Optimal Radio Resource Allocation Research Articles

Joint Optimization of 3D Placement and Radio Resource Allocation for Per-UAV Sum Rate Maximization

Unmanned aerial vehicles (UAV) have emerged as a practical solution that provides on-demand services to users in areas where the terrestrial network is non-existent or temporarily unavailable, e.g., due to natural disasters or network congestion. In general, UAVs' user-serving capacity is typically constrained by their limited battery life and the finite communication resources that highly impact their performance. This work considers the orthogonal frequency division multiple access (OFDMA) enabled multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive an efficient technique for the allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: (i) 3D placement and user association and (ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after fewer than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, results showed that the optimal power and sub-carrier allocation help to mitigate the inter-cell interference that directly impacts the system's performance.

An Optimal Resource Allocation in 5G Environment Using Novel Deep Learning Approach

In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

Joint Particle Swarm Optimization of Power and Phase Shift for IRS-Aided D2D Underlaying Cellular Systems.

Device-to-device (D2D) communication is a promising wireless communication technology which can effectively reduce the traffic load of the base station and improve the spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further improve the throughput, but the problem of interference suppression becomes more complex and challenging due to the introduction of new links. Therefore, how to perform effective and low-complexity optimal radio resource allocation is still a problem to be solved in IRS-assisted D2D communication systems. To this end, a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization is proposed in this paper. First, a multivariable joint optimization problem for the uplink cellular network with IRS-assisted D2D communication is established, where multiple DUEs are allowed to share a CUE's sub-channel. However, the proposed problem considering the joint optimization of power and phase shift, with the objective of maximizing the system sum rate and the constraints of the minimum user signal-to-interference-plus-noise ratio (SINR), is a non-convex non-linear model and is hard to solve. Different from the existing work, instead of decomposing this optimization problem into two sub-problems and optimizing the two variables separately, we jointly optimize them based on Particle Swarm Optimization (PSO). Then, a fitness function with a penalty term is established, and a penalty value priority update scheme is designed for discrete phase shift optimization variables and continuous power optimization variables. Finally, the performance analysis and simulation results show that the proposed algorithm is close to the iterative algorithm in terms of sum rate, but lower in power consumption. In particular, when the number of D2D users is four, the power consumption is reduced by 20%. In addition, compared with PSO and distributed PSO, the sum rate of the proposed algorithm increases by about 10.2% and 38.3%, respectively, when the number of D2D users is four.

Novel Deep Learning Approach to Support Optimal Resource Allocation in 5G Environment

In recent times, the advancement in network devices has focused entirely on the miniaturisation of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, we develop a resource allocation model using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

Joint Optimization of Cooperative Edge Caching and Radio Resource Allocation in 5G-Enabled Massive IoT Networks

The fifth-generation of wireless communication (5G) is a promising paradigm toward massive interconnectivity within Internet-of-Things (IoT) networks. However, because the data traffic throughput sharply increases with the number of IoT devices, a tremendous burden on the backhaul links and core networks results. With this in mind, mobile edge caching is an effective method that can relieve stress of the backhaul links, while decreasing the service latency. The purpose of this study is to analyze the problem of jointly optimizing cooperative edge caching and radio resource allocation in 5G-enabled massive IoT networks. For that, a joint optimization long-term nonlinear integer programming problem is posed. This class of problems is known to be NP-hard; thus, to reduce the problem complexity, a divide and conquer scheme will be applied—the task at hand will be divided into two subproblems: 1) cooperative edge caching and 2) radio resource allocation. The cooperative edge caching subproblem is formulated as a constrained Markov decision process. Herein, a deep reinforcement learning method to optimize the caching decisions for all the edge nodes. Then, based on the resulting optimal caching decisions, the radio resource allocation subproblem for each edge node is posed as an NLIP problem, and an improved branch-and-bound method is proposed to yield the optimal radio resource allocation decisions for each edge node. Extensive simulations were performed to confirm that the proposed methods have the capability of enhancing the content caching hit ratio, while lessening the content retrieving delays for 5G-enabled massive IoT networks—improving over various baseline algorithms.

