Joint Bandwidth Allocation Research Articles

Joint bandwidth allocation and task offloading in multi-access edge computing

In recent years, multi-access edge computing (MEC) has become a hot topic. With its distributed characteristics, MEC provides more possibilities for delay-sensitive tasks. In this paper, we study a task offloading problem to shorten task delay. The problem consists of two aspects, bandwidth allocation and task offloading decision-making. Based on alliance game, we formulate bandwidth allocation to minimize the dissatisfaction of alliances. Game participants are all users. We take into account the dissatisfaction of each alliance and find the least dissatisfaction of the alliance. Then, we formulate the task offloading decision-making to minimize task delay. Task delay consists of communication delay and execution delay. Computing and storage capacity are treated as limiting conditions for decision-making. To solve the offloading problem, we convert the dissatisfaction of alliance into a vector, and obtain the Pareto optimal through multi-objective particle swarm algorithm. Then, we use Branch and Bound method to construct the propagation tree to facilitate decision-making. To evaluate the edge servers in the tree, we build an evaluation matrix and transform the matrix to a set of evaluation index which is used on task offloading decision-making. A large number of experimental results show that our algorithm is better than compared algorithm.

Traffic Load Optimization for Multi-Satellite Relay Systems in Space Information Network: A Proportional Fairness Approach.

Backbone satellites in a space information network (SIN) can be used as air base stations or data relay satellites (DRSs) to realize cross-system, cross-network and long-distance relay transmission. In this paper, a traffic load optimization problem for multi-satellite relay systems in SIN is considered to achieve highly efficient cooperative transmission and improve resource utility. A model of SIN based on a distributed satellite cluster (DSC) is considered, and the characteristics of the model are analyzed. Based on this, a hybrid resource management architecture combining distributed and central resources control schemes is proposed to realize a centrally controllable and distributed optimization of resources to meet various comprehensive service requirements. Two scenarios of multi-satellite relay systems in SIN are given, and traffic load optimization problems with joint bandwidth and power allocation for these two scenarios are formulated based on proportional fairness (PF) criterion to achieve traffic load balancing with considerable system capacity. The optimization problems in these two scenarios are proved to be a convex optimization problem with mathematical analysis, and the closed-form solutions of two problems in their dual domain are derived by dual transformation. With the closed-form solutions, two iterative algorithms based on the subgradient method are designed under the proposed hybrid resource management architecture to solve the problems in this paper. Simulation results show that the proposed schemes can effectively improve the upper bound of system capacity by resource sharing and cooperative relay, and it can balance the traffic load well with guarantees of a reasonable level system capacity compared with existing methods.

Joint bandwidth and power resource allocation technique in multi‐carrier non‐orthogonal multiple access‐based cognitive Internet of Things networks

AbstractThe recent development of the Internet of Things (IoT) devices offers an intelligent concept for integrating massive number of interconnected wireless devices. However, supporting such massive number of IoT connections (hundreds of billions) requires an efficient and dynamic spectrum access strategy. Accordingly, the integration of non‐orthogonal multiple access (NOMA) in the cognitive radio (CR) systems, referred to as a multi‐carrier NOMA CR‐enabled system, is envisioned as a potential spectrum‐efficient networking paradigm that can support massive energy‐efficient IoT connectivity in B5G networks while maintaining the quality of services (QoS) of the IoT devices. However, the power consumption issue of the multi‐carrier NOMA CR‐based system becomes as one of key challenges, especially when considering the expected massive connectivity. Accordingly, several power‐aware optimization frameworks have been considered to address this issue. While most of the existing CR‐based multi‐carrier NOMA power allocation mechanisms only optimize the allocated per‐user power, this article proposes a novel joint bandwidth and power allocation problem over each available idle channel with the aim of minimizing the overall transmission power under a set of QoS constrains. Specifically, the bandwidth of each channel is adaptively subdivided into two variable‐width sub‐channels, each is capable to serve a group of CR users using power‐domain NOMA. This novel design is formulated as a joint optimization problem that is shown to be a non‐convex. Therefore, an iterative algorithm with a second‐order cone programming is deployed to handle the non‐convexity of the problem, and thus, determine the solution of it. Simulation results reveal that the developed variable bandwidth adaptive power allocation mechanism outperforms the conventional equal sub‐channel allocation in terms of the required transmission power, which significantly improves the energy efficiency in the system.

