Subchannel Allocation Scheme Research Articles

Energy-Efficient Resource Allocation for Aggregated RF/VLC Systems

Visible light communication (VLC) is envisioned as a core component of future wireless communication networks due to, among others, the huge unlicensed bandwidth it offers and the fact that it does not cause any interference to existing radio frequency (RF) communication systems. In order to take advantage of both RF and VLC, most research on their coexistence has focused on hybrid designs where data transmission to any user could originate from either an RF or a VLC access point (AP). However, hybrid RF/VLC systems fail to exploit the distinct transmission characteristics (e.g., susceptibility of VLC transmissions to blockages, limited field-of-view of VLC APs and receivers, more coverage and better reliability of RF systems, etc.) of RF and VLC systems to fully reap the benefits they can offer. Aggregated RF/VLC systems, in which any user can be served simultaneously by both RF and VLC APs, have recently emerged as a more promising and robust design for the coexistence of RF and VLC systems. To this end, this paper, for the first time, investigates AP assignment, subchannel allocation (SA), and transmit power allocation (PA) to optimize the energy efficiency (EE) of aggregated RF/VLC systems while considering the effects of interference and VLC line-of-sight link blockages. A novel and challenging EE optimization problem is formulated for which an efficient joint solution based on alternating optimization is developed. More particularly, an energy-efficient AP assignment algorithm based on matching theory is proposed. Then, a low-complexity SA scheme that allocates subchannels to users based on their channel conditions is developed. Finally, an effective PA algorithm is presented by utilizing the quadratic transform approach and a multi-objective optimization framework. Extensive simulation results reveal that: 1) the proposed joint AP assignment, SA, and PA solution obtains significant EE, sum-rate, and outage performance gains with low complexity, and 2) the aggregated RF/VLC system provides considerable performance improvement compared to hybrid RF/VLC systems.

Wireless-Powered OFDMA-MEC Networks With Hybrid Active–Passive Communications

In this article, we propose a novel system model for a wireless powered mobile edge computing (MEC) network, where the Internet-of-Things (IoT) nodes perform partial offloading to the MEC server via hybrid backscatter communication (BackCom) and active radio (AR) following an OFDMA protocol, and maximize the system computation bits (SCB). For the case of the system having more subchannels than IoT nodes, we formulate the SCB maximization problem that requires the joint optimisation of the transmit power and time, subchannel allocation, computation frequency and time of the MEC server, as well as the IoT nodes’ BackCom time and reflection coefficients, transmit power and time for AR-based offloading, local computing time and frequencies, subject to the MEC server’s computation capacity and the quality-of-service (QoS) and energy-causality constraints of each IoT node. By applying the proof by contradiction and time sharing relaxation, we transform the formulated problem into a convex one and then solve it by using the existing convex tools. For the case of the system having less subchannels than IoT nodes, we propose a dynamic subchannel allocation scheme that allows each IoT node to choose one task-offloading mode from three modes: HAPR, BackCom only, and AR only, while ensuring that no more than one IoT node occupies a subchannel at any time. The SCB are maximized by first determining the subchannel allocation and mode selection of each IoT node and then optimising the remaining resource allocation for the MEC server and all IoT nodes under the obtained subchannel assignment and mode selection. Simulations validate the superior performance of the proposed schemes over several benchmark schemes from the SCB perspective.

