Heterogeneous Cloud Radio Access Networks Research Articles

Energy-Efficient 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU) Using a Hybrid OGASCDASA-SAGMDEBROA Scheduling Algorithm

In fifth-generation (5G) networks, the deployment of heterogeneous networks (HetNets) with macrocells layered over tiny cells is considered to be a practical solution to handle the growing demand for mobile traffic. Even though the deployment of a significant count of small cell base stations (BSs) results in a notable rise in energy consumption. In this manuscript, Energy-Efficient (EE) 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU), using Hybrid online green algorithm-based sleep scheduling and cost-efficient deadline-aware Scheduling Algorithm (OGASCDASA), are proposed. Here, the energy efficiency optimization issue is in the downlink of two-tier Heterogeneous Cloud Radio Access Networks (H-CRAN) by lowering micro and pico cells. Hybrid OGASCDASA is proposed for reducing remote radio side energy use when maintaining coverage and Quality of service (QoS) of H-CRAN. At the cloud side Baseband Units (BBU), hybrid Simulated Annealing with the Gaussian Mutation and Distortion Equalization algorithm with Battle Royale optimization is proposed to reduce the energy consumption of BBU by decreasing the count of BBU servers. The proposed EE-HCRAN-Hybrid OGASCDASA-SAGMDEBROA method attains 20.48%, 27.34%, and 32.24% higher throughput and 28.30%, 17.30%, and 32.94% lower delay compared to the existing models, such as heterogeneous computational resource allocation for NOMA (HCRA-NOMA-GMECS), energy-efficient hierarchical resource sharing in uplink-downlink decoupled NOMA heterogeneous networks (EE-HRA-NOMA-HetNet), energy-aware hierarchical resource management with backhaul traffic optimization in heterogeneous cellular networks (EA-HRM-BTO-HCN), respectively.

Efficient User Association Strategy for Maximizing User Satisfaction and Resource Utilization in Heterogeneous Cloud Radio Access Networks (H-CRANs)

The primary aim of this study is to present a User Association Strategy that can effectively optimize the Heterogeneous Cloud Radio Access Network (H CRAN). The primary objective of this strategy is to enhance customer satisfaction by optimizing the utilization of available network resources, such as transmission power and bandwidth. To achieve this objective, the proposed approach employs a logarithmic barrier function to address inequality constraints and transform the optimization problem into a formulation that facilitates effective convergence towards the optimal solution.

UAV-Assisted Heterogeneous Cloud Radio Access Network With Comprehensive Interference Management

UAV-Assisted Heterogeneous Cloud Radio Access Network With Comprehensive Interference Management

Resource Allocation Energy Efficient Algorithm for H-CRAN in 5G

In today's generation, the demand for data rates has also increased due to the rapid surge in the number of users. With this increasing growth, there is a need to develop the next fifth generation network keeping in mind the need to replace the current 4G cellular network. The fifth generation (5G) design in mobile communication technology has been developed keeping in mind all the communication needs of the users. Heterogeneous Cloud Radio Access Network (H-CRAN) has emerged as a capable architecture for the newly emerging network infrastructure for energy efficient networks and high data rate enablement. It is considered as the main technology. Better service quality has been achieved by developing small cells into macro cells through this type of network. In addition, the reuse of radio resources is much better than that of homogeneous networks. In the present paper, we propose the H-CRAN energy-efficient methods. This energy-efficient algorithm incorporates an energy efficient resource allocation management design to deal to heterogeneous cloud radio access networks in 5G. System throughput fulfillment is elevating by incorporating an efficient resource allocation design by the energy consumption model. The simulation results have been demonstrated by comparing the efficiency of the introduced design with the existing related design.

Multi-Objective Energy Efficient Resource Allocation in Massive Multiple Input Multiple Output-Aided Heterogeneous Cloud Radio Access Networks

In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost is considered. H-CRANs serve groups of users through femto-cell access points (FAPs) and remote radio heads (RRHs) equipped with massive multiple input multiple output (MIMO) connected to the base-band unit (BBU) pool via front-haul links with limited capacity. We formulate an energy-efficient multi-objective optimization (MOO) problem with a novel utility function. Our proposed utility function simultaneously improves two conflicting goals as total system throughput and operation cost. With this MOO, we jointly assign the sub-carrier, transmit power, access point (AP)(RRH/FAP), RRH, front-haul link, and BBU. To address the conflicting objectives, we convert the MOO problem into a single-object optimization problem using an elastic-constraint scalarization method. With this approach, we flexibly adjust trade-off parameters to choose between two objective functions. To propose an efficient algorithm, we deploy successive convex approximation (SCA) and complementary geometric programming (CGP) approaches. Finally, via simulation results we discuss how to select the values of trade-off parameters, and we study their effects on conflicting objective functions (i.e., throughput and operation cost in MOO problem). Simulation results also show that our proposed approach can offload traffic from C-RANs to FAPs with low transmit power and thereby reduce operation costs by switching off the under-utilized RRHs and BBUs. It can be observed from the simulation results that the proposed approach outperforms the traditional approach in which each user is associated to the AP (RRHs/FAPs) with the largest average value of signal strength. The proposed approach reduces operation costs by 30% and increases throughput index by 25% which in turn leads to greater energy efficiency (EE).

