Resource Block Allocation Research Articles

Toward Optimal Cost-Energy Management Green Framework for Sustainable Future Wireless Networks

The design of green cellular networking according to the traffic arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective way. The cell zooming approach has appealed much attention that adaptively offloads the BS load demands adjusting the transmit power based on the traffic intensity and green energy availability. Besides, the researchers are focused on implementing renewable energy resources, which are considered the most attractive practices in designing energy-efficient wireless networks over the long term in a cost-efficient way in the existing infrastructure. The utilization of available solar can be adapted to acquire cost-effective and reliable power supply to the BSs, especially that sunlight is free, available everywhere, and a good alternative energy option for the remote areas. Nevertheless, planning a photovoltaic scheme necessitates viability assessment to avoid poor power supply, particularly for BSs. Therefore, cellular operators need to consider both technical and economic factors before the implementation of solar-powered BSs. This paper proposed the user-centric cell zooming policy of solar-powered cellular base stations taking into account the optimal technical criteria obtained by the HOMER software tool. The results have shown that the proposed system can provide operational expenditure (OPEX) savings of up to 47%. In addition, the efficient allocation of resource blocks (RBs) under the cell zooming technique attain remarkable energy-saving performance yielding up to 27%.

Neural-Network-Based Nonlinear Self- Interference Cancelation Scheme for Mobile Stations With Dual-Connectivity

Dual-connectivity technology enables a base station to assign multiple carriers from various bands to a mobile station (MS), thus increasing its bandwidth and data rate. However, when the downlink frequency assigned to the MS is approximately twice its uplink frequency, the MS’s receiver will be seriously interfered by the nonlinear self-interference from its own transmitter. This paper addresses the problem of nonlinear self-interference cancelation for MSs operating in the dual-connectivity mode. Compared with conventional systems, this scenario faces some new challenges because of the wide variety of nonlinear interference components, including not only harmonics but also intermodulation products, and the more complicated interference channels, including both nonlinear and linear devices. In addition, the frequency, bandwidth and frequency-selective channel parameters of the interference are influenced by the uplink resource block allocation. To solve these problems, a two-part nonlinear self-interference canceler is proposed, where one part is designed as a neural network to capture the nonlinear characteristics, and the other part is designed as a linear filter to capture the linear characteristics. Furthermore, a low-complexity two-step training scheme is proposed to approximate the interference channel in the entire system bandwidth. Finally, a hardware prototype is implemented to verify the effectiveness of the proposed scheme. The experimental results show that the proposed scheme achieves more than 20 dB interference cancelation, and significantly outperforms the conventional polynomial-based and pure neural-network cancelation schemes.

Energy Efficient Throughput Aware Traffic Load Balancing in Green Cellular Networks

With the augmentation of affordable multimedia wireless gadgets, the ubiquitous availability of the internet access, and the rapid pace of mobile traffic motivate research towards fifth generation (5G) communications to realize energy-efficient cloud radio access networks (C-RAN) with guaranteed quality of experience. Exploiting green energy harvesting for powering the C-RAN substantially alleviates the energy procurement from the utility grid, carbon footprint, and operational expenses. In this paper, we propose a new dynamic point selection coordinated multipoint (DPS CoMP) based load balancing paradigm emphasizing achievable throughput and energy efficiency (EE) by reducing utility grid consumption from a network level perspective. This paper investigates the radio efficiency, EE, and average on-grid energy saving addressing the key challenges of tempo-spatial dynamics of traffic intensity and renewable energy (RE) generation under a wide range of network setup. Endeavoring load balancing technique strives a balance in network utilities such as green energy utilization and user association based on BS coordination technique in a cluster. Provision of cell sleep approach is contemplated for further energy saving by turning off lightly loaded base stations (BSs) during low traffic arrivals. The proposed CoMP based load balancing algorithm proficiently manages resource block allocation to the new users and elevated the energy efficiency over the conventional location and traffic centric mechanisms. Extensive system-level simulations manifest that the suggested framework enables an adjustable trade-off between radio efficiency and EE, and saves 22% on-grid power consumption and increases EE index by 32%. Afterward, an exhaustive comparison of the proposed method with the existing schemes is pledged for further validation highlighting sustainable 5G wireless systems.