User Association, Subchannel and Power Allocation in Space-Air-Ground Integrated Vehicular Network With Delay Constraints

The space-air-ground integrated vehicular networks (SAGVN) has significant advantages in satisfying the requirements of the Internet of Vehicles (IoV) business for wide area coverage and long-distance communications. However, the multi-dimensional nature of the SAGVN leads to diverse network resources, it is necessary to provide different services for various scenarios, which makes the problem of network resource allocation becomes extremely challenging. In this paper, we focus on the resource allocation of small cells in SAGVN. Specifically, the user association is considered to optimize connection between base stations and vehicles. On this basis, a subchannel and power allocation method is designed. The proposed subchannel allocation scheme ensures that users can obtain the maximum gain on its subchannel, and the Lagrangian duality theory is introduced to solve the power allocation problem. Meanwhile, the edge computing is applied to the SAGVN, and the vehicles can offload the local tasks to the edge server. In order to reduce the delay in the offloading process, taking the time delay as a constraint condition can further optimize the communication performance. The simulation results show that the proposed scheme can effectively improve the sum rate of small cells in SAGVN, and the performance is better than existing algorithms.

The Management of Educational Talents in Vocational Colleges Based on Wireless Network in the Artificial Intelligence Era.

Faced with the AI (artificial intelligence) era, it is both theoretically and practically important to examine the challenges and opportunities that vocational college education in China faces, as well as to actively explore how vocational college education can overcome the challenges and achieve a realistic path. This paper proposes and implements a wireless network-based vocational college talent management system. The main personnel management system primarily completes business operations related to daily personnel file management, while the data mining subsystem mines talent data using the DT (decision tree) classification algorithm to aid talent selection. At the same time, a new topology optimization algorithm based on the principle of minimum rigidity graph is proposed for talent management system wireless network optimization. The maximum-minimum balance criterion and the user-by-user optimization mechanism are designed to obtain the optimal relay selection and subchannel allocation strategy, ensuring the system's reliability and fairness. The optimized algorithm has a user security satisfaction of 0.93 in the range of 250 m, which is higher than other algorithms. It demonstrates that this algorithm's communication link is short, and it has good network connectivity and structural stability.

Throughput maximisation for multi‐channel energy harvesting cognitive radio networks with hybrid overlay/underlay transmission

This paper focuses on the issue of joint time and power allocation in multi-channel energy harvesting CR networks (EH-CRNs), where the multi-antenna secondary transmitter (ST) opportunistically accesses the licensed subchannels by a hybrid overlay/underlay transmission approach. To improve spectrum efficiency and energy efficiency of the EH-CRNs, the ST scavenges energy from the radio-frequency signal radiated by the primary transmitter, and exploits the harvested energy for data transmission through subchannels of different states in overlay/underlay mode simultaneously. Moreover, under the interference power constraint, energy constraint, and maximum power constraint, the secondary throughput is improved by optimising the allocation of subchannels, the time scheduling between energy harvesting and data transmission, and the power allocation of the ST among different subchannels. A subchannel allocation scheme with low time complexity is proposed, and the secondary throughput optimisation problem is formulated with respect to the time scheduling and power allocation of the ST. Then it is proved the problem is convex, and the problem is solved by a proposed joint time and power allocation algorithm. Numerical results show that the proposed scheme has an advantage of secondary throughput over the other schemes. Finally, the impacts of key relevant factors on the secondary throughput are explored.

Joint DL/UL Decoupled Cell-Association and Resource Allocation in D2D-Underlay HetNets

With the rise of data-hungry applications and the rapid development of the Internet-of-Things (IoT), 5G and device-to-device (D2D) communications come into being. While such technologies are designed to support massive data access to meet the ever-increasing demands, the complexity and heterogeneity of the network structure bring bigger challenges for interference management. As such, downlink/uplink decoupling (DUDe) has become an attractive approach, as it offloads user equipments (UEs) to small-cells (SCells) and creates a more benign environment for D2D communication. In this paper, we investigate the application of DUDe in D2D-underlay heterogeneous networks, and propose an efficient joint cell-association, subchannel allocation, and power control scheme for network sum-rate maximization. The proposed scheme includes a minimum path-loss based DUDe to decide the uplink (UL) serving base-station, and a greedy coloring scheme with modified Munkres algorithm to cluster the UEs, and allocate subchannels. Furthermore, the difference of convex functions based programming algorithm for power control is utilized to maximize the network sum-rate while satisfying the UE transmit power and data rate constraints. The results presented show that the proposed scheme can achieve superior performance to the coupled benchmarks.

