Distributed Resource Allocation Scheme Research Articles

Distributed Resource Optimization With Blockchain Security for Immersive Digital Twin in IIoT

Virtual Reality-embedded Digital Twin (VR-DT) service integrates digital twin with virtual reality (VR) to visualize the digital representation of real-world production, boosting the digital transformation of manufacturing industry in the Industrial Internet of Things (IIoT). Balanced against the advantages of the VR-DT service, its data-driven, computing-intensive and security-sensitive features bring challenges to the current IIoT. Therefore, we propose a blockchain-based distributed resource allocation (BDRA) scheme to improve the average Quality of Service (QoS) of the VR-DT services with regard to service delay and transaction throughput. We formulate the joint optimization of channel assignment, subframe configuration, computing capacity allocation and block size adjustment as a mixed-integer nonlinear programming problem. A Fully-Decentralized Multi-Agent Compound-Action Actor-Critic (FDMA-CA2C) algorithm is developed to solve the QoS optimization problem. Simulation results demonstrate that our proposed scheme can efficiently improve the average QoS of the VR-DT services in a realizable way as compared to existing schemes.

Distributed ranking-based resource allocation for sporadic M2M communication

This work proposes a novel scheme for distributed ranking-based and contention-free resource allocation in large-scale machine-to-machine (M2M) communication networks. We partition a network of N devices into disjoint clusters based on service type, and assign to each cluster a cluster-specific signature for active cluster members to indicate their active status. The devices in each cluster are totally ordered in some a priori-known manner, which gives rise to an active ranking of active cluster members. In order to tackle complexity issues in large-scale M2M networks with a massive number of devices, we propose a distributed resource allocation scheme using the framework of compressed sensing (CS), which mainly consists of three phases: (i) In a full-duplex acquisition phase, the devices transmit their cluster-specific signatures simultaneously and the network activation pattern is collected in a distributed manner. (ii) The base station detects the active clusters and the number of active devices per cluster using block sketching, and allocates resources to each active cluster accordingly. (iii) Each active device determines its active ranking in the cluster and accesses a specific resource according to the ranking position. By exploiting the sparsity in the activation pattern of the M2M devices, the proposed scheme is formulated as a CS support recovery problem for a particular binary block-sparse signal xin {mathbb{B}}^N – with block sparsity K_{B} and in-block sparsity K_{I} over block size d. Our analysis shows that the proposed scheme efficiently reduces the signature length to mathcal {O}(max {K_{B}log N, K_{B}K_{I}log d}) and achieves less computational complexity of mathcal {O}(dK_{I}^{2}+frac{N}{d}log N) compared with standard CS algorithms. Moreover, numerical results suggest strong robustness of the proposed scheme under noisy conditions.

Distributed Joint Channel Assignment and Power Control for Sum Rate Maximization of D2D-Enabled Massive MIMO System

Device-to-device (D2D) communications underlaid massive multiple-input multiple-output (MIMO) systems have been recognized as a promising candidate technology to achieve the challenging fifth-generation (5G) network requirements. This integration enhances network throughput, improves spectral efficiency, and offloads the traffic load of base stations. However, the co/cross-tier interferences between cellular and D2D communications caused by resource sharing is a significant challenge, especially when dense D2D users exist in an underlay mode. In this paper, we jointly optimize the channel assignment and power allocation to maximize the sum data rate while maintaining the interference constraints of cellular links. Due to the lack of network-wide information in large scale networks, resource management and interference coordination is hard to be implemented in a centralized way. Therefore, we propose a three-stage stable and distributed resource allocation and interference management scheme based on local information and requires little coordination and communication between devices. We model the channel allocation optimization problem in the first stage as a many-to-one matching game. In the second stage, the algorithm adopts a cost charging policy to solve each user’s power control problem as a non-cooperative game. In the third stage, the algorithm search for swap blocking pairs until stable matching exist. It is shown in this paper that the proposed algorithm converges to a stable matching and terminates after finite iterations. Simulation results show that the proposed algorithm can achieve more than 86% of the average transmission rate performance of the optimal matching with lower complexity.

