Radio Resource Blocks Research Articles

Joint Resource Allocation in TWDM-PON-Enabled Cell-Free mMIMO System

Cell-free massive multiple input multiple outputs (CF-mMIMO) is considered a promising technology for sixth-generation (6G) telecommunication systems. In the CF-mMIMO system, an extensive array of distributed small base stations (BSs) is deployed across the network, which enables us to facilitate seamless collaboration among BSs. To achieve this goal, the baseband signal from these BSs needs to be transmitted to a central server via fronthaul networks. Due to the large number of BSs, the data that needs to be transmitted is usually huge, which brings severe requirements on fronthaul networks. Time and wavelength division multiplexed passive optical networks (TWDM-PON) can be a potential solution for CF-mMIMO fronthaul due to their large capacity and high flexibility. However, how to efficiently allocate both optical and wireless resources in a TWDM-PON-enabled CF-mMIMO system is still a problem to be addressed. This paper proposes a joint scheduling method of wavelength, antenna, radio unit (RU), and radio resource block (RB) resources in the TWDM-PON-enabled CF-mMIMO system. Furthermore, an integer linear programming (ILP) model for joint resource allocation is proposed to minimize the fronthaul resource occupancy, thereby increasing network scalability. Considering the complexity of the ILP model, two heuristic algorithms are also presented to solve this model. We compare the ILP with heuristic algorithms under different scenarios. Simulation results show that the proposed algorithm can reduce the fronthaul resource occupancy to improve the network scalability of the CF-mMIMO system.

Spectral Efficiency Improvement in Downlink Fog Radio Access Network With Deep-Reinforcement-Learning-Enabled Power Control

Fog radio access network (F-RAN) is a promising architecture that leverages edge computing and caching to improve devices’ latency and quality of service. However, interference, which arises when multiple devices are concurrently scheduled on the same radio resource block (RRB), limits the performance of a dense F-RAN. This paper considers a multi-cell F-RAN in which the devices of each small cell receive data from the associated fog access point (F-AP) over the same RRB(s). The F-APs transmit data to the associated devices using rate-splitting multiple access (RSMA) schemes to manage co-channel interference within the small cells efficiently. A transmit power control scheme is proposed to maximize the network’s spectral efficiency (SE) while considering the devices’ hardware impairments (HWIs). The considered transmit power control scheme is an NP-hard problem, which is highly challenging to solve using the legacy optimization approach. To address this challenge, we propose a distributed deep reinforcement learning (DRL)-based power allocation (DDPA) scheme that takes the time-varying dynamics of the network and the HWIs of devices into account. Each F-AP in the proposed framework is equipped with a DRL agent that collects signal-to-interference-plus-noise ratio and channel state information from connected devices and adapts the transmit power allocation each scheduling interval. In addition, the ensemble learning framework is exploited to further improve the proposed DDPA scheme’s performance. We use extensive simulations to demonstrate that the DDPA scheme achieves greater SE than contemporary transmit power control schemes. In particular, the proposed DDPA scheme is especially suited to scenarios with non-negligible HWIs-induced distortion.

DRL-assisted joint allocation of antenna, radio, and front-haul resources in TWDM-PON-based NG-RANs with mMIMO-enabled beamforming

The high spectrum efficiency of massive multiple input multiple output (mMIMO)-enabled beamforming is attractive for solving the massive number of connections in next-generation radio access networks (NG-RANs). However, the beamforming based on multiple antenna sub-arrays to support multi-connections would introduce extra 2D antenna sub-array selection and radio resource block (RB) allocation; meanwhile, considering the front-haul bandwidth consumption would reduce as much as possible the redundant data transmitting over front-haul in the NG-RANs. Therefore, a key issue is how to coordinate 2D antenna sub-array selection and radio RB allocation to minimize the front-haul bandwidth and maximize the radio RB utilization when accommodating a set of beam antenna array (BAA) requests. In this paper, we first establish a 3D BAA mapping model to jointly optimize 2D antenna sub-array selection and radio RB allocation, which would in turn affect the allocation of the front-haul wavelength bandwidth in a time and wavelength division multiplexed passive optical network (TWDM-PON)-based front-haul with the mMIMO antenna system. An integer linear programming mathematical model is formulated, and a novel deep reinforcement learning (DRL)-based algorithm is proposed to optimize the front-haul bandwidth and radio RB utilization. Besides, two heuristic algorithms are developed with different antenna sub-array selection policies as the benchmarks. The extensive simulation results show that our proposed DRL-based algorithm can attain the lowest average cost (AC) for different numbers of BAA requests considered by seeking the optimal trade-off between the front-haul bandwidth consumption and the number of the used antennas. Compared with the inter-sub-array benchmark, the DRL-based algorithm can achieve up to 7.5% AC reduction, which is mainly attributed to the 21.2% reduction of the front-haul bandwidth without increasing the number of used antennas.