Joint Task Offloading and Resource Allocation for Multi-Task Multi-Server NOMA-MEC Networks

By offloading computationally intensive tasks of smart end devices to edge servers deployed at the edge of the network, mobile edge computing (MEC) has become a promising technology to provide computing services for Internet of Things (IoT) devices. In order to further improve the access capability of MEC and increase the spectrum utilization efficiency, in this article, Non-Orthogonal Multiple Access (NOMA) technology is introduced into MEC systems and we study the computing offloading problem of multi-user, multi-task and multi-server through joint optimization of task offloading and resource allocation, we intend to maximize the system’s processing capability as an optimization goal. To solve the proposed mixed integer nonlinear programming (MINLP) problem, the objective optimization problem is firstly decoupled into two sub-problems of resource allocation and task allocation. Secondly the resource allocation problem is further decomposed into computation resource optimization and communication resource allocation. For the communication resource allocation, it first fixed power allocation, then the sub-channel allocation problem is regarded as a many-to-one matching problem between sub-channels and users. In addition, we propose a low-complexity sub-optimal matching algorithm for sub-channel allocation to maximize the offloading efficiency. Based on our proposed sub-channel allocation scheme, the transmission power allocation is regarded as a convex optimization problem, which is tackled by Lagrangian multiplier method. Finally, under the condition of resource allocation, the tasks of all end devices (EDs) are allocated. Experimental numerical results show that the proposed scheme can effectively decrease latency and energy consumption of networks, improve system processing capability, and further improve MEC system performance.

Joint Access Control and Resource Allocation for Short-Packet-Based mMTC in Status Update Systems

In this article, we investigate the performance of massive machine type communications (mMTC) in status update systems, where massive machine type communication devices (MTCDs) send status packets to the BS for system monitoring. However, massive MTCDs sending status packets to the BS will cause severe packet collisions, which will have a negative impact on status update performance. In this case, it is necessary to carry out reasonable access control and resource allocation scheme to improve the status update performance for mMTC. In this article, taking the features of mMTC into consideration, we first analyze access control, packet collisions and packet errors in mMTC respectively, and derive the closed-form expression of the average age of information for all MTCDs as the performance metric, and then propose a joint access control, frame division and subchannel allocation scheme to improve the overall status update performance. Simulation and numerical results verify the correctness of theoretical results and show that our proposed scheme can achieve almost the same performance as the exhaustive search method and outperforms benchmark schemes.

Subchannel and power allocation for D2D communication in mmWave cellular networks

This paper investigates resource allocation for device-to-device (D2D) communication in a millimeter wave (mmWave) cellular network where D2D users communicate in the cellular band. We formulate the optimization problem of subchannel and power allocation, which aims to maximize the sum rate of D2D transmitters while satisfying interference constraints at the base station (BS). Since the problem is NP-hard, solving it requires a huge amount of computation. Therefore we reduce its computational complexity by dividing it into two subproblems: a subchannel allocation problem and a power allocation problem. To solve the subchannel allocation problem, we propose two heuristic algorithms: the max greedy SNR scheme and the minimum interference scheme. The max greedy SNR scheme achieves higher sum rate and has lower computational complexity compared to the minimum interference scheme. However it requires global channel state information (CSI) of all nodes, which demands huge feedback overhead. On the other hand, the minimum interference scheme requires the location of each node. After subchannel allocation, the BS finds the optimal transmit power of each D2D transmitter by using the difference of convex (DC) programming. Simulation results verify that our proposed subchannel allocation schemes outperform the random subchannel allocation scheme, and the optimal power allocation results in the improved sum rate of D2D transmitters.