Distributed Resource Allocation for General Energy Efficiency Maximization in Offshore Maritime Device-to-Device Communication

In maritime scenario, the communication service, if any, is likely to be expensive due to the high-cost satellite transmissions or the very limited offshore coverage of terrestrial networks. Thus, how to achieve efficient radio resource management is one of the critical issues in maritime communication. In this letter, by exploiting the device-to-device (D2D) mechanism, we identify a general energy efficiency (GEE) optimization problem, which takes into account the physical link performance and the users’ pairing willingness (PW) quantified by a modified Jaccard coefficient metric. Then, we divide the GEE optimization problem into two sub-problems of user pairing and power allocation, and solve them by a distributed pairing method and by a new distributed resource allocation scheme, respectively. Simulation results demonstrate significant performance gains achieved by the proposed GEE maximization-based distributed D2D resource allocation (GEEM-DD2D-RA) algorithm over existing methods.

A Novel Distributed Resource Allocation Scheme for Wireless-Powered Cognitive Radio Internet of Things Networks

This article considers a novel Internet of Things network comprising of sensor devices and power beacons (PBs); both types of nodes are equipped with a cognitive radio (CR). In addition, these sensor devices are powered by radio-frequency signals from PBs. Our aim is to maximize the minimum rate of devices acting as sources. We outline the first mixed integer linear program (MILP) that jointly optimizes the channel assignment of PBs and devices, beamforming vector of PBs, data routing over multiple hops and link activation schedule of devices. We also design a distributed protocol called distributed max–min rate with CR (D-MRCR) for use by devices and PBs. Devices set their operation mode using local information and use a game theory-based approach to iteratively adjust their transmit power. On the other hand, each PB employs a linear program to determine its beamforming vector. Our results show that the max–min rate of D-MRCR is within 51.84% that of MILP.

A Distributed Resource Allocation Scheme for Self-Backhauled Full-Duplex Small Cell Networks

Full-duplex (FD) radio with wireless backhauling, is a key technology for dense small cell deployments in fifth generation (5G) cellular networks. In this paper, the problem of joint user scheduling, operation mode selection and power allocation in a two-tier cellular network with FD-capable small basestations is considered. The objective is to maximize the network sum rate under quality-of-service requirements and backhaul capacity constraints. In order to solve the problem, a distributed algorithm is developed which divides the original problem into two subproblems, viz., (i) user scheduling and mode selection; and (ii) power allocation. For the first subproblem, a low-complexity search method is proposed for opportunistically finding the best user scheduling and operation modes. In the second subproblem, an iterative successive convex approximation method is proposed to transform the nonconvex power allocation problem into a sequence of convex subproblems. The proposed solution provides a hybrid strategy which opportunistically selects FD or half-duplex (HD) operation modes for access and backhaul links. It is observed that the proposed algorithm converges in few iterations and mitigates the interferences by properly adjusting the transmission powers. Numerical results demonstrate the superiority of the proposed scheme over other possible scenarios. In particular, the proposed algorithm can achieve up to 44% gain in overall sum rate compared to a network that solely uses HD.

Distributed optimal resource allocation over strongly connected digraphs: A surplus-based approach

In this brief, a surplus-based approach is proposed to solve the distributed optimal resource allocation problem over directed graphs. Two features distinguish our method from the existing distributed resource allocation schemes. The first one is that the proposed method can be implemented on general strongly connected graph and overcomes the asymmetry caused by the digraphs. The second one is that the proposed method enjoys the privacy-preserving property and avoids the privacy information leakage in the process of information exchange. The distributed algorithm is designed and analyzed by virtue of primal–dual methods, projected gradient, and eigenvalue perturbation theory. Furthermore, distributed economic dispatch in power system, as a simulation example, is used to illustrate the effectiveness of the presented distributed algorithm.

Efficient Resource Allocation Using Distributed Edge Computing in D2D Based 5G-HCN With Network Slicing

Fifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead.