Cross Layer Resource Allocation in H-CRAN With Spectrum and Energy Cooperation

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in orthogonal frequency division multiple access (OFDMA)- non-orthogonal multiple access (NOMA) based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between a macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterward, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in OFDMA-NOMA based H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.

Multilayer Framework for Resource Orchestration in Next Generation Networks

Due to the significant increase in data traffic and the large number of Internet Protocol (IP) devices, operators and researchers are seeking solutions to address the greater demand. One of the most attractive of these is Heterogeneous Cloud Radio Access Networks (H-CRAN), which has the capacity to solve problems of the current generation and add several improvements, such as centralized processing and greater energy efficiency. However, resource orchestration such as radio, mapping between radio and BaseBand Unit (BBU) and load balance in BBU pool are still of the utmost importance. This paper proposes a multilayer approach that enables Peak Remote Radio Head (PRRH)-underutilized reconfiguration model and optimized mapping between PRRH and BBU, with the aim of achieving high availability, energy savings and a reduction in high-speed processing. Obtained results were compared with other approaches in the literature and showed that our model offers a more efficient means of mitigating the problems addressed in this paper.

Performance Analysis of Scalable User-Centric HC-RAN Over Rician Fading Channels

In this paper, we analyze the uplink performance of a scalable user-centric heterogeneous cloud-radio access network (HC-RAN) implemented by using dynamic cooperative clustering (DCC) framework over a Rician fading channel with phase shifts. This channel describes various practical aspects, such as a deterministic line of sight (LoS) component and random non-LoS (N-LoS) components. To account for the phase shifts due to user mobility, the phase of the LoS component is modeled as a uniformly distributed random variable. We assume that phase information is available at each remote radio head (RRH). We derived the phase aware-minimum mean square error (PA-MMSE) and phase unaware Linear-MMSE estimators and obtained the channel state information (CSI). We derived a closed-form expression for the achievable spectral efficiency (SE) to evaluate the system performance with both estimators. To address the effect of coherent interference in the ultra-dense networks, we developed a two-layer decoding scheme in uplink in which maximum ratio (MR) combining is performed at the RRH and large-scale fading decoding is performed at the base-band unit (BBU) pool. Based on the obtained results, the proposed method enhanced the uplink performance in an ultra-dense scenario. It is validated by comparing it with the simulation results. Moreover, the performance loss caused by the lack of phase knowledge will depend on the pilot sequence length.

Clustering Based Dynamic Bandwidth Allocation in HC-RAN

A wireless network is composed of several independent nodes or gadgets that communicate mutually through a wireless link. The most destructive challenge encountered in a wireless network is bandwidth allocation because it defines the amount the network will cost and how effectively it will function. The most cutting-edge network architecture in the present wireless communication system, cluster-based heterogeneous cloud radio access networks (HC-RANs), is what powers cloud computing in heterogeneous networks. In this research, we proposed an HC-RANs that may optimize energy consumption for wireless data transfer in the multi-hop device to device scenario. The proposed scheme offers bandwidth allocation in wireless environments where there are concerns about significant user mobility over the course of a given time. The above design, we used clustering with joint beam formation for the down link of heterogeneous cloud radio access network (HC-RAN), developed design to improved amount of FBS. Result outcomes helped in calculating Critical bandwidth usage (CBU).