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

The mode selection and resource allocation in fog radio access networks (F-RANs) have been advocated as key techniques to improve spectral and energy efficiency. In this paper, we investigate the joint optimization of mode selection and resource allocation in uplink F-RANs, where both of the traditional user equipments (UEs) and fog UEs are served by constructed network slice instances. The concerned optimization is formulated as a mixed-integer programming problem, and both the orthogonal and multiplexed subchannel allocation strategies are proposed to guarantee the slice isolation. Motivated by the development of machine learning, two reinforcement learning based algorithms are developed to solve the original high complexity problem under traditional and fog UEs’ specific performance requirements. The basic idea of the proposals is to generate a good mode selection policy according to the immediate reward fed back by an environment. Simulation results validate the benefits of our proposed algorithms and show that a tradeoff between system power consumption and queue delay can be achieved.

Elite User Clustering-Based Indoor Heterogeneous VLC Interference Management and Sub-Channel Allocation Strategy

The visible light communication (VLC) and wireless fidelity (WIFI) heterogeneous network is considered as a highly competitive candidate for the next generation indoor wireless communication. VLC provides high data rate as downlink transmission while WIFI can be supplied to achieve uplink transmission and make up for link interruptions caused by occlusion. Due to the dense distribution of VLC access points (APs), data rate of users is reduced by inter-cell interference (ICI) seriously, therefore, interference management (IM) is crucial to ensure user satisfaction. Meanwhile, as the number of users increases, in order to ensure the needs of users, reasonable resource allocation is required in such heterogeneous network. Hence, the center of the elite user clustering attracts its non-interfering users to join a cluster that is a user with good channel quality and high demand. And users in one cluster share resources by sub-channel reuse. In consideration of interference management and resource allocation, we investigated resource allocation based on elite users clustering for interference management in heterogeneous network, which is formulated as a throughput maximization problem, and a graph-based IM is proposed to solve it. Compared with the benchmark 1 and benchmark 2 on a rectangular layout and an irregular layout, the simulation results demonstrate that the throughput of the proposed elite user clustering algorithm is increased by 19.5% and 12%, the average user satisfaction ratio is increased by 14% and 9%, respectively.

Performance Analysis of Cooperative NOMA Systems With Adaptive Mode Selection and Subchannel Allocation

In this paper, we study the mode selection and subchannel allocation for the downlink of a multi-user multi-subchannel cooperative non-orthogonal multiple access (C-NOMA) system. Specifically, each non-orthogonal multiple access (NOMA) pair can work in two different modes, i.e., non-cooperative NOMA (NC-NOMA) and C-NOMA, where the mode selection is based on the users’ channel conditions. We propose an adaptive mode selection, subchannel allocation and user pairing (AMS-SAUP) scheme and derive the achievable diversity order by analyzing the upper bound for the outage probability. Simulation results reveal that the AMS-SAUP scheme achieves the same diversity order as the exhaustive search scheme.

Cluster-Based Joint Resource Allocation with Successive Interference Cancellation for Ultra-Dense Networks

Due to the random deployment and the decreasing coverage of numerous base stations (BSs), the interference, including co-tier interference and cross-tier interference, is increasingly severe in ultra-dense networks (UDNs), which has negative effects on the communication quality. Considering all types of interference, we propose an interference management scheme aiming at maximizing the system capacity by jointly interference graph based clustering algorithm, suboptimal heuristic algorithm and ordered successive interference cancellation (OSIC) detection algorithm for UDNs. The analysis and simulations show that the average capacity and spectral efficiency of the proposed scheme have been improved compared with the optimal femto base stations subchannel allocation (OFBSSA) scheme and cluster-based FBS subchannal allocation (CFBSSA) scheme. The results also verify that the proposed scheme outperforms these two schemes in the following network scenarios: plenty of users located in overlapping region, a great many FBSs located in overlapping region, numerous FBSs and a large amount of users with random distributions, and it owns preponderances in meeting the requirements of 5G communication.

