Radio resource coordination and scheduling scheme in ultra-dense cloud-based small cell networks

Coordinated multipoint (CoMP) transmission has been considered an important technology to manage the serious inter-cell interference in ultra-dense small cell networks (UDN). In this paper, a scheduling framework and a two-tier spectrum resource allocation method for CoMP transmissions in UDNs are proposed. For a number of clusters, the tier-one allocation divides the resource blocks (RBs) into two sets: one is for center users and the other is for edge users. Tier-two resource allocation is performed at a central scheduler to allocate spectrum resources to edge users on a cluster basis. Applying the two-tier scheduling method to a simulated UDN on OpenStack and OpenAirInterface (OAI) software platforms, the UE throughput performances are presented and discussed. We further analyze the computational complexity of each step for performing 1-slot CoMP transmission. The numerical simulation results showed that, upon deploying the small cells in a Poisson Point Process (PPP) with mean density 0.0003/km2, and the ratio of eNBs to UEs is (3:1), more than 50% UEs have 15Mbps throughput performance.

To meet the 5G requirements in terms of high capacity and enhanced quality of service, the Cloud Radio Access Network (C-RAN) relies on the network densification and coordination. The main idea behind the C- RAN is to split the base station into two parts: the first component is the Remote Radio Head (RRH) which will be responsible for all Radio transmissions and the second one is the Base Band Unit (BBU) which will be responsible for the processing and control. Only the RRH will be densified to offer more capacity. The processing resources will be pooled and clustered so that many RRHs will be managed by one BBU pool which will reduce the related CAPEX and OPEX of the network's deployment, operation and maintenance. The main goal of this paper is to propose an optimized clustering algorithm able to improve interference management between BBU clusters and decrease the network mobility signaling cost. In this work, we will be interested by the implementation and optimization of local search algorithm (Tabu Search) and evolution algorithm (Genetic Algorithm) as well as the combination of both methods in order to enhance the convergent solution.

In this paper, the optimization of energy efficiency (EE) and the improvement of user data rate in cloud radio access networks (C-RANs) are studied by introducing a weighted sparse beamforming (WSB) method. Using stochastic geometry tools, analytical expressions are derived to describe downlink ergodic rate and coverage probability for coordination multipoint joint transmission in C-RAN. Based on these expressions and by taking advantage of central processing and coordination in baseband unit pool, power allocation among remote radio heads is optimized using the WSB. The WSB optimization algorithm is based on water-filling power allocation strategy and redistributing the power among RRHs. The improvement in both EE and ergodic rate is achieved. The improvement in EE is pronounced up to 28% using WSB technique compare to the equal power beamforming method in the proposed C-RAN network.

Cloud radio access networks (C-RAN) is a potential solution to meet the traffic demand of the future network, which migrates the baseband units (BBU) to the BBU pool in the cloud. The connection between BBU and remote radio head (RRH) can be dynamically changed and the resources can be allocated more flexible according to some factor, e.g., traffic demand of RRHs. Therefore, the resource allocation scheme in C-RAN is redefined, which regards the BBUs as a sharing resource. Existing studies execute the mapping scheme with the consideration of the single BBU but not the whole network, e.g., the BBU with high utilization switches the supported RRH to other BBU to reduce the resource usage. To achieve this goal, this paper makes use of the concept of cloud resources and centralized management to fully utilize the resources and allocate the resources more centralized. The main contribution of this paper is to propose a dynamic BBU–RRH mapping scheme using a borrow-and-lend approach to dynamically allocate the resources to RRHs in a clever and efficient way. With the centralized management, the max busy group borrows the resource for RRH switching, whereas the min helper group lends the resource to handle the switching RRHs. Finally, simulation results illustrate the proposed mechanism in C-RAN significantly reduces the waste of the resource usage and improves the throughput.

The following topics are dealt with: MIMO communication; array signal processing; radiofrequency interference; probability; wireless channels; iterative methods; OFDM modulation; optimisation; channel estimation; precoding.

Cloud radio-access network (C-RAN) has the most promising network architecture to support the new fifth generation (5G) mobile network. The C-RAN witnesses a revolutionary base station functionality split, where it is composed of simplified remote radio heads (RRH) and several baseband units (BBUs) pools in network backbone. The C-RAN assists in employing several functionalities more efficiently. It has privileges over the existing radio-access networks (RANs) in case of global resource management, interference management, and radio-access technology (RAT) selection to guarantee higher system throughput and lower energy consumption. In this paper, we demonstrate a C-RAN system-level simulator that performs centralized user scheduling, edge-user joint transmission (JT), and global per-antenna carrier aggregation (CA). In addition, we demonstrate the capability of our system-level simulator to investigate the bottleneck effect of the fronthaul-link capacity as the system expands or C-RAN coordinated facilities increase. Our analysis shows throughput enhancement compared to the traditional RAN; however, with high fronthaul links utilization rates.