Joint Bandwidth and Transmission Opportunity Allocation for the Coexistence Between NR-U and WiFi Systems in the Unlicensed Band

The explosively increasing traffic demand in the fifth generation (5 G) mobile communication system has made the limited licensed spectrum quite competitive. Such status quo motivates the development of new radio (NR) based access to the unlicensed band, which is a promising solution for system performance enhancement. In this paper, we focus on the coexistence between the NR system and the WiFi system based on the duty cycle mechanism. Firstly, an optimization model is formulated to maximize the throughput of the NR system in the unlicensed band (NR-U) via the joint bandwidth and transmission opportunity allocation under the fairness requirement. Then, to solve this optimization problem, we convert it into a Markov decision process with continuous action space, based on which a deep deterministic policy gradient (DDPG) based coexistence algorithm is proposed. At last, simulation results show that the proposed algorithm can ensure the coexistence between the NR-U system and the WiFi system from three aspects. Firstly, the throughput improvements of the NR-U system under different fairness thresholds and different number of user equipments and access points are illustrated. Secondly, the proposed algorithm adapts to the dynamic situations of varying fairness thresholds and channel conditions. Thirdly, the effectiveness of the proposed algorithm is validated with at least 5.01% throughput improvement by comparing with proportional scheme, average scheme and Q-learning scheme.

Joint Bandwidth Allocation and Path Selection in WANs with Path Cardinality Constraints

In this paper, we study a joint bandwidth allocation and path selection problem via solving a multi-objective minimization problem under the path cardinality constraints, namely MOPC. Our problem formulation captures various types of objectives including the proportional fairness, the total completion time, as well as the worst-case link utilization ratio. Such an optimization problem is very challenging since it is highly non-convex. Almost all existing works deal with such a problem using relaxation techniques to transform it to be a convex optimization problem. However, we provide a novel solution framework based on the classic alternating direction method of multipliers (ADMM) approach for solving this problem. Our proposed algorithm is simple and easy to be implemented. Each step of our algorithm consists of either finding the maximal root of a single-cubic equation which is guaranteed to have at least one positive solution or solving a one-dimensional convex subproblem in a fixed interval. Under some mild assumptions, we prove that any limiting point of the generated sequence under our proposed algorithm is a stationary point. Extensive numerical simulations are performed to demonstrate the advantages of our algorithm compared with various baselines.

Improper Gaussian Signaling for D2D Communication Coexisting MISO Cellular Networks

Improper Gaussian signaling (IGS) has shown its capability of improving the rate of interference-limited networks by exploiting the additional degrees of freedom in signal processing. This article considers a system of a multiple-input single-output (MISO) cellular network coexisting with device-to-device (D2D) communication, where the former employs proper Gaussian signaling (PGS) but the latter employs IGS to improve D2D's rate and also to mitigate interference to the former. Both non-orthogonal and orthogonal bandwidth sharing between cellular users (CUs) and D2D pairs are considered. The problems of joint bandwidth allocation and signal beamforming to maximize the minimum CUs' rate subject to the transmit power budget and D2D's rate threshold are addressed, which pose critical computational challenges. Path-following algorithms of low complexity are developed for computational solutions. Two distinct scenarios, i.e., unmanned aerial vehicle (UAV)-enabled networks, and MISO cellular networks are simulated to give insight into the superiority of using IGS over PGS. Our results reveal that in UAV-enabled networks, orthogonal sharing produces a higher D2D's rate, while non-orthogonal sharing offers better CUs' rate under practical levels of D2D's rate. In MISO systems, IGS is a game-changer, which enables the orthogonal sharing to uniformly outperform the non-orthogonal sharing in terms of CUs' rate.