Coordinated Scheduling and Decentralized Federated Learning Using Conflict Clustering Graphs in Fog-Assisted IoD Networks

Despite the advantages of fog-assisted internet of drones (IoD) networks for federated learning (FL) model aggregations, it is restricted by the limited battery capacity of drones and wireless channel conditions between a fog and drones. In this paper, we address the deployment problem of decentralized FL and the secrecy rate maximization problem in fog-assisted IoD networks. In particular, we leverage the overlapped zones of drones to spread their local aggregated models to the network. As such, the global model aggregation is achieved without any participation from the fog. Then, aiming to secure the proposed decentralized FL-based framework, we consider the secrecy rate maximization problem, which can be addressed by scheduling users to drones and their available radio resource blocks (RRBs). This coordinated scheduling problem of users-drones/RRBs is NP-hard. To efficiently solve it using graph theory techniques, we construct a centralized conflict clustering graph and reformulate the considered scheduling problem as a maximum-weight independent set (MWIS) problem in the constructed conflict graph. To alleviate the need for the coordinated scheduling policy at the fog, we propose to solve the coordinated scheduling problem in a distributed manner. Our proposed distributed schemes rely on the configuration of the constructed conflict clustering graph used to solve the MWIS problem. We conduct extensive simulations to verify the effectiveness of the proposed schemes over existing schemes. Selected numerical results show that the proposed distributed scheduling schemes provide similar performances in terms of secrecy rate and energy consumption as compared with the centralized scheme, with reasonable amount of information exchanges among the drones.

Task Offloading Optimization in NOMA-Enabled Dual-Hop Mobile Edge Computing System Using Conflict Graph

Resource allocation is investigated for offloading computational-intensive tasks in dual-hop mobile edge computing (MEC) system. The envisioned system has both the cooperative access points (APs) with the computing capability and the MEC servers. A user-device (UD), therefore, first uploads a computing task to the nearest AP, and the AP can either locally process the received task or offload to MEC server. To utilize the radio resource blocks (RRBs) in the APs efficiently, we exploit the non-orthogonal multiple access (NOMA) for offloading the tasks from the UDs to the AP(s). In order to investigate the trade-off between latency and energy consumption, this work considers minimizing a weighted-sum that consists of latency and energy consumption, subject to UDs’ rate threshold, tasks’ time-delay, computational frequency scaling, and transmit power allocation constraints. With a joint consideration of all such factors, the problem is NP-hard and its global optimal solution is computationally intractable. A graph-theoretical approach is employed to solve the problem efficiently. Specifically, a novel joint MEC graph-based approach is devised, which solves the scheduling among the UDs, APs, and RRBs, the transmit power control, and the local computational frequency scaling problem(s) jointly. The joint MEC approach achieves near-optimal performance with high computational complexity. To strike a suitable balance between the performance and computational complexity of the resource allocation, a low complexity, yet efficient, pruning graph approach is also devised. The efficiency of the proposed graph-based approaches over several benchmark schemes is verified via extensive simulations.