Greedy Based Radio Resource Allocation Algorithm with Sectoring Scheme in D2D Underlaying Communication

D2D communication is a communication that allows users to communicate without passing through the Base Station (BS). The D2D Underlaying communication system can use the same Resource Block (RB) as the Cellular User Equipment (CUE). Implementing this communication system can ease the burden on BS and can transmit data with higher throughput in low power. However, using RB simultaneously can cause interference and therefore an RB allocation scheme is needed. The algorithm used in the scheduling process of the uplink RB owned by CUE to the D2D pair is the joint greedy algorithm with sectoring scheme. This work used a scenario where the value of the D2D pair are varied. The parameters measured in this simulation were sum-rate, spectral efficiency and fairness. The simulation results show that the joint greedy with sectoring allocation scheme has good energy efficiency and spectral efficiency values of 6.063× 106 bps/watt and 16.982 bps/Hz. On the other hand, the D2D fairness value in the joint greedy with sectoring allocation scheme is 0.886.

Convergence Time Optimization for Federated Learning Over Wireless Networks

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL training loss and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that can select the users who can contribute toward improving the FL convergence speed more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time and the FL training loss. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on the global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to improve the global model, the FL convergence speed, and the training loss. Simulation results show that the proposed approach can reduce the FL convergence time by up to 56% and improve the accuracy of identifying handwritten digits by up to 3%, compared to a standard FL algorithm.

Joint optimization on resource allocation with coordinated scheduling-based transmission for interference handling in Femtocell networks

Background: The most challenging problem in femtocell networks is alleviating the Inter-Cell Interference (ICI) between the macro and femtocells while broadcasting the video streams in the Heterogeneous Networks (HetNets). It degrades the spectral efficacy and throughput significantly. To alleviate this problem, a Joint dynamic Layer, Channel and Power assignment with Further enhanced ICI Coordination (JLCP-FeICIC) method was recommended to assign the resources within every User Equipments (UEs) and enhance the throughput in a two-tier HetNets. Nonetheless, it schedules only the Cell Range Expansion (CRE) UEs of femtocells while the center UEs are not assigned and so the fairness of Resource Block (RB) allocation is reduced. Objective: Therefore in this study, JLCP-FeICIC is further enhanced with the Coordinated Multipoint Transmission (CMT) method (JLCP-FeICIC-CMT) for maximizing the efficiency of the HetNetcenter UEs. Methods: In this method, RBs are assigned by a primary scheduler. Also, the UE is detected in every eNodeBs (eNBs) according to the determination of the interference from the adjacent cells. Then, the Modulation and Coding Scheme (MCS) level is selected in all eNBs for UEs with and without the support of CMT according to the interference-free RB allocation. So, a higher level of spectral efficiency is achieved for corresponding RBs. Findings: Finally, the experimental results exhibit the JLCP-FeICIC-CMT achieves an increased efficacy than the JLCP-FeICIC.The efficiency is analyzed based on the average of Peak Singal-to-Noise Ratio (PSNR) per Femtocell UE (FUE), utility, monetary cost, and Playback Interruption Rate (PIR). Keywords: Femtocell network; HetNet; JLCPFeICIC; coordinated multipoint transmission; modulation and coding scheme

5G network-oriented hierarchical distributed cloud computing system resource optimization scheduling and allocation

As the core technology of the next generation mobile communication system, the development of 5G key technologies needs to be able to efficiently and effectively support massive data services. Aiming at the impact of massive data traffic on mobile communication networks in 5G communication systems, this paper proposes a 5G-oriented hierarchical distributed cloud service mobile communication system architecture. The model consists of a cloud access layer, a distributed micro-cloud system, and a core cloud data center. The distributed micro cloud system consists of multiple micro clouds that are deployed to the edge of the network. The service content in the core cloud data center can be deployed and cached to the local micro cloud server in advance to reduce repeated redundant transmission of user requested content in the network. Aiming at the problem of how to determine the migration object when dynamically optimizing the resource structure, a heuristic function-based dynamic optimization algorithm for cloud resources is proposed. The experimental results show that the dynamic expansion algorithm of cloud resources based on dynamic programming ideas can better improve the performance of virtual resources, and the dynamic optimization algorithm of cloud resources based on heuristic functions can effectively and quickly optimize the resource structure, thereby improving the operating efficiency of user virtual machine groups. An efficient resource allocation scheme based on cooperative Q (Quality) learning is proposed. The environmental knowledge obtained by the base station learning and exchanging information is used for distributed resource block allocation. This resource allocation scheme can obtain the optimal resource allocation strategy in a short learning time, and can terminate the learning process at any time according to the delay requirements of different services. Compared with traditional resource allocation schemes, it can effectively improve system throughput.