UAV-Aided Two-Way Multi-User Relaying

Unmanned aerial vehicle (UAV)-aided two-way relaying networks are designed, where a UAV is deployed to assist multiple pairs of users in their information exchange. There are two basic approaches for the user pairs’ information exchange within a single time slot via the UAV relay. The first approach is based on full-duplex, where all participants operate in the full-duplex mode to transmit and receive signals simultaneously. However, all transceivers have to operate in the face of severe self-interference, which cannot be completely suppressed. The second approach is based on conventional half-duplex, where the users send their information to the UAV within a certain fraction of the time slot, and the UAV relays them within the remaining fraction to avoid the self-interference. In either approach, the joint bandwidth and power allocation maximizing the sum information exchange throughput under realistic resource and user throughput constraints poses a complex nonconvex problem. New inner approximations are proposed for developing path-following algorithms for their computation. Our numerical results show that the time-fraction-based half-duplex approach clearly outperforms the high-complexity full-duplex approach.

Joint Task Assignment and Spectrum Allocation in Heterogeneous UAV Communication Networks: A Coalition Formation Game-Theoretic Approach

Coalition structure is an efficient networking architecture for task implementation in unmanned aerial vehicle (UAV) networks. However, both the formation of coalition and the spectrum resource for intra-coalition communication affect the reconnaissance performance. In this paper, we investigate a cooperative reconnaissance and spectrum access (CRSA) scheme for task-driven heterogeneous coalition-based UAV networks by jointly optimizing task layer and resource layer. Specifically, coalition formation game (CFG) is formulated to jointly optimize task selection and bandwidth allocation. In addition to the traditional Pareto order and selfish order, coalition expected altruistic order maximizing coalitions' utility is proposed. The CFG under the proposed order is proved to be an exact potential game (EPG). Then the existence of stable coalition partition is guaranteed with the help of Nash equilibrium (NE). We propose a joint bandwidth allocation and coalition formation (JBACF) algorithm to achieve stable coalition partition wherein an efficient gradient projection (GP) based method is applied to solve bandwidth allocation. The effectiveness of the proposed scheme and algorithms are demonstrated through in-depth numerical simulations. The results show that our proposed CRSA scheme is superior to non-joint optimization scheme. Also, the proposed order is superior to traditional Pareto order and selfish order.

Let's Share the Resource When We’re Co-Located: Colocation Edge Computing

Multi-access Edge Computing (MEC) is recently acknowledged as one of the key pillars for the next revolution of mobile communications area, where the convergence of IT and telecommunications network provides the low latency and computation capability for cellular base stations (BSs). As a result, this is a great opportunity for mobile network operators by deploying new services and applications at BSs. Nevertheless, huge capital and operational cost can challenge mobile operators for the deployment of new BSs and MEC micro-datacenters. Colocation Edge Computing (ColoMEC) is a new concept where multiple operators share not only the same BS tower but also their radio and computation resources colocated at the edge sites. In order to reduce the operational cost of a ColoMEC system, the limited bandwidth at over-utilized colocation BSs can be extended by sharing the bandwidth among BSs, while shared MEC micro-datacenters can be scaled based on the arrival traffic loads. Thus, sharing the BS infrastructure, bandwidth, and MEC micro-datacenters among the co-located mobile operators can be an economical solution to provide high-performance services with low expenses by exploiting the temporal and spatial difference in traffic loads. Turning this vision into reality, we study a joint bandwidth allocation sharing and MEC micro-datacenter scaling in ColoMEC management problem ( $\mathbf {ColoMEC-MP}$ ). To solve $\mathbf {ColoMEC-MP}$ problem, we propose an algorithm based on proximal block coordinate descent technique by iteratively solving the decoupled convex subproblems (i.e., user association, bandwidth allocation, and MEC micro-datacenter scaling) with additional proximal terms. To improve the convergence of the proposed algorithm, we propose a greedy initialization for the user association which is based on the link capacity at each user. Our simulation demonstrates the superiority of the algorithm in terms of the operational cost compared with fixed service rate of shared MEC micro-datacenters strategies.