Hybrid Radio Resource Management for Time-Varying 5G Heterogeneous Wireless Access Network

In this paper, we explore radio resource management for a time-varying 5G heterogeneous wireless access network that includes multi-RATs such as 5G new radio (NR) and long-term evolution (LTE). To cope with the practical challenges of a centralized approach such as signalling overhead and computational complexity, we decomposed the process of radio resource management into three parts, 1) RAT selection, 2) optimal radio resource allocation, and 3) congestion control. RAT selection is performed by each user device with network assistance, whereas the problem of radio resource allocation and congestion control is formulated as a stochastic optimization problem. Maintaining network stability, the average throughput utility is maximized subject to admission control and resource allocation. By using Lyapunov optimization, this utility maximization problem is decomposed into two subproblems. Radio resource allocation policy implemented at the central controller node allocates resources at each time slot using the Lagrange dual method, whereas the process of congestion control is carried out at user end based on throughput adaptation according to its current channel conditions. The theoretical and simulation results evaluate the performance of our proposed approach under the assumption of network stability. Simulation results related to individual users throughput and queue length, and performance comparison of equal power and adaptive power allocation techniques, are presented to depict the effectiveness of our proposed scheme. Furthermore, our proposed RAT selection scheme performs better than the traditional centralized and distributive mechanisms.

QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

In this paper, we consider the saturation problem in the 3GPP LTE cellular system caused by the expected huge number of machine‐type communication (MTC) devices, leading to a significant impact on both machine‐to‐machine (M2M) and human‐to‐machine H2H traffic. M2M communications are expected to dominate traffic in LTE and beyond cellular networks. In order to address this problem, we proposed an advanced architecture designed for 5G LTE networks to enable the coexistence of H2H/M2M traffic, supported by different priority strategies to meet QoS for each traffic. The queuing strategy is implemented with an M2M gateway that manages four queues allocated to different types of MTC traffic. The optimal radio resource allocation method in LTE and beyond cellular networks was developed. This method is based on adaptive selection of channel bandwidth depending on the QoS requirements and priority traffic aggregation in the M2M gateway. Additionally, a new simulation model is proposed which can help in studying and analyzing the mutual impact between M2M and H2H traffic coexistence in 5G networks while considering high and low priority traffics for both M2M and H2H devices. This simulator automates the proposed method of optimal radio resource allocation between the M2M and H2H traffic to ensure the required QoS. Our simulation results proved that the proposed method improved the efficiency of radio resource utilization to 13% by optimizing the LTE frame formation process.

A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling.

Considering that networks based on New Radio (NR) technology are oriented to provide services of desired quality (QoS), it becomes questionable how to model and predict targeted QoS values, especially if the physical channel is dynamically changing. In order to overcome mobility issues, we aim to support the evaluation of second-order statistics of signal, namely level-crossing rate (LCR) and average fade duration (AFD) that is missing in general channel 5G models. Presenting results from our symbolic encapsulation point 5G (SEP5G) additional tool, we fill this gap and motivate further extensions on current general channel 5G. As a matter of contribution, we clearly propose: (i) anadditional tool for encapsulating different mobile 5G modeling approaches; (ii) extended, wideband, LCR, and AFD evaluation for optimal radio resource allocation modeling; and (iii) lower computational complexity and simulation time regarding analytical expression simulations in related scenario-specific 5G channel models. Using our deterministic channel model for selected scenarios and comparing it with stochastic models, we show steps towards higherlevel finite state Markov chain (FSMC) modeling, where mentioned QoS parameters become more feasible, placing symbolic encapsulation at the center of cross-layer design. Furthermore, we generate values within a specified 5G passband, indicating how it can be used for provisioningoptimal radio resource allocation.