Robust Max–Min Fairness Resource Allocation in Sensing-Based Wideband Cognitive Radio With SWIPT: Imperfect Channel Sensing

Fairness among different users and energy utilization are key issues in the future communication network design. Robust max-min fairness resource allocation in sensing-based wideband cognitive radio with simultaneous wireless information and power transfer is studied when spectrum sensing and channel state information are imperfect. A worst-case throughput is maximized by jointly optimizing the sensing time, transmit power, and subchannel allocation under the worst-case channel state information error model, subject to constraints on energy harvesting, interference power, and transmit power. Two operation paradigms for cognitive radio are considered, namely, opportunistic spectrum access and sensing-based spectrum sharing. The formulated robust max-min fairness resource allocation problems are mixed-integer and nonconvex programming with infinite inequality constraints. An efficient one-dimensional search algorithm is designed based on the proposed transmit power and subchannel allocation scheme. Simulation results show that the secondary user under sensing-based spectrum sharing can obtain a performance gain compared with that under opportunistic spectrum access at the cost of implementation complexity. Design tradeoffs are identified and discussed.

Joint spectrum allocation and energy harvesting optimization in green powered heterogeneous cognitive radio networks

We aim at maximizing the sum rate of secondary users (SUs) in OFDM-based Heterogeneous Cognitive Radio (CR) Networks using RF energy harvesting. Assuming SUs operate in a time switching fashion, each time slot is partitioned into three non-overlapping parts devoted for energy harvesting, spectrum sensing and data transmission. The general problem of joint resource allocation and structure optimization is formulated as a Mixed Integer Nonlinear Programming task which is NP-hard and intractable. Thus, we propose to tackle it by decomposing it into two subproblems. We first propose a sub-channel allocation scheme to approximately satisfy SUs’ rate requirements and remove the integer constraints. For the second step, we prove that the general optimization problem is reduced to a convex optimization task. Considering the trade-off among fractions of each time slot, we focus on optimizing the time slot structures of SUs that maximize the total throughput while guaranteeing the rate requirements of both real-time and non-real-time SUs. Since the reduced optimization problem does not have a simple closed-form solution, we thus propose a near optimal closed-form solution by utilizing Lambert-W function. We also exploit iterative gradient method based on Lagrangian dual decomposition to achieve near optimal solutions. Simulation results are presented to validate the optimality of the proposed schemes.

Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation

In this paper, we study the utility fairness resource allocation in a multi-user orthogonal-based cognitive radio network with cochannel interference (CCI) mitigation. In our proposed system model, we introduce the correct reception probability (CRP) model as a network utility metric. Furthermore, useful bounds on CRP are derived to analyze the performance of proposed allocation schemes. The optimal resource allocation is formulated as a worst-case user CRP maximum problem with both average CCI and average power budget constraints. However, this problem is non-convex and generally challenging to solve. Therefore, we solve this problem by successively performing subchannel allocation and power allocation. Firstly, a $k$ -means clustering inspired subchannel allocation strategy is proposed to divide secondary users (SUs) into multiple groups by minimizing the average mutual-signal-to-interference-ratio degree between any two SUs. The concept of reference user is employed to guarantee the quality of service of the primary user. In each subchannel, we formulate a max-min utility optimal power allocation problem. The non-linear Perron Frobenius theory is applied to solve this power allocation problem. Simulation results show that the proposed resource allocation scheme is fair and has relatively fast convergence.