A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

In this article, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that generates a global FL model and sends the model back to the users. Since all training parameters are transmitted over wireless links, the quality of training is affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS needs to select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To seek the solution, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can improve the identification accuracy by up to 1.4%, 3.5% and 4.1%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation, 2) a standard FL algorithm with random user selection and resource allocation, and 3) a wireless optimization algorithm that minimizes the sum packet error rates of all users while being agnostic to the FL parameters.

Energy-Efficient Resource Allocation With Flexible Frame Structure for Hybrid eMBB and URLLC Services

The key objective of the fifth generation (5G) wireless technology is to support services with highly diverse requirements, which necessitates the specification of flexible numerology and frame structure for radio resource allocation in 5G new radio (NR). In this article, flexible 2-dimensional resource allocation is investigated to exploit the frequency and time diversities of the resource grid, and eventually maximize the overall system energy efficiency (EE) in the scenario where enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC) services coexist. A new definition of EE is proposed to capture and fulfill the heterogeneous quality-of-service (QoS) requirements of eMBB and URLLC services. By first sliding a window on the resource grid to determine the candidate resource pool, and then allocating resource blocks (RBs) therein by leveraging linear programming (LP) relaxation, we propose a sliding window (SW) based algorithm with a low complexity for the NP-hard EE optimization problem. In addition, we also analyze the achievable EE performance of the proposed algorithm to demonstrate its scalability with respect to the grid sizes and the maximum transmit power to provide an in-depth understanding. Numerical results show that the proposed algorithm can bring about as much as 16.7% power consumption reduction, and improve EE by about 29.3% compared to other baseline systems.

RAPTAP: a socio-inspired approach to resource allocation and interference management in dense small cells

Femto Cells offer higher data rates to users within closed spaces. Dense deployment of small cells is a characteristic of pre-5G/LTE-Advanced Pro (LTE-A Pro) networks and is a precursor to the future 5G cellular networks. Combined with a Frequency Reuse Factor (FRF) of one, the dense small cell systems result in high co-tier interference which is undesirable. High interference Optimized scheduling decisions and interference management techniques are required to guarantee certain minimum data rates to the users at the cell-edge and increase the overall throughput of the system. This work presents a centralized scheduling approach that mitigates the detrimental impact of interference, thereby maximizing the overall throughput of the system. In doing so, we make use of concepts from Social theory and incorporate these ideas in the solution design. The centralized approach uses optimized scheduling algorithm (RAPTA) which takes feedback from the indoor users of all Femtos as the input and formulates a Mixed Integer Non-Linear Programming (MINLP) problem. Thereafter, the MINLP problem is relaxed and its solution carries out the Resource Block allocation and ensures optimal power transmission for every allocated resource block in all Femtos. We implement the proposed OPT algorithm on top of Proportional Fair (PF) in the Vienna Simulator. Since the RAPTA is NP-Hard and takes a considerably long time to solve the MINLP problem, we derive from it a polynomial time Heuristic algorithm (RAPTAP) which performs Resource Block allocation and sub-optimal Power transmission quite close to the RAPTA algorithm in terms of performance. RAPTAP is a two-stage approach each of which is inspired by ideas from two different strands of Sociological theory. We demonstrate through experiments that the proposed RAPTA + PF achieves 60.67% improvement in the services offered to the mobile users when compared to the classic PF algorithm with Soft Fractional Frequency Reuse (SFFR) interference management technique in the built environment. The Socio-inspired RAPTAP performs almost as well as the RAPTA + PF algorithm, with only a marginal 4% drop in the overall system throughput as compared to the latter. Further, we evaluate the RAPTAP heuristic in scenarios involving mobile users and demonstrate 14.52% improvement when compared to classic PF algorithm with Fractional Frequency Reuse-Full Isolation (FFR-FI) interference management. Finally, we compare the proposed Socio-inspired RAPTAP + PF with two state-of-the-art algorithms, viz., a Genetic Algorithm approach to resource allocation (NSGA-UDN) and a recent work on LTE-Unlicensed (LTE-U) + PF. Proposed Socio-inspired solution outperforms the LTE-U + PF and NSGA-UDN by 28.26% and 31.41%, respectively, in terms of throughput.