Resource Allocation for Joint Interference Management and Security Enhancement in Cellular-Connected Internet-of-Drones Networks

Internet-of-drones (IoD) systems require enhanced data transmission security and efficient interference management to accommodate the rapidly growing drone-based and rate-intensive applications. This paper develops a novel resource allocation scheme to jointly manage interference and enhance the physical layer security of cellular-connected IoD networks in the presence of a multi-band eavesdropping drone. Our envisioned cellular-connected IoD network has multiple full-duplex cellular base stations (CBSs), where each CBS reserves an orthogonal cellular radio resource block (RRB) for the aerial communication links. To efficiently utilize the cellular RRBs, each CBS is connected to a cluster of data transmitting drones using uplink non-orthogonal multiple access (NOMA) scheme. In addition, all the CBSs simultaneously transmit artificial noise signals to weaken the eavesdropper links. A joint optimization problem, considering the transmit power allocation and clustering of the legitimate drones, and the jamming power allocation of the CBSs, is formulated to maximize the worst-case average sum-secrecy-rate of the network. The joint optimization problem is decomposed into drone-clustering and power allocation sub-problems to obtain an efficient solution. A multi-agent reinforcement-learning framework is devised to solve the drone-clustering sub-problem. Meanwhile, the transmit and jamming power allocation sub-problem is solved by employing fractional programming, successive convex approximation, and alternating optimization techniques. By iteratively solving these two sub-problems, a convergent resource allocation algorithm, namely, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\underline{\text{s}}$</tex-math></inline-formula> ecurity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\underline{\text{i}}$</tex-math></inline-formula> nterference management with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\underline{\text{re}}$</tex-math></inline-formula> inforcement-learning and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\underline{\text{N}}$</tex-math></inline-formula> OMA (SIREN), is proposed. The superiority of SIREN over several benchmark schemes is verified via extensive simulations.

Interference Management in Cellular-Connected Internet of Drones Networks With Drone-Pairing and Uplink Rate-Splitting Multiple Access

Interference management is a key challenge for cellular-connected Internet of Drones (IoD) networks that employ multiple cellular-connected hovering drones for data acquisition in surveillance and monitoring applications. This article proposes a novel resource optimization framework for managing interference in cellular-connected IoD networks. Specifically, the envisioned system divides the set of transmitting drones into distinct drone pairs, where the paired drones simultaneously transmit over the same radio resource blocks (RRBs). Each drone pair is assigned a set of orthogonal RRBs for data transmission, where these RRBs are shared with the terrestrial cellular network as well. An uplink rate-splitting multiple access scheme is employed to mitigate the interdrone interference at the drone pairs, and an RRB pricing method is exploited to control the interference between the aerial and cellular communication links. Our goal is to maximize the uplink capacity of the IoD network while reducing interference over the shared RRBs between the IoD and cellular networks. Toward this goal, a joint optimization of the drones’ transmit power allocation, drone pairing, and RRB scheduling among the drone pairs is presented. In order to obtain an efficient suboptimal solution, an iterative optimization is devised. Particularly, the presented joint optimization problem is decomposed into three subproblems for transmit power allocation, drone pairing and RRB scheduling, and RRB price update. By solving theses subproblems iteratively, a convergent rate-splitting-empowered resource allocation and clustering for interference management (REACT) algorithm is proposed. Extensive simulations are conducted to verify the effectiveness of the proposed REACT algorithm over several benchmark schemes.

Throughput Maximization in Cloud-Radio Access Networks Using Cross-Layer Network Coding

Cloud radio access networks (C-RANs) are promising paradigms for the fifth-generation (5G) networks due to their interference management capabilities. In a C-RAN, a central processor (CP) is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resource blocks (RRBs). In this paper, we develop a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-layer network coding (CLNC)</i> approach that proposes to optimize RRH’s transmit powers and user’s rates in making the coding decisions. As such, cross-layer throughput of the network is maximized. The joint user scheduling, file encoding, and power adaptation problem is solved by designing a subgraph for each RRB, in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and power levels (PLs) for one RRB. It is then shown that the C-RAN throughput maximization problem is equivalent to a maximum-weight clique problem over the union of all such subgraphs, called herein the CRAN-CLNC graph. Numerical results revealed that the proposed joint and iterative schemes offer improved throughput performances as compared to the existing algorithms in the literature. Compared to our proposed joint scheme, our proposed iterative scheme has a certain degradation, roughly in the range of 9%–14%. This small degradation in the throughput performance of the iterative scheme comes at the achieved low computational complexity as compared to the high complexity of the joint scheme.