Data Distribution Optimization Strategy in Wireless Sensor Networks With Edge Computing

Resource-constrained wireless sensor networks (WSNs) impose a substantial challenge to the transmission and processing of non-redundant perceptual data. Moreover, breakthroughs in this area have opened up a new dimension that integrates edge computing technology in wireless sensor networks. However, achieving reliable data distribution in wireless sensor networks with edge computing (EWSN) and balancing the link bandwidth and node energy resources have become challenging problems. In this article, we propose a joint bandwidth allocation and energy consumption (JBAEC) algorithm for distributing different perceptual data to different sensor nodes of the EWSN by considering the available transmission links between the source sensor and destination nodes under the condition of early perceived energy. Therefore, we first establish the model of different data packet types and energy detection costs. Second, we describe the problem of effective bandwidth allocation and prove its NP-hard feature. In addition, we formulate the bandwidth allocation problem as an optimization problem that is divided into the two states of sufficient energy and lack of energy. Afterwards, we present an available node selection (ANS) algorithm for sorting the available relay nodes under the condition of different energy-sufficient nodes. The effective transmission network construction (ETNC) algorithm selects the available transmission links based on the set of available nodes. Finally, we implement the JBAEC algorithm by using the NS-2 simulator and present extensive simulation results to verify the data distribution efficiency of the JBAEC algorithm in the EWSN.

Effective Capacity-Based Resource Allocation in Mobile Edge Computing With Two-Stage Tandem Queues

In the mobile edge computing (MEC) network, the applications of devices can be offloaded to the MEC server via the wireless link and then processed through the computation resource, to satisfy the computation and latency demand. Thus, a two-stage tandem queue is formed in the MEC network, consisting of the transmission queue and computation processing queue. However, the fluctuating wireless channel environment not only leads to the stochasticity of service in the first transmission queue, but also brings random computation task arrival in the second computation processing queue, which makes it difficult to guarantee the end-to-end quality of service (QoS) requirement. In this paper, we firstly derive the effective capacity of MEC with the two-stage tandem queue. Further, we formulate the joint bandwidth and computation resource allocation problem under the statistical QoS guarantee, to maximize the total revenue of network. This problem is proven to be NP-hard by the reduction to the two-dimensional knapsack problem. Then we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to reduce the computation complexity, where the complicated problem can be decomposed and transformed into some convex subproblems. Simulation results reveal the inherent relationship between the required bandwidth and computation resource in terms of the supported arrival rate and end-to-end delay, and also demonstrate the proposed scheme can achieve better performance than other schemes.

Buffer-Aware Streaming in Small-Scale Wireless Networks: A Deep Reinforcement Learning Approach

Buffer-aware video streaming, which exploits the available storage space in user device to store the prefetched video data in good channels for video data use in poor channels, has been proved to have the potential to reduce the impact of fluctuating wireless channels on user-perceived video performance. However, in practical wireless networks, due to the unknown channel state and video rate, providing buffer-aware video streaming service to wireless user is a challenging problem. In this paper, with the aim to design an autonomous wireless video streaming system, we apply the deep reinforcement learning approach to dynamic resource optimization for wireless buffer-aware video streaming under unknown channel state and video rate. Specifically, we define a reward function for buffer-aware video streaming as the effective video streaming time when neither video-playback overflow nor video-playback underflow occurs. We propose a Markov decision process based problem formulation of the joint bandwidth allocation and buffer management for maximizing the effective video streaming time of all users. The optimal bandwidth allocation and buffer management policy is learned from training a deep neural network based on a deep reinforcement learning algorithm. We simulate the proposed algorithm in Tensorflow. Simulation results verify that the proposed deep reinforcement learning approach is effective for buffer-aware video streaming in wireless networks.