Centralized and Distributed Age of Information Minimization With Nonlinear Aging Functions in the Internet of Things

Resource management in Internet-of-Things (IoT) systems is a major challenge due to the massive scale and heterogeneity of the IoT system. For instance, most IoT applications require timely delivery of collected information, which is a key challenge for the IoT. In this article, novel centralized and distributed resource allocation schemes are proposed to enable IoT devices to share limited communication resources and to transmit IoT messages in a timely manner. In the considered system, the timeliness of information is captured using nonlinear Age-of-Information (AoI) metrics that can naturally quantify the freshness of information. To model the inherent heterogeneity of the IoT system, the nonlinear aging functions are defined in terms of IoT device types and message content. To minimize AoI, the proposed resource management schemes allocate the limited communication resources considering AoI. In particular, the proposed centralized scheme enables the base station to learn the device types and to determine aging functions. Moreover, the proposed distributed scheme enables the devices to share the limited communication resources based on available information on other devices and their AoI. The convergence of the proposed distributed scheme is proved, and the effectiveness in reducing the AoI with partial information is analyzed. Furthermore, the proposed resource management schemes with different number of devices, activation probabilities, and outage probabilities are analyzed in terms of the average instantaneous AoI. Simulation results show that the proposed centralized scheme achieves significantly lower average instantaneous AoI when compared to simple centralized allocation without learning, while the proposed distributed scheme achieves significantly lower average instantaneous AoI when compared to random allocation. The results also show that the proposed centralized scheme outperforms the proposed distributed scheme in almost all cases, but the distributed approach is more viable for a massive IoT.

Resource and Power Allocation for OFDM-Based Device-to-Device Communications in a Multicell Environment

We investigate the optimal resource and power allocation for device-to-device (D2D) communications in a multicell environment. When D2D links reuse the cellular radio resources, each D2D user will interfere with a cellular link and other D2D links, in its own cell as well as in adjacent cells. We first develop a distributed resource allocation scheme that is performed by each base station independently. Then, we propose a coordinated resource allocation scheme that can handle the intercell interferences as well as the intracell interference. For a given resource allocation, we also formulate a power optimization problem and present an algorithm to find the optimal solution. The resource and power allocation algorithms are designed to maximize the achievable rate of the D2D link, while limiting the interference generated to the cellular link. The performance of the proposed algorithms is evaluated through simulations in a multicell environment. Numerical results are presented to verify the coordination gain in the resource and power allocation.

Fuzzy-logic based Q-Learning interference management algorithms in two-tier networks

Unloading from macrocell network and enhancing coverage can be realized by deploying femtocells in the indoor scenario. However, the system performance of the two-tier network could be impaired by the co-tier and cross-tier interference. In this paper, a distributed resource allocation scheme is studied when each femtocell base station is self-governed and the resource cannot be assigned centrally through the gateway. A novel Q-Learning interference management scheme is proposed, that is divided into cooperative and independent part. In the cooperative algorithm, the interference information is exchanged between the cell-edge users which are classified by the fuzzy logic in the same cell. Meanwhile, we allocate the orthogonal subchannels to the high-rate cell-edge users to disperse the interference power when the data rate requirement is satisfied. The resource is assigned directly according to the minimum power principle in the independent algorithm. Simulation results are provided to demonstrate the significant performance improvements in terms of the average data rate, interference power and energy efficiency over the cutting-edge resource allocation algorithms.

An Interference Coordination-Based Distributed Resource Allocation Scheme in Heterogeneous Cellular Networks

As a result of the significant increase of overlapped coverage areas among base stations (BSs), interference coordination in heterogeneous cellular networks (HetCNets) becomes necessary, since interference would degrade network performance and even cause dropped calls. In addition, various types of BSs coexist in HetCNets, and BSs with low power (such as Pico BSs and Femto BSs) are deployed more arbitrarily than those in macrocell BSs (MBSs) so that the traditional anti-interference technologies are not enough in HetCNets. To reduce the interference between MBSs and low-power BSs within the coverage, a novel distributed resource allocation algorithm is proposed. First, an interference graph is established, by which the corresponding orthogonal resources can be assigned to different BSs, and the users can be classified into center and edge users, respectively. After that, users select appropriate resource blocks according to an improved proportional fair algorithm, and BSs distribute transmission power to different resource blocks to enhance network throughput further. Simulation results demonstrate the effectiveness of our proposed scheme in resource allocation and utilization.