Optimized artificial neural network assisted trade-off between transmission and delay in LTE networks

The quality of data transmitted via LTE channels is often affected with the trade-off between the delay and network throughput. It often affects the performance of video transmitted with data rate distortion. In this paper, Lagrange dual multiplier with Artificial Neural Network (ANN) is used to solve the NP-hard problem and incorporates the Lagrange approach to achieve an optimum solution for higher network rating by reducing resource allocation. The way to find the optimal radio resource block (RRB) assignment strategy is iterated many times. Lagrange Dual Multiplier will also be used and implemented in this solution and this will help to achieve a closed form solution for any RRB assignment and further simulations to assess the efficiency of the process proposed. The experimental verification shows the efficacy of the model over existing state-of-art mechanism in terms of transmission errors and delay.

A Deep Learning Based Automatic Interference Avoidance Resource Allocation Scheme for SCMA Systems

Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.

Multi-Timescale Multi-Dimension Resource Allocation for NOMA-Edge Computing-Based Power IoT With Massive Connectivity

Integrating edge computing and non-orthogonal multiple access (NOMA) into the power Internet of Things (PIoT) provides a promising way to meet the stringent demands of communication delay, energy consumption, and massive connectivity. The service tasks can be partitioned into independent subtasks, and can be processed locally by PIoT devices or offloaded to the edge servers through reusing radio resource blocks (RBs). In this paper, we propose a Multi-timescale multi-dimEnsion Resource allocatIon and Task Splitting algorithm, namely MERITS, for NOMA-edge computing-based PIoT. First, Lyapunov optimization is exploited to decompose the long-term stochastic optimization problem into three short-term deterministic subproblems, i.e., RB allocation in a large timescale, task splitting and computation resource allocation in a small timescale. The first subproblem is modeled as a one-to-many matching problem with externalities, and solved by the proposed swap matching-based RB allocation algorithm. The second subproblem is relaxed into a continuous convex problem which is easily to be solved, and the third subproblem is solved by Lagrange dual decomposition. Simulation results demonstrate that MERITS outperforms the existing resource allocation and task splitting algorithms in terms of energy consumption, queuing delay, queue backlog, and connection success ratio in the massive connectivity scenario.

Joint Throughput-Power Optimization of Fog-RAN Using Rate-Splitting Multiple Access and Reinforcement-Learning Based User Clustering

Rate-splitting multiple access (RSMA) and reinforcement-learning (RL) based user clustering are leveraged to manage interference of a downlink fog radio access network. In particular, clustering of the user devices (UDs) is considered. The UDs of a given cluster can receive data from a certain fog access point (F-AP) over the same radio resource blocks (RRBs) simultaneously. To this end, RSMA enabled data transmission is exploited at each UD cluster. To manage intra-cluster and inter-cluster interference of the network, we focus on the resource allocation to maximize the sum-rate and to minimize total transmission power of the F-APs in each transmission slot. Towards this goal, we optimize jointly the F-APs’ transmit power allocation for RSMA enabled data transmission, clustering of UDs, and assignment of RRBs among the F-APs. The proposed optimization problem is NP-hard, and as a result, a global optimal solution is computationally intractable even with the centralized implementation. To obtain an efficient solution without having the global network information, the proposed optimization problem is decomposed into two sub-problems, namely, UD clustering and resource allocation among the F-APs. Specifically, the UD clusters are obtained by applying a multi-agent RL technique, and the resource allocation among the F-APs is obtained by applying the fractional programming, Lagrangian duality, and alternating optimization techniques. A distributed user clustering-power allocation-RRB assignment (UC-PA-RA) algorithm is proposed, and its convergence to the near-optimal solution is proved. Through extensive simulations, the superiority of the proposed UC-PA-RA algorithm over the contemporary multiple access schemes, UD clustering technique, and RL method is verified.

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes.