In this paper we consider a cloud radio access network (C-RAN) where the remote radio heads (RRHs) are separated from the baseband signal processing servers, named baseband units (BBUs). The latter forms a centralized pool of high-performance data center resources. To benefit from network function virtualization, we consider virtualized BBU (V-BBU) resources where the BBU functionality and services have been virtualized in the form of virtual network functions. All RRHs in the C-RAN form a single cluster. Each RRH of such a cluster may accommodate random or quasi-random traffic. That means that new calls in a RRH can be generated by an infinite number of mobile users (random traffic) or by a finite number of mobile users (quasi-random traffic). An arriving call requires a radio resource unit from the serving RRH and a computational resource unit from the V-BBU. If these resource units are available, then the call is accepted and remains in the system for a generally distributed service time. Otherwise, the call is blocked and lost. In order to analyze this C-RAN we model it as a loss system and study two cases: (i) all RRHs accommodate quasi-random traffic and (ii) some RRHs accommodate random traffic and the rest accommodate quasi-random traffic. In both cases, we show that the steady state probabilities have a product form solution and propose convolution algorithms for the accurate determination of the main teletraffic performance measure which is congestion probability. The accuracy of these algorithms is verified via simulation.

Cloud radio access network (CRAN) is a promising candidate for the next-generation wireless communication systems. In CRAN, remote radio heads (RRHs) are deployed to serve users in a target area, which are connected to a central processor (CP) via limited-capacity links termed the fronthaul. In practice, the large amount of information transferred between each RRH and the CP for centralized processing can easily exceed its fronthaul capacity. This motivates our study in this paper on a new hybrid decoding scheme here the RRHs can either locally decode-and-forward the user messages to save the fronthaul capacity, or quantize and forward their signals to the CP for joint decoding as in the conventional CRAN (i.e., forward-and-decode). We consider the uplink transmission in an orthogonal frequency division multiplexing (OFDM)-based CRAN with multiple RRHs to serve a single user, where the proposed hybrid decoding is performed on each OFDM sub-channel (SC). We consider a joint optimization of the processing mode selections (decode-and-forward or forward-and-decode) along with the user's power allocation over all SCs to maximize the sum-rate for the user subject to the RRHs' individual fronthaul capacity constraints and the user's total power constraint. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We also compare the performance of the proposed hybrid decoding with existing schemes that perform either decode-and-forward or forward-and-decode processing at all SCs, which shows promising throughput gains.

A novel architecture design for Radio Access Networks (RANs) to address critical elements in resource management and to achieve the 5G mobile network requirements is needed. Cloud Radio Access Network (C-RAN) and Software Defined Networking (SDN) / Network Function Virtualization (NFV) technologies are recognized as key enabling solutions for the future mobile networks. C-RAN is a new mobile network architecture, characterized by a redistribution of functionalities. More specifically, the baseband processing resources are centralized in remote locations, known as Base Band Unit (BBU) Pool, whereas the radio functionalities, known as Remote Radio Head (RRH), remain in edge locations. SDN focuses on decoupling control and data plane, while NFV performs the functionalities abstraction from the underlying hardware. In this paper, a hierarchical layered software-defined architecture for future 5G networks is proposed. We deploy a Next Generation Fronthaul Interface (NGFI) based architecture, in order to overcome issues related to massive MIMO deployment. Furthermore, in order to implement dynamically different functional splitting options, a virtualized Remote Aggregation Unit (RAU) is proposed. As further contribution, our solution aims to provide new features that facilitate SDN/NFV integration in wireless/mobile environments.

Cloud radio access network (CRAN), in which remote radio heads (RRHs) are deployed to serve users in a target area, and connected to a central processor (CP) via limited-capacity links termed the fronthaul, is a promising candidate for the next-generation wireless communication systems. Due to the content-centric nature of future wireless communications, it is desirable to cache popular contents beforehand at the RRHs, to reduce the burden on the fronthaul and achieve energy saving through cooperative transmission. This motivates our study in this paper on the energy efficient transmission in an orthogonal frequency division multiple access (OFDMA)-based CRAN with multiple RRHs and users, where the RRHs can prefetch popular contents. We consider a joint optimization of the user-SC assignment, RRH selection and transmit power allocation over all the SCs to minimize the total transmit power of the RRHs, subject to the RRHs' individual fronthaul capacity constraints and the users' minimum rate constraints, while taking into account the caching status at the RRHs. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We compare the minimum transmit power required by the proposed algorithm with different caching strategies against the case without caching by simulations, which show the significant energy saving with caching.

