On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

With the explosive growth of wireless data traffic, the energy-efficient design in wireless networks is becoming an inevitable trend. In this paper, we study the low-complexity design issue of energy-efficient radio resource allocation in downlink OFDMA networks. Distinguished from the existing works, we focus on the tradeoff between the energy efficiency and the total transmit power. The closed-form optimal solution is obtained by solving the first-order optimality condition of the energy efficiency maximization problem. Furthermore, based on the analytical solutions, we develop a low-complexity optimal algorithm for the energy-efficient resource allocation. Simulation results show that the proposed algorithm achieves the optimal energy efficiency with significantly lower computational complexity compared with the conventional iterative methods.

Wireless Body Area Network (WBAN) represents one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, a buffer aware Energy-efficient and QoS-effective resource allocation scheme is proposed in which the sensor queue buffer states, constraints of QoS metrics and the characteristics of dynamic links are considered. Specifically, a buffer aware sensor evaluation method is designed to dynamically evaluate the sensor state with considering the sensor buffer states for improving the system performance. We then formulate the resource allocation problem for optimizing the transmission power, the transmission rate and the allocated time slots for each sensor to minimize the sum mix-cost, which is defined to characterize the energy cost and QoS cost between attainable QoS support and QoS requirements. Simulation results demonstrate the effectiveness of the buffer aware sensor evaluation method and the proposed energy-efficient and QoS-effective resource allocation scheme.

In this paper, we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of uplink Long Term Evolution (LTE) systems. The proposed algorithm considers the main constraints in uplink LTE resource allocation, i.e., the allocation of contiguous sets of resource blocks of the localized Single Carrier—Frequency Division Multiple Access (SC-FDMA) physical layer to each user, and the imperfect knowledge of the users' uplink buffer status and packet waiting time. The optimal resource allocation is formulated as a discrete connected cake-cutting problem, where different agents are allocated consecutive subsequences of a sequence of indivisible items. This problem is NP-hard, therefore a suboptimal algorithm is introduced, which performs resource allocation using information on the estimated uplink packet delay, the average delay and data rate of past allocations, as well as the required uplink power per resource block. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, and fairness. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.

Long‐Term Evolution–unlicensed (LTE‐U) has recently attracted worldwide interest to meet the explosion in cellular traffic data. By using carrier aggregation, licensed and unlicensed bands are integrated to enhance transmission capacity while maintaining reliable and predictable performance. As there may exist other conventional unlicensed band users, such as WiFi users, LTE‐U users have to share the same unlicensed bands with them. Thus, an optimized resource allocation scheme to ensure the fairness between LTE‐U users and conventional unlicensed band users is critical for the deployment of LTE‐U networks. In this paper, we investigate an energy efficient resource allocation problem in LTE‐U coexisting with other wireless networks, which aims at guaranteeing fairness among the users of different radio access networks. We formulate the problem as a multiobjective optimization problem and propose a semidistributed matching framework with a partial information‐based algorithm to solve it. We demonstrate our contributions with simulations in which various network densities and traffic load levels are considered.

Wireless network virtualization has been envisioned as a promising technology to enhance resource utilization and reduce expenditure. In this paper, we propose a virtual resource allocation strategy in a mobile virtual network, which reuses spectrum resource to reduce the physical resource that service providers (SPs) need to purchase and also enhance spectrum efficiency. Mobile virtual network operators (MVNOs) benefit not only from sales of the spectrum resource, but also from its reuse levels by SPs. However, the SP's service quality requirement and user channel state information are hidden information to the MVNO, and are the basis needed for the MVNO's resource allocation design. Besides, how many users will be arranged to reuse is a hidden action, independently decided by the SPs themselves, which the MVNO is unable to know. To deal with the two kinds of asymmetric information problems, we design contracts which include two parts of fees charged for each SP. First of all, SPs are classified into different types based on their distinct characteristics and assigned with corresponding resource prices to deal with the hidden information problem. Then to incentivize higher resource reuse level, rank-order tournament (ROT) design is adopted to obtain the MVNO's commission rates for SPs. Numerical results show that our scheme can improve the utility of both SPs and MVNOs as well as the spectrum efficiency.

In 5G mobile technology, the expansion of Internet of Things (IoT) has created a huge need for a wide variety of latency, dependability, and energy efficiency requirements, etc… Spectrum efficiency (SE) of such large scale network needs to be improved with an economical power consumption. The non-orthogonal multiple access (NOMA) technique is utilized to enhance system efficiency (SE) by merging several users in the same frequency. An energy efficient (EE) resource allocation (RA) problem has been formulated for NOMA based heterogeneous IoT networks. Using the examining technique of Cognitive Radio (CR) Network, a stepwise RA scheme is assigned for IoT Users (IoTUs) and Mobile Users (MUs) with the mutual interference management. Later, to find a solution quickly and flawlessly, a deep recurrent neural network (RNN) based mechanism has been proposed. Furthermore, to systemize the approach of heterogeneous users, a rate and precedence demands based scheduling method has been implemented. Extensive results demonstrate that the deep learning based framework performs better than traditional RA methods in terms of computational complexity. On comparing with the prevailing OFDMA technique, the NOMA system with the imperfect SIC provides an acceptable performance on the EE at the cost of low EE and high power consumption.

