Energy Efficient Resource Allocation over Cloud-RAN Based Heterogeneous Network

Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can solve lots of challenges in the next generation mobile communication system, including the demand for higher energy efficiency(EE). In C-RAN EE research field, most of recent work focuses on systematic energy saving and ignores the needs of users. To improve the performance of C-RAN under fronthaul capacity constraint, signal quantization techniques have been developed. But how to introduce ‘quantization’ into C-RAN EE field is still an open issue. Motivated by this, in this paper, based on informational-optimal Gaussian quantization, we intend to design the suitable algorithms to maximize user-centric EE in the uplink communication of an orthogonal frequency division multiple access (OFDMA) based C-RAN. In the special case of single user and single RRH, we propose a joint optimization algorithm to maximize the uplink user-centric EE by optimizing power and fronthaul rate allocation. In the extended general case of multi-user and multi-RRH, we propose a Modified Particle Swarm Optimization(M-PSO) algorithm to solve the non-linear and non-convex issue for simplicity. Our simulation results show the proposed algorithms can improve the user-centric EE obviously compared with other optimal algorithms.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

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.

An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.

Recent advances in self-interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Unlike most existing works, we study the problem of energy-efficient resource allocation in FDR networks. We consider a shared FDR deployment scenario, where an FDR relay is deployed at the intersection of several adjacent cell sectors. First, a simple but practical transmission strategy is proposed to deal with the involved interference, i.e., multiaccess interference, multiuser interference, and self-interference. Then, the problem of joint power and subcarrier allocation is formulated to maximize the network-level energy efficiency while taking the residual self-interference into account. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem with high computation complexity, we use Dinkelbach and discrete stochastic optimization methods to solve the energy-efficient resource-allocation problem efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.

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

This paper investigates the resource allocation issue between downlink training stage and data transmission stage for the frequency division duplexing (FDD) massive multiple-input multiple-output system from the viewpoint of energy efficiency (EE). For a given total energy budget during a coherence period, how to jointly select the training duration, training power and data power is of great significance for the system EE. Thus, an optimization problem of energy-efficient resource allocation is put forward. Since the analytical expression of the involved average spectral efficiency (SE) is intractable, a closed-form approximation of the SE is deduced using deterministic equivalent. Based on the simplified expression, the original non-convex fractional optimization problem is transformed into an equivalent problem in subtractive form by the means of fraction programming, which includes an achievable solution. Then, an iterative algorithm is proposed. Numerical results validates the benefits of the proposed resource allocation scheme.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by merging RAN and cloud computing together. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The introduced Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

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.

In device-to-device (D2D) communications underlaying cellular systems based on the cloud radio access network, including distributed remote radio heads (RRHs) and centralized BaseBand Unit (BBU) pool, due to dense deployment and spectrum reuse, there are strong intra-cell interference and inter-cell interference, which will severely degrade both the energy efficiency (EE) and the quality of service. To address this problem, we propose an energy-efficient resource allocation scheme to determine channel selection and power allocation by introducing the relay-aided in-band D2D communication mode and the non-cooperative game theory. We explore the above resource allocation problem in a hybrid manner and model it as a non-cooperative game, where each player optimizes its EE individually with the aid of RRHs. Then, we alleviate the inter-cell interference from the cellular UEs near the cell edge by allowing these UEs to select in-band D2D relay links to shorten the long transmission distances between them and RRHs. Moreover, the interfering signal processing load of the BBU is lowered due to the reduction of the interfering signal strength, and the EE is improved due to the reduction of transmission powers of these cellular UEs. The achievable performance of the proposed scheme is analyzed through simulations.