Utility-Based Joint Power Control and Resource Allocation Algorithm for Heterogeneous Cloud Radio Access Network (H-CRAN)

The high density of H-CRAN associated with frequent UE handover may degrade the throughput. The infrastructure equipment like RRHs and BBUs consumes more energy to reduce UE energy consumptions. In this paper, we propose a utility-based joint power control and resource allocation (UJPCRA) algorithm for heterogeneous cloud radio access network (H-CRAN). In this framework, the power consumption of baseband units (BBUs), remote radio heads (RRHs), and macrocell base station (MBS) are estimated by predicting their dynamic loads. The data rate achievable for UE associated with each RRH and MBS on resource block RBk is then estimated. The user wishing to connect to a RRH or MBS then checks the corresponding utility with minimum expected energy consumption and the maximum expected data rate. If any UE with high priority traffic connected to MBS could not achieve its desired data rate requirements, then it can cooperatively seek the assistance of any RRH for assigning the balance RBs. The throughput may be enhanced by the high density of H-CRAN and frequent UE handover. Inter- and intracell interference causes the H-CRAN macrocells’ improved data rate to diminish. To lower UE energy consumption, infrastructure devices like RRHs and BBUs need more energy. As a result, there is a trade-off between operators and UE energy conservation. It is possible to determine the power consumption of BBUs, RRHs, and MBS using predictions of their dynamic loads. The UE may then forecast the data rate for each RRH and MBS on the resource block. When a user wishes to connect to an RRH or MBS, they look at the utility with the highest expected data rate and the least predicted energy usage first. A UE with high priority traffic connected to the MBS can cooperatively ask any RRH for assistance in allocating the remaining RBs if it is unable to achieve its intended data rate needs. Experimental results have shown that the proposed JRAUA algorithm achieves higher throughput, resource utilization, and energy efficiency with reduced packet loss ratio, when compared to the existing techniques.

Performance analysis of Heterogeneous Cloud-Radio Access Networks: A user-centric approach with network scalability

Heterogeneous cloud-radio access network (HC-RAN) is a very promising network architecture for future generation wireless communication systems. The centralized computation of HC-RAN provides flexibility to coordinate multi-point (CoMP) transmission of remote radio heads (RRHs). These RRHs can cooperatively form static or dynamic clusters which are network-centric or user-centric to serve the user equipment (UE). To meet the demands of future generation wireless networks, the user-centric approach is gaining much attention, especially in dynamic clustering. In dynamic user-centric CoMP, a set of overlapped clusters can cooperate dynamically to serve UEs. In a large network, the main challenge is the scalability of the computational complexity and fronthaul load with the increase of the number of UEs. To address this, we developed a scalable user-centric HC-RAN by utilizing dynamic cooperation clustering (DCC) framework. We presented an algorithm for joint user association, resource allocation, and cluster formation. We derived the expressions for different combining and precoding vectors to satisfy the scalability condition. We evaluated the performance of the scalable user-centric HC-RAN in terms of the achievable rate per UE at two different levels of cooperation with imperfect channel state information (CSI). The performance of the derived schemes is nearly optimal compared to the unscalable benchmark schemes and the ideal scalable system.

Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

AbstractIn this article, we propose an iterative algorithm based on lazy learning. The proposed scheme considers two consecutive algorithms for optimal resource allocation in heterogeneous cloud radio access networks (H‐CRAN). In the conventional approach, resources allocation is usually presented as a mathematical optimization problem and solved online using a real‐time scenario. Due to the non‐convex nature of resource allocation problems, finding optimal solutions in real‐time is a difficult challenge and has high computational complexity. To overcome these challenges in this paper, the problem is first formulated as a mixed‐integer nonlinear programming (MINLP) to optimize the system capacity and assign the underlay users to the subchannels, which is NP‐hard. To obtain the solution, in the first algorithm, the proposed problem is first reformulated as a convex problem using a matching theory and coalition formation method. Then, in the second algorithm, the lazy learning method is employed to find the optimal solution to the resources allocation problem. The lazy algorithm uses the output results of the first algorithm as training data, which are known as historical data. Similarities between current scenario data and historical scenario data, as well as the solution of resources allocation in historical scenarios, can be exploited to improve the resource allocation of the current scenario. Simulation results confirm that compared with the conventional approach, the suggested technique can significantly improve the system performance in terms of the total users assigned, overall throughput, the pace of resource allocation, and user fairness.

Artificial Bee optimization aided joint user association and resource allocation in HCRAN

Heterogeneous cloud radio access network (HCRAN) is a versatile network that leverages the heterogeneous base-station (BS) deployment scheme of Heterogeneous Networks (HetNets) and the centralized baseband signal processing functionalities of cloud RAN (CRAN). Due to dissimilarities between the traditional mobile systems and HCRAN, the currently available power-efficient networking mechanisms of traditional mobile systems cannot fully support HCRAN. Thus, these mechanisms are not feasible for the efficient reduction of energy consumption in HCRAN. In this paper, a multi-carrier orthogonal frequency division multiplexing (OFDMA) based joint user association and resource allocation problem is solved to optimize the HCRAN energy efficiency. The joint user association and resource allocation is a complex problem because the resource allocation to users depends on the RRH-User association. Therefore, a new mechanism is used to generate the initial random solution in the modified Artificial Bee Colony (ABC) algorithm and proposed to address the joint user association and resource allocation problem without decomposing it into two sub-problems. Simulation results show that the ABC algorithm provides a better energy efficiency improvement compared to the existing algorithms.