Balancing energy efficiency and throughput fairness in IEEE 802.11 WLANs

Multi-objective optimization oriented policy for performance and energy efficient resource allocation in Cloud environment

In this paper, an energy efficient resource allocation is investigated to enhance the network performance of cellular networks. Specifically, the small cells (SCs) using both green and on-grid energy and the power splitter-enabled wireless charging for user equipments (UEs) are considered. A quality-of-service (QoS)-constrained problem is then designed to maximize energy efficiency (EE) through the joint strategies of power allocation, resource block (RB) assignment, and power splitting ratio adjustment. By exploiting some mathematical transformations, the non-convex optimization problem can be solved by adopting the proposed joint wireless charging and hybrid power based resource allocation (JWHRA) algorithm. The efficiency of proposed algorithm is validated via simulation. Numerical results show that the proposed JWHRA scheme can provide higher EE with its joint design on wireless charging and hybrid energy source.

In this paper, resource allocation for energy efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs) is studied. The considered problem is formulated as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate for joint BS zero-forcing beamforming (ZFBF) transmission. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation solution is derived with a low complexity suboptimal subcarrier allocation policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency and network capacity.

This paper investigates the efficient deployment and resource allocation of multiple unmanned aerial vehicles (UAVs) to be integrated with wireless terrestrial networks as aerial base stations (BSs) and serve ground user devices by utilizing non-orthogonal multiple access (NOMA) scheme. Aiming to optimize the energy efficiency (EE) of ground BSs (GBSs) and deployed UAVs, a constrained EE maximization problem is formulated. To solve the optimization problem, we decompose the problem into three subproblems. Firstly, GBS association and subchannel assignment subproblem is formulated and solved by applying matching theory. Then, UAV deployment and subchannel assignment subproblem is formulated. To solve the problem, we propose a clustering strategy based on K-means algorithm to divide users into approximately equal-size clusters. Then, a dynamic many-to-many matching algorithm is proposed to assign subchannels to the users. Finally, a non-convex power allocation subproblem is formulated and solved iteratively using a two-stage quadratic transform. The numerical results demonstrate the effectiveness of our proposed scheme.

Internet-based social and interactive video applications have become major constituents of the envisaged applications for next-generation multimedia networks. However, inherently dynamic network conditions, together with varying user expectations, pose many challenges for resource allocation mechanisms for such applications. Yet, in addition to addressing these challenges, service providers must also consider how to mitigate their operational costs (e.g., energy costs, equipment costs) while satisfying the end-user quality of service (QoS) expectations. This paper proposes a heuristic solution to the problem, where the energy incurred by the applications, and the monetary costs associated with the service infrastructure, are minimized while simultaneously maximizing the average end-user QoS. We evaluate the performance of the proposed solution in terms of serving probability, i.e., the likelihood of being able to allocate resources to groups of users, the computation time of the resource allocation process, and the adaptability and sensitivity to dynamic network conditions. The proposed method demonstrates improvements in serving probability of up to 27%, in comparison with greedy resource allocation schemes, and a several-orders-of-magnitude reduction in computation time, compared to the linear programming approach, which significantly reduces the service-interrupted user percentage when operating under variable network conditions.

To enjoy rich Internet services, contemporary mobile devices generate heavy loads of computation tasks, which cannot be suffered locally due to the limited computing and energy capacities of each device. Cloud radio access network (Cloud RNA) based heterogeneous structure is widely accepted as a promising way to make up devices' shortage and computation need. To determine whether the computation task should be migrated from the device to the Cloud RAN, traditional work usually aims to improve resource utilization efficiency and constrain the latency of computation task. However, less concerned on the energy efficiency problem for the device. Through maintaining higher energy efficiency, device can utilize the limited energy in a reasonable way. In this paper, we formulate an energy efficient resource allocation problem for the computation tasks in Cloud-RAN based heterogeneous Network (Cloud-RAN HetNet). Within this problem, two subproblems are proposed. The first is determining whether computation task on the device should be offloaded to the the Cloud RAN. The second is energy efficiency based resource allocation while considering latency limitation for the computation tasks. To solve the proposed problem, a computation location selection and resource allocation (CCSRA) method is put forward. Finally, we utilize Various simulation results show the property of the strategy.

This thesis presents novel resource allocation algorithms for energy and power efficient transmissions in future mobile wireless networks. Algorithms presented in thesis incorporate a combination of practical conditions: mixed services, rate fairness, the density of deployment of femtocells and non-linear power consumption. The resource allocation problem of sparse and dense deployment of Orthogonal Frequency Division Multiple Access based heterogeneous femtocell networks is solved by optimising spectral efficiency, energy efficiency and spectral and energy efficiency trade-off respectively. In general, this research proposes several resource allocation algorithms that are practical, efficient and lower in computational complexity.

In 3GPP Release-12 specification, the adoption of small cells in long term evolution (LTE) networks is considered as a promising solution to meet the growing traffic demand for mobile data usage. However, energy consumption becomes a crucial challenge for network operators to deploy more small cells in heterogeneous networks in order to fulfill the requirement for achieving high data rates. In this paper, an advanced signaling mechanism has been proposed to practically acquire global channel state information (CSI) between network components. Based on the signaling scheme, an energy-efficient cell selection and resource allocation (ECR) algorithm is proposed to increase system energy efficiency by handovering UEs from light-loaded base station to nearby cells. Some active cells can therefore be switched off to reduce total energy consumption. Simulation results show that better energy efficiency can be achieved by adopting the proposed signaling and ECR schemes.