As the demand for high-speed, low-latency, and energy-efficient mobile communications continues to surge with the proliferation of IoT, AR/VR, and ultra-reliable applications, traditional Distributed Radio Access Network (D-RAN) architectures face critical limitations. Cloud Radio Access Network (C-RAN) emerges as a promising alternative that centralizes baseband processing to improve scalability, resource utilization, and operational flexibility. This paper presents a comprehensive evaluation of C-RAN architecture, focusing on structural models, fronthaul technologies, and cloud-based service logic. A detailed mathematical modeling framework is developed to assess key performance indicators, including latency, spectral efficiency, energy efficiency, and fronthaul capacity. Extensive results demonstrate that C-RAN achieves up to 45% gains in energy efficiency, a 35% improvement in spectral efficiency, and latency reductions of over 40% compared to D-RAN. Additional results reveal enhanced handover success rates, better BBU pool utili-zation, and increased reliability, with packet loss rates reduced to under 0.5%. Despite increased fronthaul bandwidth requirements, optical solutions such as DWDM and PON mitigate the bottleneck effectively. The findings confirm that C-RAN offers a robust, scalable, and cost-efficient solution for 5G and future mobile networks, enabling dynamic resource allocation, advanced interference management, and centralized network intelligence. The paper also addresses implementation challenges, including fronthaul provisioning and security, and outlines future research directions such as virtualization, AI-driven orchestration, and edge-cloud integration to fully harness the potential of C-RAN in ultra-dense and heterogeneous network environments.

The energy-efficient resource allocation problem is investigated for the downlink massive multiple-input–multiple-output (MIMO) frequency-division duplexing (FDD) system under a correlated Rayleigh fading channel. Our objective is to maximize energy efficiency by adjusting the training duration, training power, and data power, under the constraint of the total transmit energy and spectral efficiency requirement for the user, which evaluates the impacts of the training and data transmission phases simultaneously. The optimization problem is established in a complicated form, where the difficulties lie in the nonanalytic expression of the involved objective function, as well as the nonconvex nature of the optimization problem. To solve it, the deterministic equivalent approximation methodology is introduced to obtain an accurate analytical expression of the cost function. Based on this, the optimization problem in a nonconvex fractional form is transformed into an equivalent optimization problem with a parametric subtractive form, which is still nonconvex. Thus, the objective function is lower bounded by a concave function, which leads to the availability of standard convex theory, and finally, an iterative resource allocation algorithm is proposed. Moreover, we obtain the closed-form solutions using the Lambert W function for some special channel cases. Numerical results validate the benefits of the proposed scheme and illustrate the tradeoff between training and data transmission.

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.

Energy efficiency is an important design criterion for wireless\ncommunications. When parallel frequency channels are used for multiuser\ntransmission, the channel bandwidths and user power are adjusted to maximize\nthe sum information rate with the bandwidth budget, the transmit power budget,\nand the user-specific rate requirements. The maximum sum rate is used in\nmeasuring the energy efficiency. With fixed or flexible bandwidths of the\nfrequency channels, practical methods are developed to find the total transmit\npower with the unique optimal resource (bandwidth and power) allocation for\nmaximum energy efficiency. This resource allocation ensures that, while each\nuser's minimum rate requirement is satisfied, all the excess resource of the\nspectrum and transmit power is dedicated to the one user with the best channel\nquality. Simulation results validate the optimal solutions of total transmit\npower and resource allocation that support the energy-efficient multiuser\ntransmission.\n

To alleviate the greenhouse effect, high network energy efficiency (EE) has become an important research target in wireless green communications. Therefore, an investigation for resource management to mitigate co-tier interference in a small-cell network (SCN) is provided. Moreover, with the merits of cloud radio access networks (C-RANs), small-cell base stations can be decomposed into central small cells (CSCs) and remote small cells (RSCs). To achieve coordination, split medium access control (MAC)-based functional splitting is adopted with a scheduler deployed at CSCs and retransmission functions implemented at RSCs. However, limited fronthaul has a compelling effect on RSCs owing to the requirements of user quality of service (QoS). In this paper, we propose a hybrid controlled user association and resource management (HARM) scheme to deal with the infeasibility of controlling all RSCs using a single CSC. A CSC performs the traffic-control-based user association and resource allocation (TURA) scheme for RSCs to mitigate intra-group interference within localized C-RANs, whereas the CSCs among separate C-RANs conduct cooperative resource competition (CRC) games to alleviate inter-group interference. Based on the regret-based learning algorithm, the proposed schemes are analytically proven to reach the correlated equilibrium (CE). The simulation results validate the capability of traffic control in the TURA scheme and the convergence of CRC. Moreover, a comparison of the proposed TURA, HARM, and CRC schemes with the benchmark is revealed. It is observed that the TURA scheme outperforms the other methods under ideal fronthaul control, whereas the HARM scheme can sustain EE performance considering practical implementation.