Massive IoT Access With NOMA in 5G Networks and Beyond Using Online Competitiveness and Learning

This paper studies the problem of online user grouping, scheduling and power allocation in beyond 5G cellular-based Internet of things networks. Due to the massive number of devices trying to be granted to the network, non-orthogonal multiple access method is adopted in order to accommodate multiple devices in the same radio resource block. Different from most previous works, the objective is to maximize the number of served devices while allocating their transmission powers such that their real-time requirements as well as their limited operating energy are respected. First, we formulate the general problem as a mixed integer non-linear program (MINLP) that can be transformed easily to MILP for some special cases. Second, we study its computational complexity by characterizing the NP-hardness of different special cases. Then, by dividing the problem into multiple NOMA grouping and scheduling subproblems, efficient online competitive algorithms are proposed. Further, we show how to use these online algorithms and combine their solutions in a reinforcement learning setting to obtain the power allocation and hence the global solution to the problem. Our analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms with comparison to optimal and state-of-the-art methods.

Low-Complexity Power Allocation for Network-Coded User Scheduling in Fog-RANs

Consider a Fog Radio Access Network (FRAN) in which a cloud base station (CBS) is responsible for scheduling user-equipments (UEs) to a set of radio resource blocks (RRBs) of Fog Access Points (F-APs) and for allocating power to the RRBs. The conventional graphical approach for solving the coordinated scheduling and power control problem in FRAN requires prohibitive computational complexity. This letter, instead, proposes a low-complexity solution to the problem under the constraint that all the scheduled UEs can decode the requested files sent by their associated RRBs/F-APs. Unlike previous solution that requires constructing the total power control graph, the proposed computationally efficient solution is developed using a single power control subgraph. Numerical results reveal a close-to-optimal performance of the proposed method in terms of throughput maximization for correlated channels with a significant reduction in the computational complexity.

Secrecy Rate Maximization via Radio Resource Allocation in Cellular Underlaying V2V Communications

In device-to-device (D2D) underlaying vehicle-to-vehicle (V2V) communications, radio resource blocks (RBs) are allocated to primary cellular users, which yields sub-optimal rates and a long access latency for secondary D2D-enabled vehicles. This work investigates a joint radio resource and power management (RRPM) problem for secure cellular underlaying V2V communications, where cellular users and vehicles have the same priority, giving vehicles more opportunities to access the RBs. Specifically, we aim to maximize secrecy rates of V2V channels under the condition that eavesdropper has an adaptive receiving detection vector to maximize the received signal-to-interference-plus-noise ratio (SINR) from vehicles. For a single pair of a vehicle and a cellular user, the closed-form optimal power allocation expressions are derived for the interference-limited scenario and the noise-limited scenario, respectively. Moreover, for multiple pairs of vehicles and cellular users, a 3-partite hypergraph based 3-dimensional matching approach is proposed to solve a mixed-integer and non-convex problem, which achieves a near-optimal result with an O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) time complexity. Simulations in different scenarios show that the secrecy rate of the proposed scheme can be improved by 50% if compared to existing schemes.

Ieee Access Special Section Editorial: Advances in Signal Processing for Non-Orthogonal Multiple Access

Orthogonal multiple access (OMA) technologies have prevailed from the first-generation (1G) to the fourth-generation (4G) of modern mobile communications, primarily because of their low complexity. The key idea of OMA is to ensure that the communication resources allocated to different users are orthogonal in at least one radio resource dimension. As a result, the number of active users allowed to access the OMA system is strictly limited by the number of available orthogonal resources, which becomes less effective for supporting massive connectivity and achieving user fairness. In contrast to OMA, non-orthogonal multiple access (NOMA) simultaneously accommodates a multitude of users using the same radio resource block via superposition signaling and employs various transmit or receive signal processing techniques to mitigate the resulting interference. However, the success of NOMA technologies relies heavily on the implementation of advanced signal processing techniques for transceivers, which may introduce large processing delays and increase computational complexity. Thanks to recent progress in hardware and theory in signal processing and machine learning, high signal processing complexity has become more affordable and processing latency can be significantly reduced, which enables the development of NOMA. Thus, sophisticated signal processing algorithms for multi-user detection, scheduling, and interference management are indispensable for the successful implementation of NOMA in the next-generation wireless systems.