User relay‐aided energy‐efficient resource allocation in heterogeneous cloud radio access network

AbstractIn this article, we consider a user relay‐aided resource allocation problem for femtocell access points (FAPs) and macrocells to improve energy efficiency in heterogeneous cloud radio access networks. The formulated optimization problem is NP‐hard and mixed‐integer non‐linear programming due to the incredibly complex relation between different optimization variables and their impacts on each other. To solve this non‐convex and NP‐hard optimization problem in an effective and distributed method, we propose an efficient three‐step iterative algorithm. The solution approaches are derived from successive convex approximation and complementary geometric programming by considering maximum power, front‐haul link and baseband unit (BBU) capacity, Jain's fairness index, and quality of service constraints of macro and femtocell users. The simulation results depict that the proposed algorithm can effectively decrease the energy consumption of BBUs by switching off under‐utilized BBUs and offloading the traffic from remote radio heads and macro‐cell to FAPs.

Spectrum sensing and resource allocation for 5G heterogeneous cloud radio access networks

In this paper, the problem of opportunistic spectrum sharing for the next generation of wireless systems empowered by the cloud radio access network (C-RAN) is studied. More precisely, low-priority users employ cooperative spectrum sensing to detect a vacant portion of the spectrum that is not currently used by high-priority users. The authors' aim is to maximize the overall throughput of the low-priority users while guaranteeing the quality of service of the high-priority users. This objective is attained by optimally adjusting spectrum sensing time, with respect to target probabilities of detection and false alarm, as well as dynamically allocating C-RAN resources, that is, powers, sub-carriers, remote radio heads, and base-band units. To solve this problem, which is non-convex and NP-hard, a low-complex iterative solution is proposed. Numerical results demonstrate the necessity of sensing time adjustment as well as effectiveness of the proposed solution.

Power consumption analysis of access network in 5G mobile communication infrastructures — An analytical quantification model

Energy consumption growth of the fifth-generation (5G) mobile network infrastructure can be significant due to the increased traffic demand for a massive number of end-users with increasing traffic volume, user density, and data rate. The emerging technologies of radio access networks (RAN), e.g., millimeter-wave (mm-wave) communication and large-scale antennas, make a considerable contribution to such an increase in energy consumption. The multiband 2-tier heterogeneous network (HetNet), cloud radio access network (C-RAN), and heterogeneous cloud radio access network (H-CRAN) are considered the prospective RAN architectures of the 5G mobile communication. This paper explores these novel architectures from the energy consumption and network power efficiency perspective considering the varying high volume traffic load, the number of antennas, varying bandwidth, and varying density of low power nodes (LPNs), integrated with mm-wave communication and large-scale multiple antennas. The architectural differences of these networks are highlighted and power consumption analytical models that characterize the energy consumption of radio resource heads (RRHs), base band unit (BBU) pool, fronthaul, macro base station (MBS), and small cell base stations (SCBs) in HetNet, C-RAN, and H-CRAN are developed. The network power efficiency with the consideration of propagation environment and network constraints is investigated to identify the energy-efficient architecture for the 5G mobile network. The simulation results reveal that the power consumption of all these architectures increases in all considered scenarios due to an increase in power consumption of radio frequency components and computation power. Moreover, CRAN is the most energy-efficient RAN architecture due to its cooperative processing and decreased cooling and site support devices and H-CRAN consumes most of the energy compared to other 5G RAN architectures mainly due to a high level of heterogeneity.

Edge-Aware Remote Radio Heads Cooperation for Interference Mitigation in Heterogeneous C-RAN

Heterogeneous cloud radio access network (H-CRAN) has been deemed as a promising architecture for the fifth generation wireless networks. In H-CRAN, small remote radio heads (SRRHs) are deployed underlaying the macro remote radio head (MRRH) to boost the spectral efficiency. H-CRANs with dense RRHs suffer from severe inter-cell interference due to the random deployment of dense SRRHs, which limits its performance. In this paper, we propose an Edge-Aware RRH-Cooperation (EARC) scheme to mitigate the impact of both the cross-tier and co-tier interference. We partition the users into two groups, Cell Edge Device (CED) and Non-cell Edge Device (NED). These two groups, CED and NED, are mapped into two association modes, dual-association and single-association mode, respectively. In single-association mode, the NED associates with the RRH that provides the maximum received signal. On the other hand, in the dual-association mode, the CED associates with the two strongest RRHs where the two RRHs may belong to the same or different tier(s). Using tools from stochastic geometry, we quantify the performance gains of the proposed EARC scheme in terms of outage probability and ergodic rate. To further show the efficacy of the proposed scheme, we compare it to four schemes developed in the literature.