Energy-Efficient Multi-UAV-Enabled Multiaccess Edge Computing Incorporating NOMA

Multiaccess edge computing (MEC) is regarded as a promising solution to overcome the limit on the computation capacity of mobile devices. This article investigates an energy-efficient unmanned aerial vehicle (UAV)-enabled MEC framework incorporating nonorthogonal multiple access (NOMA), where multiple UAVs are deployed as edge servers to provide computation assistance to terrestrial users and NOMA is adopted to reduce the energy consumption of task offloading. A utility is formed to mathematically evaluate the weighted energy cost of the system. Due to the coupling of parameters, the minimization of utility is a highly nonconvex problem and therefore, the problem is decomposed into two more tractable subproblems, i.e., the optimal allocation of radio and computation resources given UAV trajectories, and the trajectory planning based on given resource allocation schemes. These two problems are converted to convex ones via successive convex approximation (SCA) and quadratic approximation, respectively. Then, an efficient iterative algorithm is proposed where these two subproblems are alternately solved to gradually approach the optimal resource management of the proposed system. Sufficient numerical results show that our proposed strategy has a remarkable advantage over existing systems in terms of energy efficiency.

Optimal radio resource allocation to achieve a low BER in PD‐NOMA–based heterogeneous cellular networks

AbstractIn this paper, the problem of downlink radio resource allocation for a power‐domain nonorthogonal multiple‐access–based heterogeneous cellular network is investigated. We propose two scenarios: (1) resource allocation with exclusive subcarrier allocation and (2) resource allocation considering intercell interference. The aim of this paper is to obtain joint subcarrier and power allocation for the two considered system models by addressing the maximization of the users' sum rate subject to the bit error rate constraint, the successive interference cancellation constraint, subcarrier constraints, and the transmit power restriction of each base station. To solve the corresponding optimization problems, an alternating method is applied by decoupling the proposed problems into two subproblems, namely, subcarrier allocation problem and power allocation problem, where they are solved by using the mesh adaptive direct search algorithm and different successive convex approximation methods, respectively. Moreover, to evaluate the optimality gap of the alternating method, the proposed problems are solved using an optimal method, namely, monotonic optimization with high complexity as a benchmark. As the results show, with the bit error rate constraint, the link‐level performance, at the cost of a decreasing system sum rate, is improved.

The Influence of Spectrum Resource Optimization Allocation on Economic Growth Oriented to the Information Maturity

With the rapid development of information and communication technology (ICT), a higher demand is put forward for the optimal allocation of radio spectrum resources carrying core ICT applications, include in the fields of intelligent manufacturing, transportation and public utilities and so on. The influence of optimal allocation for radio spectrum resources on economic growth is increasingly being widely attention. In this paper, we analyze the influence and contribution of optimized allocation of radio spectrum resources on economic growth. An impact index system of spectrum resource allocation on economic growth is built based on information maturity. Then, the index of key influence factors is extracted by the rough set theory. Moreover, the influence and contribution of optimal allocation of radio spectrum resources on economic growth are analyzed by Cobb-Douglas (C-D) production function. The results show that the influence and comprehensive contribution of spectrum resources optimal allocation on economic growth (GDP) are fitted, based on the indirect impact of ICT on various industries/industries, using actual economic growth data from 2008 to 2017. The proposed method provides a scientific basis and decision support for optimal allocation of spectrum resources in the future. Meanwhile, the conclusion has theoretical and practical significance to promote the progress of ICT and the development of social economy.

Spectral and Energy Efficiency Trade-offs in Cellular Networks

This paper presents a simple and effective method to study the spectral and energy efficiency (SE-EE) trade-off in cellular networks, an issue that has attracted significant recent interest in the wireless community. The proposed theoretical framework is based on an optimal radio resource allocation of transmit power and bandwidth for the downlink direction, applicable for an orthogonal cellular network. The analysis is initially focused on a single cell scenario, for which in addition to the solution of the main SE-EE optimization problem, it is proved that a traffic repartition scheme can also be adopted as a way to simplify this approach. By exploiting this interesting result along with properties of stochastic geometry, this work is extended to a more challenging multicell environment, where interference is shown to play an essential role and for this reason several interference reduction techniques are investigated. Special attention is also given to the case of low signal-to-noise ratio (SNR) and a way to evaluate the upper bound on EE in this regime is provided. This methodology leads to tractable analytical results under certain common channel properties, and thus allows the study of various models without the need for demanding system-level simulations.