Distributed and Dynamic Resource Management for Wireless Service Delivery to High-Speed Trains

With the fast development of high-speed railway (HSR), how to provide high-quality and cost-effective wireless services for HSR users has attracted increasing attention in recent years. A key issue is to design an efficient resource management scheme for wireless service delivery between the train and the ground. In this paper, we first provide an overview of the existing resource management schemes and some unsolved challenges are identified. To address these challenges, a cross-layer optimization framework is developed for facilitating the design and optimization of dynamic resource management. Then, the resource management problem is formulated as a stochastic optimization problem, which jointly considers the quality-of-service requirements and dynamic characteristics of HSR wireless communications. The stochastic network optimization theory is applied to transform the intractable stochastic optimization problem into a tractable deterministic optimization problem, which can be further decomposed into two separate subproblems: admission control and resource allocation. A fully distributed admission control scheme is proposed for the admission control subproblem, and a cooperative distributed resource allocation scheme is developed for the mixed-integer resource allocation subproblem with guaranteed global optimality. Finally, a distributed dynamic resource management algorithm is proposed to solve the original stochastic optimization problem with high reliability and robustness against central node failure. The performance of the proposed algorithm is analyzed theoretically and further validated by numerical simulations under realistic conditions for HSR wireless communications.

Social-Aware Resource Allocation for Content Dissemination Networks: An Evolutionary Game Approach

Recently, the increasing popularity of local area services, such as YouTube, Facebook, and Twitter, on which thousands of clients subscribe and download popular contents all the while, has drawn more and more attention on the study of content dissemination networks. Device-to-device (D2D) communication, which allows two devices to directly communicate with each other, has become an effective content dissemination method. With the goal of reducing the delay of content dissemination process with D2D communication, we propose an evolutionary game (EG)-based distributed resource allocation scheme. Moreover, we theoretically prove the existence and stability of game equilibrium and further propose a global search algorithm to achieve equilibrium. In the algorithm, all D2D links will select resource adaptively based on the predicted contact duration. Aiming at improving the prediction accuracy of contact duration, we propose social trajectory similarity (STS) to represent the overlapping of history trajectory among all the mobile users by mining user behavior patterns . Numerical results show that our proposed STS increases nearly 20% correlation with history trajectory overlap compared with jaccard index. Furthermore, our EG-based scheme reduces the delay over 15% compared with the coalition game scheme, and over 30% compared with the random selection scheme. In addition, our proposed scheme also increases throughput efficiency with nearly 20% and achieves better fairness.

Virtual Resource Allocation in Information-Centric Wireless Networks With Virtualization

Wireless network virtualization and information-centric networking (ICN) are two promising technologies in next-generation wireless networks. Traditionally, these two technologies have been addressed separately. In this paper, we show that jointly considering wireless network virtualization and ICN is necessary. Specifically, we propose an information-centric wireless network virtualization framework for enabling wireless network virtualization and ICN. Then, we formulate the virtual resource allocation and in-network caching strategy as an optimization problem, considering not only the revenue earned by serving the end users but the cost-of-leasing infrastructure as well. In addition, with recent advances in distributed convex optimization, we develop an efficient alternating direction method of multipliers (ADMM)-based distributed virtual resource allocation and in-network caching scheme. Simulation results are presented to show the effectiveness of the proposed scheme.

Joint Spectrum and Power Allocation for D2D Communications Underlaying Cellular Networks

This paper addresses the joint spectrum sharing and power allocation problem for device-to-device (D2D) communications underlaying a cellular network (CN). In the context of orthogonal frequency-division multiple-access systems, with the uplink resources shared with D2D links, both centralized and decentralized methods are proposed. Assuming global channel state information (CSI), the resource allocation problem is first formulated as a nonconvex optimization problem, which is solved using convex approximation techniques. We prove that the approximation method converges to a suboptimal solution and is often very close to the global optimal solution. On the other hand, by exploiting the decentralized network structure with only local CSI at each node, the Stackelberg game model is then adopted to devise a distributed resource allocation scheme. In this game-theoretic model, the base station (BS), which is modeled as the leader, coordinates the interference from the D2D transmission to the cellular users (CUs) by pricing the interference. Subsequently, the D2D pairs, as followers, compete for the spectrum in a noncooperative fashion. Sufficient conditions for the existence of the Nash equilibrium (NE) and the uniqueness of the solution are presented, and an iterative algorithm is proposed to solve the problem. In addition, the signaling overhead is compared between the centralized and decentralized schemes. Finally, numerical results are presented to verify the proposed schemes. It is shown that the distributed scheme is effective for the resource allocation and could protect the CUs with limited signaling overhead.