Energy-Efficient Resource Allocation in Single-RF Load-Modulated Massive MIMO HetNets

Due to the dramatic increase in wireless data traffic and the associated increase in energy consumption, designing energy-efficient wireless networks with improved spectral efficiency is a pressing concern. The focus of this article is the design of a green, highly energy-efficient cellular heterogeneous network (HetNet) by taking advantage of multiple-input-multiple-output (MIMO) structure and deployment of small cells. We consider the downlink of a two-tier HetNet, in which multiple-antenna small cells are coordinated to serve users. Even though the deployment of MIMO together with small cells improves the communication system's performance in terms of data rate and reliability, circuit energy consumption in such a network is a critical issue. To address this, an energy-efficient antenna selection and radio resource block assignment algorithm is proposed for the small cells, and a single radio-frequency (RF) chain structure is considered for the massive MIMO macro base station. Then, while coordinating transmissions between cells subject to user-centric clustering, an energy-efficient beamforming design and power allocation optimization problem with respect to the quality of service requirement of users, transmit power budget of base stations, and fronthaul capacity is formulated; the problem is solved using the Dinkelbach method. Simulation results demonstrate the performance potential of our proposed algorithm in terms of energy efficiency and spectral efficiency.

Joint Radio Resource Allocation and Content Caching in Heterogeneous Virtualized Wireless Networks

An efficient content caching policy at the edge of the mobile cellular network can improve the quality of services of the mobile users and reduces network congestion at the backhaul. On the other hand, the wireless network virtualization emerges as a cutting-edge technique to address the limited network capacity problem due to the exponential growth of the mobile data traffic. In addition, the wireless network virtualization can bring huge benefits such as reducing the capital expenditure (CAPEX) and the operational expenditure (OPEX) as well as improving the network capacity. In this regard, one of the key requirements to recognize the benefits of the above mentioned two technologies is to have an applicable resource allocation framework, that enables the deployment of the content caching schemes in the virtualized wireless network. In this study, we investigate a novel joint radio resource allocation and content caching problem to efficiently utilize the radio resource blocks, the transmit power, and the available cache storage at the base stations (BSs). The goal of the formulated problem presented in this paper focuses on minimizing the delays experienced by the end mobile users of mobile virtual network operators (MVNOs). We show that the formulated problem is a non-convex, mixed integer non-linear problem (MINLP), which is NP-hard and simply intractable. Therefore, we deploy the block upper-bound minimization (BSUM) algorithm to solve the formulated problem. Numerical results show that our method outperforms the existing baseline resource allocation schemes, with up to 19% performance gain in terms of network delay.

Device‐centric communication in IoT: An energy efficiency perspective

AbstractEmerging device‐centric communication technologies such as device‐to‐device (D2D) communication, devices‐to‐device (Ds2D) communication, and multi‐homing (MH) D2D have been considered as essential parts of future fifth‐generation networks as well as Internet of Things (IoT). The device‐centric communication offers enhanced cellular data rates, high spectral efficiency, reduced latency, improved fairness, better energy efficiency, and extended coverage; however, the battery life of end devices is crucial to fully reap the benefits of this technology. In this article, a new method for device‐centric communication in IoT system is introduced, where multiple source IoT devices (SIDs) can send data to multiple destination IoT devices (DIDs) using multiple radio resource blocks. This method is called devices‐to‐devices (Ds2Ds) communication. The objective is to select the optimal SIDs, DIDs, and radio resource blocks such that the total energy efficiency for all DIDs is maximized. A tree search algorithm is proposed to select the optimal SIDs, DIDs, and radio resource blocks. Extensive simulations have been carried out to compare energy efficiency per SID/DID for Ds2Ds, Ds2D, and MH‐D2D. The simulation results show the superiority of Ds2Ds over Ds2D in terms of energy efficiency, which, in turn, implies better throughput. Furthermore, Ds2Ds is superior to MH‐D2D in terms of energy consumption per source device, a very good and promising requirement for green communication.