Cross‐layer routing and optimal radio resource allocation in OFDMA‐based cognitive radio ad‐hoc networks: spectrum sensing strategy

AbstractIn this paper, we propose a cross‐layer routing and radio resource allocation in an orthogonal frequency‐division multiple access‐based wireless cognitive radio ad hoc networks, where the availability of local spectrum resources may change instantaneously. The main objective is to determine sensing time, the data routes, subcarrier schedules and power allocations that maximize throughput of the data communicated subject to the transmission power, interference, routing and capacity constraints. We propose an iterative algorithm that optimises the subcarrier allocation and the power allocation alternatively routing in spectrum sensing based cognitive radio ad hoc networks. Finally, we provide simulation results to show the performance of the proposed algorithms and validate our theoretical analysis. Copyright © 2015 John Wiley & Sons, Ltd.

A novel low complexity energy‐efficient resource allocation for OFDM systems

AbstractIn this paper, we study energy efficiency of an orthogonal frequency division multiplexing based system under power constraint and minimum rate requirement with system circuit power consideration. The optimal radio resource allocation is then formulated as an energy‐efficient maximisation where we propose a novel low complexity solution for obtaining the optimal solution. The solution consists of the optimal source transmission power and its distribution among subcarriers. In spite of the iterative solution with high computational complexity in previous works, we propose a quickly convergent low complexity scheme based on solving a nonlinear logarithmic equation. To evaluate the accuracy of the proposed method, we compared its accuracy through simulations with the optimal solution in systems with and without circuit power consideration. Simulation studies indicate that the proposed method provide accurate solutions in both low and high signal to noise ratio regimes. The simulation results also indicate using our proposed method results in a significant improvement in the computational complexity. Copyright © 2015 John Wiley & Sons, Ltd.

Optimal radio resource allocation for mobile task offloading in cellular networks

The increasing capabilities of mobile devices (MDs) enable users to run desktoplevel applications and tasks anywhere. However, the local execution of resource-demanding mobile tasks, such as real-time rendering in 3D gaming, may result in poor performance and short battery lifetime on MDs. One solution to the resource scarcity problem on MDs is to offload resource-demanding mobile tasks to surrogates, which are remote servers with stronger capabilities. When multiple MDs in the same cell attempt to offload mobile tasks to surrogates through a cellular network, the communication latencies for data transmissions may be high due to limited radio resources. To improve the offloading performance of mobile tasks, the limited radio resources should be carefully scheduled to reduce the communication latencies of data transmissions. As motivated, we pose the optimal radio resource allocation problem for the mobile task offloading in cellular networks, and solve the problem. Moreover, extensive numerical results show the performance comparison of the optimal radio resource allocation plan and two baseline plans.

Intracell Cooperation and Resource Allocation in a Heterogeneous Network With Relays

With macro and micro base stations (BSs) of different transmission and processing capabilities jointly deployed, heterogeneous networks effectively extend the coverage and capacity of wireless networks and have been adopted as one of the key technologies in Fourth-Generation-Long-Term Evolution (4G-LTE). To fully exploit the benefits of heterogeneous networks, proper mobile association, interference management, and radio resource management schemes need to be developed. In this paper, we investigate radio resource allocation for heterogeneous networks with cooperative relays, where the relay nodes (RNs) with in-band backhaul act as micro BSs and are able to serve user equipment (UE) either independently or cooperatively with the BSs. In such a network, radio resource-allocation schemes need to decide for each resource block: 1) whether a UE is served by a BS or an RN, and 2) whether a UE should be cooperatively served or not. In making these decisions, radio resource consumption on RN's in-band backhaul links and fairness among the UEs should be taken into consideration, which further complicates the resource-allocation problem. In this paper, we propose a radio resource-allocation framework and derive a resource-allocation strategy that is asymptotically optimal on the proportional fairness metric. The derived resource-allocation scheme gives insights on the optimal radio resource allocation for the heterogeneous networks with cooperative RNs using in-band backhauls.