In this paper, we analyze the performance of a cloud radio access network (CRAN), consisting of multiple randomly distributed remote radio heads (RRHs) and a macro base station (MBS), each equipped with multiple antennas. To model the spatial distribution of RRHs and analyze its performance, we use stochastic geometry tools. In contrast to previous works on CRAN that consider Poisson Point Process (PPP) model for the spatial distribution of RRHs, we consider a more realistic Matern hard-core point process (MHCPP) model that imposes a certain minimal distance (referred to as hard-core distance) between the two RRHs so that the RRHs are not too close to each other. To compare system performance of CRAN when different transmission strategies are used, three RRH selection schemes are adopted including 1) the best RRH selection (BRS); 2) all RRHs participation (ARP); and 3) nearest RRH selection (NRS). Considering downlink transmission, the ergodic capacity, outage probability, and system throughput of CRAN are analytically characterized for different RRH selection schemes. The presented results demonstrate that compared to PPP model, the increase in hard-core distance will result in a higher outage probability and cause a negative impact on ergodic capacity. Furthermore, when the same total transmit power is consumed, BRS scheme provides the best outage performance while ARP scheme is the best RRH selection scheme when the same transmit SNR at each RRH is assumed. Moreover, it is shown that the hard-core distance has a more significant impact on systems with higher intensity of PPP distributed candidate points and in large hard-core distance regime increasing the intensity of candidate points can only provide a small improvement in outage performance. We extend our work to multiuser case with zero-forcing (ZF) precoding where it is proven that the results in multiuser case reduce to the derived results in this work by substituting K=1 for single-user.

SummarySmall cell networks can fulfill the increasing demandfor the high data rate of wireless applications. Energy efficiency is an important design parameter of the ultra dense small cell network (UDSCN). The sleeping strategy of small base stations (s‐BSs) is used to enhance the network's energy efficiency. An efficient sleeping strategy of s‐BSs is required while preserving users' quality of service (QoS). The idle s‐BSs can be switched to sleep mode. This paper proposes a blockchain‐enabled solution for the sleeping strategy of s‐BSs. Here, a blockchain‐enabled small cell network is created between the s‐BSs. The network is decentralized, which eliminates the workload of the macro base station (MBS). The proposed network architecture is enabled as a decentralized network through blockchain. The blockchain provides distributed control over the s‐BS operations through a smart contract. Here, smart contracts act as distributed self organizing network features to handle self‐transactions among small cells for switching off s‐BSs in the network. All the software logic required to perform s‐BS operations is written in a smart contract using Ethereum. The proposed solution improves energy efficiency and enables the ultra dense small cell network to be decentralized.

Cloud radio access network (C-RAN) has emerged as a promising solution to support exponentially increasing demand in data rate. The attractive capacity enhancement mainly comes from centralized and coordinated processing, which poses great challenges on computing capability in the baseband unit pool. This requires the efficient allocation of computing resources to minimize the hardware and energy costs of C-RANs. Therefore, in this letter, we first model the computing resource consumption of joint downlink transmissions from remote radio heads (RRHs) to users. Then, we investigate the computing resource minimization problem on how much computing capability is needed given certain number of RRHs and user density. Numerical results show that the computing resource consumption increases non-linearly with the user density. In particular, the required computing resource drastically increases when densely populated hotspots are present in the system.

The surging mobile traffic poses serious challenges for mobile operators, one of which is the unsustainable growth caused by the high energy consumption of the massively deployed base stations (BSs). Cloud radio access network (CRAN), as a new architecture, is proposed to confront this challenge. By isolating baseband unit (BBU) from its remote radio head (RRH) in BS, the BBUs are consolidated into a common place (i.e., BBU pool). Since the “any-to-any” connection between BBUs and RRHs is realized in CRAN, low utilized BBUs can be switched to sleep mode to save energy during traffic valley, which can effectively reduce the energy consumption of CRAN. However, when a BBU enters into the sleep mode, RRHs connected with this BBU must be switched to another BBU, which would degrade the quality of service (QoS) for the RRHs. In this paper, to simultaneously guarantee low BBU energy consumption and low RRH traffic migration, both of which are interrelated and mutual restraint, we propose a deep reinforcement learning (DRL) based BBU aggregation scheme. Furthermore, to train the DRL fast and well, we introduce several heuristic algorithms to assist the DRL training. Extensive numerical evaluations show that our proposed heuristic-assisted DRL (HA-DRL) can guarantee both the low power consumption and the less traffic migration. When compared with the baselines, HA-DRL can achieve up to 18.3% cost reduction and 32.8% migrated traffic reduction with at most 8.4% higher energy consumption and attains the lowest cost for all cases considered.

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.