RESOURCE ALLOCATION FOR LOW-POWER DEVICES OF M2M TECHNOLOGY IN 5G NETWORKS

Background. One of the key tasks in 5G networks is the resource allocation for machine communication devices (MCD), since they affect the battery life of devices and the quality of service for applications. The MCD system usually consists of small devices and sensors. MCD cannot afford high energy consumption, as their battery life is limited due to the difficulty of replacing or charging them.Objective. The purpose of the work is to increase energy efficiency while increasing the autonomous distribution resource on MCD. To achieve this goal, the task is allotted to create a resource allocation scheme using a controlled power limit of M2M small devices of 5G communication system.Methods. The article proposes a new resource allocation algorithm that uses traffic capacity controlled access. This is an uplink resource allocation technique in which devices decide to allocate resource blocks based on their priority and power level of the corresponding application.Results. The method consists of two stages. At the first stage, the number of carriers that should be allocated for a certain MCD device with low energy consumption is selected in order to increase its service life. At the second stage, an effective solution is implemented by introducing a traffic capacity value. A specific traffic capacity value is selected based on the QoS metric. This traffic capacity improves carrier selection for less powerful devices.Conclusions. The presented method is used for the physical layer of a 5G network. The results obtained show that the proposed algorithm is simpler and achieves better performance compared to existing ones, as it provides shorter delay times, greater energy efficiency and greater traffic capacity.

Deep-Reinforcement-Learning-Based Proportional Fair Scheduling Control Scheme for Underlay D2D Communication

In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-enabled devices in different applications starting from e-healthcare, transportation, agriculture, etc., across the globe. These interconnected devices share information using the Internet to improve the Quality of Service of the end users. There is a requirement of synchronization among the devices to provide scalability, reliability, and connectivity. Despite these advantages, proximity gain, interference, and fairness are various challenges for these devices in IoT which need to be resolved. To overcome these issues, we propose deep reinforcement learning (DRL)-based control scheme in the underlay of device-to-device (D2D) communication. D2D communication reuses the spectrum resources with cellular user equipment (CUE) to improve spectral efficiency. We propose the joint resource block (RB) scheduling and power control scheme to improve the sum rate of the network while considering the users' fairness among all the links. To solve this problem, first, we transform the nonconvex optimization problem into a multiagent reinforcement learning formulation using the Markov decision process (MDP). Then, to solve the RB allocation, we used the multiagent deep Q-network (DQN) framework to reduce the output dimension and improve the learning efficiency. Then, to convert the stochastic policy into deterministic policy, and to improve the fairness we combine the DQN with deep deterministic policy gradient to form the distributed deep deterministic policy gradient (DDDPG) scheme. Finally, to control the power of both the CUEs and D2D transmitters (DTs), we integrated the conventional optimization scheme with the DDDPG (CO-DDDPG). This combination enhances the convergence speed and reduces the computational complexity of the overall network. Numerical results show that the proposed scheme improves the network sum rate of 11.76% and the fairness 4.21% as compared to the state-of-the-art existing distributed DRL schemes.

Joint resource block and power allocation in heterogeneous cellular networks with different backhaul capacity limitations

AbstractIn heterogeneous cellular networks (HCNs), the growing demand of small cell data traffic puts great pressure on the capacity limited backhaul links. Resource allocation is an efficient approach to mitigate the severe interference and improve the network performance in HCNs. However, differentiated backhaul capacity constraints introduce the inherent nonconvexity to the resource optimization problem, which hinders the development of the optimal solutions. In this article, we investigate the joint resource block (RB) and power allocation problem under two different backhaul capacity limitations. First, we consider the small cell backhaul limitation scenario where the backhaul capacity constraint is imposed on each small cell. Then, we extend the resource optimization to the macrocell base station backhaul limitation scenario which considers the overall backhaul capacity constraint on the whole small cell network. Besides, the quality of service (QoS) requirements of users and the cross‐tier interference mitigation are also considered. This resource allocation problem is formulated as a nonconvex mixed‐integer nonlinear programming (MINLP) problem. By variable substitution and constraints relaxation, we convert the original optimization problem into a convex one and obtain the optimal solutions. Accordingly, joint RB and power allocation algorithms are proposed. Numerical results verify the effectiveness of the proposed algorithms and evaluate the impact of different types of backhaul capacity limitations on system performance.