Downlink Radio Resource Allocation in OFDMA Spectrum Sharing Environment with Partial Channel State Information

Here our focus is on the downlink radio resource allocation in underlay spectrum sharing based on OFDMA technology. Both continuous and discrete rate strategies are investigated. We consider the practical case in which for the wireless channel between the secondary base station and secondary users only partial channel state information (CSI) is available at the secondary base station. We formulate the resource allocation problem in the secondary network as an optimization problem in which the objective is to maximize the secondary users weighted sum rate. Two main constraints at the secondary base station are the maximum total transmission power, and the primary service collision probabilities. The only available a priori information is the channel distribution information (CDI) for the channel between the secondary base station and the primary receivers. Since the optimal radio resource allocation is non convex we utilize dual optimization method to obtain suboptimal solutions. The computational complexity due to the constraints in the original radio resource allocation is then reduced by exploiting system specifications and substituting the original constraints with the equivalent constraints on the transmission power and rate. Simulations studies are conducted to investigate the impact of the different system parameters. We also compare the proposed algorithm with the conventional radio resource allocation and show that by the proposed algorithm we are able to enforce the collision probability constraint in the primary service which in return results in a slight decreasing in the sum rate of the secondary system. Furthermore, we show that the proposed schemes are able to keep the outage probability imposed by imperfect CSI below a given threshold.

Radio resource allocation in OFDM-based cooperative relaying networks for a mixture of elastic and streaming traffic

In this study, the authors formulate the optimal radio resource allocation for OFDM-based relay networks with heterogeneous delay requirements. The authors consider streaming traffic that requires a maximum guaranteed average delay and elastic traffic with flexible rate requirements. The main objective is to maximise the total transmission rate of elastic users, while average delay constraint for streaming traffic is satisfied. In the proposed formulation we also consider transmit power constraint for the base station as well as each relay. The authors then propose an algorithm based on dual approach to find the optimum subcarrier, relay assignment and power allocation. Using simulations, the authors evaluate the impact of streaming traffic on the total rate of elastic users for different number of relays and various power constraints.

Layered resource allocation for video broadcasts over wireless networks

This paper aims to combine adaptive modulation and coding with layered video coding to improve the quality of video services to users experiencing differing radio conditions, in the context of broadcast and multicast standards such as MBMS and BCMCS. We propose an optimal radio resource allocation algorithm which maximizes a general performance metric for a video session in polynomial time. We show that system-wide optimal resource allocation can be obtained by combining our algorithm with a simple two-step decomposition of the system. In some configurations frequent re-allocations of resource are required, so we also present a sub-optimal allocation algorithm which runs in near linear time. Simulation results show better video quality than existing resource allocation schemes over a range of conditions, and also suggest that the difference between the performance of optimal and suboptimal solutions is less than 3%.

Integrated Radio Resource Allocation for Multihop Cellular Networks With Fixed Relay Stations

Recently, the notion that a logical next step towards future mobile radio networks is to introduce multihop relaying into cellular networks, has gained wide acceptance. Nevertheless, due to the inherent drawbacks of multihop relaying, e.g., the requirement for extra radio resources for relaying hops, and the sensitivity to the quality of relaying routes, multihop cellular networks (MCNs) require a well-designed radio resource allocation strategy in order to secure performance gains. In this paper, the optimal radio resource allocation problem in MCNs, with the objective of throughput maximization, is formulated mathematically and proven to be NP-hard. Considering the prohibitive complexity of finding the optimal solution for such an NP-hard problem, we propose an efficient heuristic algorithm, named integrated radio resource allocation (IRRA), to find suboptimal solutions. The IRRA is featured as a low-complexity algorithm that involves not only base station (BS) resource scheduling, but also routing and relay station (RS) load balancing. Specifically, a load-based scheme is developed for routing. A mode-aware BS resource-scheduling scheme is proposed for handling links in different transmission modes, i.e., direct or multihop. Moreover, a priority-based RS load balancing approach is presented for the prevention of the overloading of RSs. Within the framework of the IRRA, the above three functions operate periodically with coordinated interactions. To prove the effectiveness of the proposed IRRA algorithm, a case study was carried out based on enhanced uplink UMTS terrestrial radio access/frequency-division duplex with fixed RSs. The IRRA is evaluated through system level simulations, and compared with two other cases: 1) nonrelaying and 2) relaying with a benchmark approach. The results show that the proposed algorithm can ensure significant gains in terms of cell throughput