Utility-based efficient dynamic distributed resource allocation in buffer-aided relay-assisted OFDMA networks

In this paper, we study resource allocation in buffer-aided relay-assisted OFDMA networks. We consider utility-based stochastic optimization framework where there are constraints to be met either instantaneously or in average sense. Using the well-known Lyapunov drift-plus-penalty policy, we extract the instantaneous problem that needs to be solved in each slot to control the data admission and allocate the time slots, power, and subchannels. We propose the parameters that should be taken into account in utilizing the drift-plus-penalty policy in relay-assisted cellular networks, for providing fair data admission and satisfying the average power constraints. We introduce a low-complexity strategy for power and subchannel allocation and propose distributed and centralized algorithms to utilize it. Specifically, the proposed efficient dynamic distributed resource allocation (EDDRA) scheme is suitable for use in practice as it imposes less overhead on the system and splits the resource allocation tasks among the base station (BS) and the relays. Extensive simulation results show the effectiveness of the proposed parameters in meeting the objective and the constraints of the studied problem. We also show that the proposed EDDRA scheme has close performance to the proposed centralized one and outperforms an existing centralized scheme.

Distributed Resource Allocation for Multihop Decode-and-Forward Relay Systems

In multihop relay networks with a large number of hops, it is challenging to allocate the available resources optimally among the relay nodes due to the large number of required control signals between the controller and the relay nodes. In addition, the latency caused by this signaling can result in using outdated resource-allocation information as the channels are time varying. Hence, a distributed resource-allocation scheme is proposed for multihop decode-and-forward (DF) relay systems, with low complexity in analytical computation and practical implementation. Based on the type of division multiplexing in time or frequency, this scheme can be used to obtain the optimal per-hop time slot length or bandwidth, respectively. This distributed iterative scheme needs no additional resource for optimization. The required communication to fulfill this optimization is only with the adjacent nodes of each relay and can be performed through the forwarding and acknowledgment packets (ACKs). It is shown that this scheme converges to the global optimal solution with an exponential convergence rate. The performance of this scheme is also investigated for the time-variant transmission channels by computer simulations.

Weighted Bandwidth–Power Product Optimization in Downlink Femtocell Networks

In order to develop a completely distributed resource allocation scheme to improve data rates and mitigate mutual interference across orthogonal frequency-division multiple access (OFDMA)-based femtocell networks, a space–bandwidth product (SBP) has been proposed as an effective measurement of network coverage. Intuitively, a femtocell minimizing its SBP tends to utilize subchannels experiencing less interference and will thus minimize its interference on other femtocells, which represents good self-organizing performance and enables a completely distributed resource allocation algorithm. However, for an OFDMA-based femtocell, most existing studies have applied the same weights on the bandwidth and the power without distinguishing the different significance on them. In this letter, we first formulate a weighted bandwidth–power product minimization problem for an OFDMA-based femtocell, applying different emphases on the consumed bandwidth and power while satisfying all users' data rate requirements. Then, in this direction, we further reformulate and iteratively solve this nonconvex problem via the Successive Convex Approximation for Low-complExity (SCALE) approach. Simulation results show that our proposed algorithm converges to stable solutions in a finite number of iterations.

An Evolutionary Game for Distributed Resource Allocation in Self-Organizing Small Cells

We propose an evolutionary game theory (EGT)-based distributed resource allocation scheme for small cells underlaying a macro cellular network. EGT is a suitable tool to address the problem of resource allocation in self-organizing small cells since it allows the players with bounded-rationality to learn from the environment and take individual decisions for attaining the equilibrium with minimum information exchange. EGT-based resource allocation can also provide fairness among users. We show how EGT can be used for distributed subcarrier and power allocation in orthogonal frequency-division multiple access (OFDMA)-based small cell networks while limiting interference to the macrocell users below given thresholds. Two game models are considered, where the utility of each small cell depends on average achievable signal-to-interference-plus-noise ratio (SINR) and data rate, respectively. Forthe proposed distributed resource allocation method, the average SINR and data rate are obtained based on a stochastic geometry analysis. Replicator dynamics is used to model the strategy adaptation process of the small cell base stations and an evolutionary equilibrium is obtained as the solution. Based on the results obtained using stochastic geometry, the stability of the equilibrium is analyzed. We also extend the formulation by considering information exchange delay and investigate its impact on the convergence of the algorithm. Numerical results are presented to validate ourtheoretical findings and to show the effectiveness of the proposed scheme in comparison to a centralized resource allocation scheme.