Optimizing Energy Efficiency for Supporting Near-Cloud Access Region of UAV-Based NOMA Networks in IoT Systems

Nonorthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for the wireless fifth generation (5G) networks and beyond. On the one hand, UAVs can be deployed as flying base stations to build line-of-sight (LoS) communication links to two ground users (GUs) and to improve the performance of conventional terrestrial cellular networks. On the other hand, NOMA enables the share of an orthogonal resource to multiple users simultaneously, thus improving the spectral efficiency and supporting massive connectivities. This paper presents two protocols, namely, cloud-based central station- (CCS-) based power-splitting protocol (PSR) and time-switching protocol (TSR), for simultaneous wireless information and power transmission (SWIPT) at UAV employed in power domain NOMA-based multitier heterogeneous cloud radio access network (H-CRAN) of Internet of Things (IoT) system. The system model with k types of UAVs and two users in which the CCS manages the entire H-CRAN and operates as a central unit in the cloud is proposed in our work. Closed-form expressions of throughput and energy efficiency (EE) for UAVs are derived. In particular, the EE is determined for the impacts of power allocation at CCS, various UAV types, and channel environment. The simulation results show that the performance for CCS-based PSR outperforms that for CCS-based TSR for the impacts of power allocation at the CCS. On the contrary, the TSR protocol has a higher EE than the PSR in the cases of the impact of various UAV types and channel environment. The analytic results match Monte Carlo simulations.

Joint beamforming and user association with reduced CSI signaling in mobile environments: A Deep Q-learning approach

Heterogeneous Cloud Radio Access Network (H-CRAN) is considered as a cost-efficient network solution to meet 5G data traffic requirements. In this paper, we consider the problem of beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have heterogeneous mobility profiles. Given the rapidly time-varying nature of the mobile wireless environment, it is challenging to offer an optimal beamforming and user association performance during a long-term allocation process without incurring large Channel State Information (CSI) and signaling overheads. For that purpose, we proposed in Ha et al. (2019) an Adaptive Beamforming and User Clustering (ABUC) algorithm which resolves the joint beamforming and user clustering problem when considering CSI cost and imperfectness under user mobility assumptions. In this paper, we design a deep reinforcement-learning framework which enables the proposed ABUC algorithm to select on-the-fly its best feedback scheduling parameters, namely the period and type of CSI feedback, given each user mobility profile. The proposed ABUC-DQL approach can overcome the scalability limitation of the Q-learning approach (Ha et al., 2019) and better handle the problem when formulated using a POMDP (Partially Observable Markov Decision Process) model. The simulation results show that the convergence time is mainly impacted by the number of users in the network, and the online-learning ability of the framework can quickly adapt to the changes of users mobility.

Computing-Aware Base Station Sleeping Mechanism in H-CRAN-Cloud-Edge Networks

In this paper, a power minimization problem using base station sleeping is proposed for heterogeneous cloud radio access networks (H-CRANs) taking into account the computing delay constraints. In the proposed system, which is modeled using M/M/k queues, the edge device coexists with the small base station (SBS) to provide computing capabilities beside the central cloud. In general, the SBS sleeping is governed by the availability of resources provided the macro base station (MBS) which is in charge of accommodating offloaded users from sleeping SBSs. However, switching off lightly loaded SBSs can impose significant burdens on cloud servers. Here, the proposed sleeping scheme allows SBSs serving more computing tasks to remain active in order to fulfill the task completion deadlines requested by mobile users and to keep the cloud response time within a predefined limit. In other words, the proposed scheme aims to save power by undertaking a centralized selection of active and sleeping SBSs taking into account the delay constraints of both cloud and mobile devices. First, we consider a disjoint cloud-edge system, where computing services can be provided by either the cloud or the edge device, and aim to minimize the number of active SBSs. The problem is formulated as a 0-1 knapsack problem with SBS utilization considered as the weight while the ratio of computing tasks to all incoming tasks is considered as the value of that SBS. In this problem, which is solved using dynamic programming, SBSs processing less computing tasks are given higher values; and as a result, higher chance to sleep compared to others. Second, a shared computing system is proposed whereby active SBSs (edge devices) contribute to the total computing capability. Here, an exhaustive search approach is used to achieve the optimal power saving. We also proved that the shared computing system performs better in terms of response time compared to the disjoint system depending on the number of active SBSs.