Network Slicing Enabled Resource Management for Service-Oriented Ultra-Reliable and Low-Latency Vehicular Networks

Network slicing has been considered as a promising candidate to provide customized services for vehicular applications that have extremely high requirements of latency and reliability. However, the high mobility of vehicles poses significant challenges to resource management in such a stochastic vehicular environment with time-varying service demands. In this paper, we develop an online network slicing scheduling strategy for joint resource block (RB) allocation and power control in vehicular networks. The long-term time-averaged total system capacity is maximized while guaranteeing strict ultra-reliable and low-latency requirements of vehicle communication links, subject to stability constraints of task queues. The formulated problem is a mixed integer nonlinear stochastic optimization problem, which is decoupled into three subproblems by leveraging Lyapunov optimization. In order to tackle this problem, we propose an online algorithm, namely JRPSV, to obtain the optimal RB allocation and power control at each time slot according to the current network state. Furthermore, rigorous theoretical analysis is conducted for the proposed JRPSV algorithm, indicating that the system capacity and the system average latency obey a $[ {{\mathcal O}({1/V}),{\mathcal O}(V)} ]$ trade-off with the control parameter $V$ . Extensive simulation results are provided to validate the theoretical analysis and demonstrate the effectiveness of JRPSV as well as the impacts of various parameters.

URJA: Usage Jammer as a Resource Allocation for Secure Transmission in a CR-NOMA-Based 5G Femtocell System

In this article, a joint user pairing and secure resource allocation scheme for cognitive radio inspired nonorthogonal multiple access (CR-NOMA) femtocells in the presence of a jammer base station (JBS) is proposed. In the considered CR-NOMA-based femtocells, femto base stations (FBSs) serve their femto mobile station (FMS) by sharing the resource block (RB) with macromobile stations (MMSs), while the JBS in each femtocell secures the data rate of a weak user from the eavesdropping capability of the strong FMS. The strong FMS intercepts the data rate of the weak FMS using a successive interference cancellation technique to decode their own data as per the NOMA principle. The weak FMS protects its data from the strong eavesdroppers through the JBS. The JBS generates artificial noise to confuse the strong eavesdroppers. The problem of the joint user pairing and resource allocation is formulated as an optimization problem, whose main goal is to maximize the secure sum rate of the FMS while guaranteeing the secure data rate of the weak FMS and MMS. For RB allocation, CR at the FBS senses the best RB for its FMS using the Greedy approach. To optimize the power of the FBS and JBS, a successive convex approximation scheme with a low complexity is applied. Simulation results show that our proposed scheme provides an improvement in the secure sum rate of the FMS in comparison to the conventional NOMA and preexisting orthogonal multiple access schemes.

Joint resource block and power allocation through distributed learning for energy efficient underlay D2D communication with rate guarantee

In this paper, we propose a novel distributed energy efficient joint resource block (RB) and discrete transmit power allocation scheme for an underlay device-to-device (D2D) network. In addition to minimizing the total transmit power of the D2D tier, the proposed scheme also ensures the rate constraints for all the cellular user equipments (CUEs) and D2D pairs. D2D pairs learn the RB and transmit power to be used by playing a game in Satisfaction Form (SF). The Efficient Satisfaction Equilibrium (ESE) of the game is shown to be the RB and discrete transmit power allocation with the lowest aggregate transmit power, in the case when the rate requirements of all the D2D pairs can be satisfied with non-zero transmit powers; else, the game is shown to converge to the K-Person Satisfaction Point (K-PSP), where the largest subset of D2D pairs are satisfied. The base station (BS) then decides to reject or accommodate the D2D pairs to ensure the rate constraints of the CUEs. The proposed scheme is established to be equivalent to the deferred acceptance (DA) algorithm applied to a many-to-one matching game. We exploit this equivalence to investigate the stability and optimality of the solution. The performance of the proposed scheme is evaluated and compared with existing schemes in the literature.

Experienced Deep Reinforcement Learning With Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication

In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC-6G) in the downlink of a wireless network. The goal is to guarantee high end-to-end reliability and low end-to-end latency, under explicit data rate constraints, for each wireless user without any models of or assumptions on the users' traffic. In particular, in order to enable the deep-RL framework to account for extreme network conditions and operate in highly reliable systems, a new approach based on generative adversarial networks (GANs) is proposed. This GAN approach is used to pre-train the deep-RL framework using a mix of real and synthetic data, thus creating an experienced deep-RL framework that has been exposed to a broad range of network conditions. The proposed deep-RL framework is particularly applied to a multi-user orthogonal frequency division multiple access (OFDMA) resource allocation system. Formally, this URLLC-6G resource allocation problem in OFDMA systems is posed as a power minimization problem under reliability, latency, and rate constraints. To solve this problem using experienced deep-RL, first, the rate of each user is determined. Then, these rates are mapped to the resource block and power allocation vectors of the studied wireless system. Finally, the end-to-end reliability and latency of each user are used as feedback to the deep-RL framework. It is then shown that at the fixed-point of the deep-RL algorithm, the reliability and latency of the users are near-optimal. Moreover, for the proposed GAN approach, a theoretical limit for the generator output is analytically derived. Simulation results show how the proposed approach can achieve near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements. The results also show that the proposed experienced deep-RL framework is able to remove the transient training time that makes conventional deep-RL methods unsuitable for URLLC-6G. Moreover, during extreme conditions, it is shown that the proposed, experienced deep-RL agent can recover instantly while a conventional deep-RL agent takes several epochs to adapt to new extreme conditions.

