Globally, it is observed that Virtual scenarios perform better than the Standard one, as the cluster serving data rate may increase by approximately 46 %, if minimum and reference values for the VNet's contracted capacity have been set. Moreover, in this case, end-users performing best-effort services are served with an average data rate approximately three times higher than in other scenarios, where the reference data rate for the VNet providing these services is not set. [ABSTRACT FROM AUTHOR]

In parallel with the amazing increase in mobile data traffic, the fast-growing requirement and development of the green communication technology have led to many energy-saving designs in mobile networks. Meanwhile, as advanced cellular technologies progress, more than one component carrier (CC) can now be jointly utilized in a base station (BS). As a result, the energy consumption of the BS has become an important concern. In this paper, a novel green rate-and-power control transmission scheme is therefore proposed for the BS transmission to address the problem of energy minimization at BS transceivers subject to certain quality-of-service and fairness requirements for all users. Communication activities in downlink transmissions of the BS with orthogonal frequency-division multiple access-based multi-CCs are considered. Simulation results demonstrate that the energy consumption of the proposed novel energy-saving scheme is much better than that of existing schemes, when all schemes satisfy the same constraints. [ABSTRACT FROM PUBLISHER]

This paper considers radio resource allocation (RRA) in the downlink of an orthogonal frequency division multiple access (OFDM)-based spectrum-sharing network. The objective of RRA is to maximize the average achievable throughput subject to the primary service interference threshold and the secondary service transmit power constraint. RRA is usually implemented based on a time window $T$ over which system parameters are averaged and checked against resource constraints. We use short-term ( $T=1$ time slot) and long-term ( $T\gg 1$ slots) averaging as approximations to instantaneous and average constraints, respectively. RRA is also investigated for this system with long-term interference threshold and short-term interference threshold constraints. RRA optimization is a nonconvex optimization problem in which the duality principle is adopted to obtain approximate solutions. The duality gap indicates the degree of approximation in the thus-obtained solution. We prove that the duality gap corresponding to each resource allocation asymptotically decays at least with an exponential rate of $T$. We further show that OFDMA is, asymptotically, the optimal subcarrier assignment. We also propose a practically implementable online power and subcarrier allocation with on-the-fly channel state information measurement. An extensive simulation study has been conducted to verify the theoretically predicted duality gap behavior and to investigate the impact of different system parameters on the secondary service performance. The developed algorithms are also validated to be robust in practical settings and converge fast to theoretical bounds, and thus practically implementable. [ABSTRACT FROM PUBLISHER]