Resource Allocation for Cellular-based Inter-Vehicle Communications in Autonomous Multiplatoons

With the significant population growth in megacities everywhere, traffic congestion is becoming a severe impediment, leading to long travel delays and large economic loss on a global scale. Platooning is a promising intelligent transportation framework that can improve road capacity, on-road safety, and fuel efficiency. Furthermore, enabling inter-vehicle communications within a platoon and among platoons (in a multiplatoon) can potentially enhance platoon control by keeping constant inter-vehicle and inter-platoon distances. However, an efficient resource allocation (RA) approach is required for the timely and successful delivery of inter-vehicle information within multiplatoons. In this paper, subchannel allocation scheme and power control mechanism are proposed for LTE-based inter-vehicle communications in a multiplatooning scenario. We jointly consider the evolved multimedia broadcast multicast services and device-to-device (D2D) multicast communications to enable intra- and inter-platoon communications such that a desired tradeoff between the required cellular resources and the imposed communication delay can be achieved. Simulation results are given to demonstrate that the proposed approaches can reduce the communication delay comparing to D2D-unicast based RA scheme, especially in a multiplatoon scenario with a large number of vehicles.

QoS-Based Interference Alignment With Similarity Clustering for Efficient Subchannel Allocation in Dense Small Cell Networks

Interference alignment (IA) can remarkably improve the spectral efficiency of dense small cell networks (SCNs) underlaying a macrocell, but its feasibility condition and implementation complexity are restricted by the number of small cell equipments (SUEs). Moreover, the SUEs performing IA may have unsatisfactory quality of service (QoS) requirements as IA only eliminates interference while neglecting the gain of desired signals. In this paper, we propose a centralized efficient subchannel allocation scheme based on IA with similarity clustering in dense SCNs underlaying a macrocell, which aims at maximizing the number of QoS guaranteed SUEs performing IA. The corresponding problem is formulated as a combinatorial optimization problem which is NP-hard. So a low-complexity solution is proposed which includes three phases: similarity clustering for SUEs through graph partitioning, further adjustments of cluster sizes to make IA feasible in each cluster, and subchannel allocation for the formed clusters, each of which is performed with a notably reduced computational complexity. Moreover, the proposed solution greatly reduces the signaling overhead incurred by channel state information estimation. Numerical results show that the proposed solution not only outperforms other related schemes, but also achieves a performance close to the optimal solution.

Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges

5G networks are expected to be able to satisfy users' different QoS requirements. Network slicing is a promising technology for 5G networks to provide services tailored for users' specific QoS demands. Driven by the increased massive wireless data traffic from different application scenarios, efficient resource allocation schemes should be exploited to improve the flexibility of network resource allocation and capacity of 5G networks based on network slicing. Due to the diversity of 5G application scenarios, new mobility management schemes are greatly needed to guarantee seamless handover in network-slicing-based 5G systems. In this article, we introduce a logical architecture for network-slicing-based 5G systems, and present a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum-sharing two-tier systems based on network slicing, where both the co-tier interference and cross-tier interference are taken into account. Simulation results demonstrate that the proposed resource allocation scheme can flexibly allocate network resources between different slices in 5G systems. Finally, several open issues and challenges in network-slicing-based 5G networks are discussed, including network reconstruction, network slicing management, and cooperation with other 5G technologies.

Energy-Efficient Transmission Strategy with Dynamic Load for Power Line Communications

To improve energy efficiency (EE) in power line communication (PLC) systems, we proposed a dynamic load-based PLC system model as a new model for EE maximization and an energy-efficient resource allocation strategy optimizing load impedance, transmission power and subchannel allocation as the optimization arguments. Since the load impedance at receivers is influenced by characteristics of a power line channel, optimizing the load impedance is required to maximally induce a received power while considering the channel characteristics. We sought to maximize network EE while satisfying constraints that transmission power of a transmitter cannot be exceeded by its maximum limit and minimum quality of service should be guaranteed. Therefore, we studied a scenario optimizing the three arguments based on orthogonal frequency division multiplexing downlink networks with the non-white Gaussian noise channel in multi-receiver PLC systems. Using nonlinear fractional programming and Lagrange dual method, we provided a tractable solution as an iterative algorithm obtaining the optimal value of the arguments. Simulation results showed that the proposed system is more energy-efficient compared to baseline schemes, and EE is greatly improved by the synergistic effects of the impedance optimization and the subchannel allocation strategy.