Uplink Resource Allocation in Energy Harvesting Cellular Network With H2H/M2M Coexistence

We consider uplink transmission using a single carrier frequency division multiple access (SC-FDMA) scheme in a cellular network with human-to-human (H2H) and machine-to-machine (M2M) communications. The M2M traffic is relayed through energy harvesting (EH) gateways. An optimization framework is developed to minimize the data dropping in the EH gateways caused by delay constraint violation of the M2M traffic while taking into account the rate requirements of the H2H communication, the data and energy causality constraints as well as the SC-FDMA transmission constraints. By introducing two transforms, the original problem is expressed in a linearly separable form in terms of its discrete and continuous variables with the convexified continuous part. Then, Generalized Benders Decomposition is applied to solve the problem by decomposing it into primal and master problems. Due to the NP-hardness of the optimal solution, a low-complexity method is proposed by combining the solution of the primal problem with a heuristic resource block allocation algorithm. Simulation results show that the proposed heuristic method performs better than two alternative heuristic methods when applied to small scale and large scale networks.

An Efficient Resource Allocation for Massive MTC in NOMA-OFDMA Based Cellular Networks

To alleviate random access congestion and support massive-connections with less energy consumption for machine-type communications (MTC) in the 5G cellular network, we propose an efficient resource allocation for massive MTC (mMTC) with hybrid non-orthogonal multiple access (NOMA)-orthogonal frequency division multiple access (OFDMA). First, a hybrid multiple access scheme, including the NOMA-based congestion-alleviating access scheme (NCAS) and OFDMA-based congestion-alleviating access scheme (OCAS), is proposed, in which the NOMA based devices coexist with OFDMA based ones. Then, aiming at maximizing the system access capacity, a traffic-aware resource blocks (RBs) allocation is investigated to optimize RBs allocation for preamble transmission and data packets transmission, as well as to optimize the RBs allocation between NCAS and OCAS for the RBs usage efficiency improvement. Next, aiming at the problem of high computational complexity and improving energy efficiency in hybrid NOMA-OFDMA based cellular M2M communications, this paper proposes an improved low complexity power allocation algorithm. The feasibility conditions of power allocation solution under the maximum transmit power constraints and quality of service (QoS) requirements of the devices is investigated. The original non-convex optimization problem is solved under the feasibility conditions by two iterative algorithms. Finally, a device clustering scheme is proposed based on the channel gain difference and feasible condition of power allocation solution, by which NOMA based devices and OFDMA based devices can be determined. Simulation results show that compared with non-orthogonal random access and transmission (NORA-DT), the proposed resource allocation scheme for hybrid NOMA-OFDMA systems can efficiently improve the performance of access capacity and energy efficiency.

Joint Task Offloading and QoS-Aware Resource Allocation in Fog-Enabled Internet-of-Things Networks

Fog computing is an advanced technique to enhance the Quality of Service (QoS), decrease network latency and energy consumption for Internet-of-Things devices (IDs). In this article, to minimize the overhead of the fog computing network, including the task process delay and energy consumption, while ensuring multiply QoS requirements of different types of IDs, we propose a QoS-aware resource allocation scheme, which jointly considers the association between fog nodes (FNs) and IDs, transmission and computing resource allocation to optimize the offloading decisions while minimizing the network overhead. First, an analytic hierarchy process-based evaluation framework is established to find the preference of QoS parameters and the priority of different types of ID tasks. Second, we introduce a resource block (RB) allocation algorithm to allocate RBs to IDs based on the IDs priority, satisfaction degree, and the quality of RBs. Moreover, a QoS-aware bilateral matching game is introduced to optimize the association between FNs and IDs. Finally, the offloading decisions are based on the previous steps to minimize the network overhead. The simulation results demonstrate that the proposed scheme could efficiently ensure the loading balance of the network, improve the RB utilization, and reduce the network overhead.