Joint bandwidth and power allocation for multiple services in TV white space

As the bandwidth resource in TV white space (TVWS) is permitted to be used for the secondary users, multiple secondary networks coexisting in the same geographical area can provide multiple services without interfering with the primary service, such as the TV broadcasting. To satisfy the minimum rate requirements and maximise the sum rate of the multiple services, the problem of resource allocation can be modelled as a mixed integer non-linear programming (MINLP). According to this model, the joint bandwidth and power allocation (JBPA) method is proposed to solve the resource allocation for multiple services. The JBPA method is sub-optimal, which can approximate the performance of the optimal two-step exhaustive search (TSES) method very well, especially when the number of services is larger than the number of idle channels. Compared with TSES, the JBPA method decreases the complexity from exponential level to polynomial level. Besides, compared with one channel occupied by single service, the JBPA can serve more services and provide stable performance when the number of services varies dynamically. Compared with the algorithm random bandwidth allocation, the JBPA can approximate the optimal performance provided by TSES better with the time complexity of the same order.

On max-min fair allocation for multi-source transmission

Max-min fair is widely used in network traffic engineering to allocate available resources among different traffic transfers. Recently, as data replication technique developed, increasing systems enforce multi-source transmission to maximize network utilization. However, existing TE approaches fail to deal with multi-source transfers because the optimization becomes a joint problem of bandwidth allocation as well as flow assignment among different sources. In this paper, we present a novel allocation approach for multi-source transfers to achieve global max-min fairness. The joint bandwidth allocation and flow assignment optimization problem poses a major challenge due to nonlinearity and multiple objectives. We cope with this by deriving a novel transformation with simple equivalent canonical linear programming to achieve global optimality efficiently. We conduct data-driven simulations, showing that our approach is more max-min fair than other single-source and multi-source allocation approaches, meanwhile it outperforms others with substantial gains in terms of network throughput and transfer completion time.

SDN-Based Multi-Class QoS Guarantee in Inter-Data Center Communications

In this paper, we describe a software defined networking (SDN) approach to traffic engineering in inter-data center communication; then present <inline-formula><tex-math notation="LaTeX">$\mathrm{MCTEQ}$</tex-math></inline-formula> , a constrained utility-optimization formulation of the joint-bandwidth allocation problem for multiple classes of traffic in inter-data center communication. <inline-formula><tex-math notation="LaTeX">$\mathrm{MCTEQ}$</tex-math></inline-formula> handles priorities between traffic classes in a soft manner and explicitly considers the delay requirement of <inline-formula><tex-math notation="LaTeX">$\mathrm{Interactive}$</tex-math></inline-formula> flows. <inline-formula><tex-math notation="LaTeX">$\mathrm{MCTEQ}$</tex-math></inline-formula> being NP-hard, we construct a new approximation to be able to lean on the mature and efficient linear programming solvers to obtain fast and accurate approximations. We demonstrate via numerical experiments with two realistic inter-data center network topologies that <inline-formula><tex-math notation="LaTeX">$\mathrm{MCTEQ}$</tex-math></inline-formula> achieves considerably more network utilization than the best known solutions from the literature, while running at least twice faster. We also show via ns-2 packet level simulation that with <inline-formula><tex-math notation="LaTeX">$\mathrm{MCTEQ}$</tex-math></inline-formula> traffic engineering, end-to-end packet delay requirements of <inline-formula><tex-math notation="LaTeX">$\mathrm{Interactive}$</tex-math></inline-formula> flows are indeed guaranteed for in-profile traffic, which is not the case for alternative approaches.

3-D Drone-Base-Station Placement With In-Band Full-Duplex Communications

Drone base stations (DBSs) can potentially provision low-cost and flexible networking with high mobility while in-band full-duplex (IBFD) can conceivably improve spectrum efficiency. Therefore, it is logical to employ DBSs with IBFD in a cellular network to improve the network throughput. We decompose this problem into the DBS placement problem and the joint bandwidth and power allocation problem, and propose two heuristic algorithms to solve the whole problem. Simulation results have demonstrated that the total throughput of the dynamic DBS placement algorithm achieves up to 45% improvement as compared with that of the strategy without DBSs.

Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs

Regarding access point (AP) overload and performance anomaly which is caused by mobile terminals with different bitrates, a joint AP association and bandwidth allocation optimization algorithm is presented in this paper. Meanwhile, load balancing and proportional fairness are analyzed and formulated as an optimization model. Then, we present a Fair Bandwidth Allocation algorithm based on clients’ Business Priority (FBA-BP), which allocates bandwidth based on the bandwidth demand of clients and their business priority. Furthermore, we propose a Categorized AP Association algorithm based on clients’ demands (CAA-BD), which classifies APs by different types of clients and chooses an optimal associating AP for a new client according to AP categories and the aggregated demand transmission time that are calculated by the FBA-BP algorithm. The CAA-BD can achieve load balance and solve the performance anomaly caused by multi-rate clients coexisting. The simulation results show that our proposed algorithm obtains significant performance in terms of AP utilization, throughput, transmission delay and channel fairness in different client density levels compared with the categorized and Strong Signal First (SSF) algorithms.

Power and Bandwidth Allocation for Cognitive Heterogeneous Multi-Homing Networks

In this paper, an uplink power and bandwidth allocation problem for multiple services with multi-homing technology is formulated for cognitive heterogeneous networks. The joint power and bandwidth allocation with multiple services is subject to constraints in system available bandwidth, proportional fairness transmission rate for non-real-time secondary mobile terminals (MTs), minimum required transmission rate for real-time secondary MTs, interference power for primary base station, and total power consumption for each secondary MT. The joint power and bandwidth allocation problem with multiple services based on risk-return model is formulated as a bargaining game framework, first. Then, an optimal power and bandwidth allocation algorithm utilizing a dual decomposition method is proposed to obtain Nash bargaining solution. Finally, a heuristic algorithm is proposed to reduce computational complexity. Simulation results demonstrate the optimal and heuristic algorithms not only improve the spectrum efficiency, but also guarantee the fairness for secondary MTs with non-real-time service.

Cooperation in 5G Heterogeneous Networking: Relay Scheme Combination and Resource Allocation

In 5G heterogeneous networking, it is promising to integrate different wireless networks to provide higher data rate. This paper models the integrated system as a receiver frequency division relay channel (RFDRC) and studies how to improve the transmission rate by combining decode-forward (DF), compress-forward (CF), and amplify-forward (AF) schemes. First, we establish clear criterions on how to select a relay scheme among DF, CF, and AF schemes and prove that the CF outperforms AF for all possible configurations. Based on the scheme selection criterions, we propose a hybrid DF–CF scheme which takes advantage of both DF and CF schemes in RFDRC. A near-optimal resource allocation is presented for the DF–CF-based system, leading to a new achievable rate for RFDRC. For ease of implementation, we further put forward a hybrid DF–AF scheme and reconsider the joint bandwidth and power allocation. Two suboptimal resource allocation solutions are established. In particular, when source frequency band and relay frequency band have equivalent bandwidth, we show that the proposed hybrid DF–AF scheme can achieve the concave envelope of the maximum between DF rate and AF rate. Numerical results show that the proposed schemes bring significant gains for RFDRC.

Survey on Energy Saving Methods for Green Communication Network

In recent years telecommunication is playing a vital role in communication field. The power usage has become a major problem since the usage of wireless communication has increased, which has leaded to environmental impact in a drastic manner. The main goal of designing green base stations is for saving energy and reducing power consumption while guaranteeing service and coverage for users and ensuring the capability of base station for evolution. To reduce the energy consumed by base station sleep mode mechanism is adapted. For uplink and downlink communication a joint bandwidth and power allocation method is deployed which promotes energy saving for both network operators and mobile users. Thus, Green networking is to reduce the unnecessary energy consumption keeping environmental factors into consideration. This paper deals with the survey of the problems upon implementation of base station in cellular networks for heterogeneous communication and also compares the various work related to green networking.