Number Of Remote Radio Heads Research Articles

DC-RAN dynamic coverage methods to improve its performance

Abstract
 In cellular networks, there are usually times when the density of the network changes at different times of the day, where congested areas attract a large demand for communications and data, which generates peak demand at specific times of the day, and vice versa also happens in the same area but at another time of the day, and in order to meet the needs of this changing demand the network must keep pace with changes in the density of the areas served. This can only be done if the network has a dynamic ability to handle the change in traffic density and demand intensity, which requires turning on/off a certain number of Remote Radio Heads (RRHs) that represent the Radio Frequency (RF) front end of the mobile network. For the purpose of providing a reliable and acceptable telecommunications service to users and financially useful to network operators by rationing energy consumption and meeting the required needs, dynamic 5G networks can do this by relying on a Dynamic Cloud Radio Access Network (DC-RAN) that enables them to respond to demand variables by shifting several types of coverage cells into a single serviced area and stopping them when needed.

Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective

Currently, 5G and the forthcoming 6G mobile communication systems are the most promising cellular generations expected to beat the growing hunger for bandwidth and enable the fully connected world presented by the Internet of Everything (IoE). The cloud radio access network (CRAN) has been proposed as a promising architecture for meeting the needs and goals of 5G/6G (5G and beyond) networks. Nevertheless, the provisioning of cost-efficient connections between a large number of remote radio heads (RRHs) in the cell sites and the baseband unit (BBU) pool in the central location, known as the fronthaul, has emerged as a new challenge. Many wired and wireless solutions have been proposed to address this bottleneck. Specifically, optical technologies presented by passive optical networks (PONs) are introduced as the best suitable solution for 5G and beyond network fronthaul due to their properties of providing high capacity and low latency connections. We considered time and wavelength division multiplexed passive optical networks (TWDM-PONs) as a fronthaul for 5G and beyond. Taking that into consideration, in this paper, we propose an integer linear program (ILP) that results in the optimal optical fronthaul deployment while minimizing the total cost of 5G and beyond instances. However, for larger network instances, solving the ILP problem becomes unscalable and time-consuming. To address that, we developed two heuristic-based algorithms (the K-means clustering algorithm and the one based on the genetic algorithm-GA). We evaluated the suitability of our proposed ILP and heuristic algorithms in simulations by utilizing them to plan different network instances (dense and sparse).

Analysis of an active and passive optical fronthaul with dense wavelength division multiplexing for coexisting 4G/5G cloud radio access network architectures based on software defined radio

We experimentally investigate the performance of an active and passive optical fronthaul to be employed in a cloud/centralized radio access network (C-RAN) architecture based on software-defined radio (SDR) for 4G systems, and we discuss the viability to integrate these fronthaul configurations in coexisting 4G/5G systems based on SDR by considering the reconfigurable characteristics of SDRs and the capacity and latency characteristics of the optical fronthaul. The active optical fronthaul consists of two transponders employing dense wavelength division multiplexing (DWDM), whereas the passive optical fronthaul consists of a point-to-point scheme based on DWDM only. To perform a comparative study between the active and passive optical fronthaul in the C-RAN architecture, we have measured the throughput (bit rate) generated via iPerf® for uplink and downlink transmissions at different DWDM channels, and we analyze the losses and latency presented in both optical fronthaul schemes. We found that for different optical fronthaul lengths up to 21.76 km, the bit rate is practically the same for the passive and active fronthaul despite the higher optical losses present in the passive fronthaul in comparison to the active fronthaul. In addition, the latency among active and passive fronthaul are almost similar with an estimated increment of 20 μs in the active fronthaul. These results are independently of the number of remote radio heads (RRHs) and user equipment (UEs) considered in the C-RAN architecture. Our findings put forward the proposed DWDM passive fronthaul as a viable, less complex, and cost-sensitive solution for C-RAN systems with fronthaul lengths up to 21.76 km. These results and all the experiences reported on the C-RAN implementation provide valuable information to design and develop 4G and 5G C-RAN architectures based on SDR with the capability to operate in a DWDM optical fronthaul infrastructure.

Topological Pilot Assignment in Large-Scale Distributed MIMO Networks

We consider the pilot assignment problem in large-scale distributed multi-input multi-output (MIMO) networks, where a large number of remote radio head (RRH) antennas are randomly distributed in a wide area, and jointly serve a relatively smaller number of users (UE) coherently. By artificially imposing structures on the UE-RRH connectivity, we model the network by a partially-connected interference network, so that the pilot assignment problem can be cast as a topological interference management problem with multiple groupcast messages. Building upon such connection, we formulate the topological pilot assignment (TPA) problem in two different ways with respect to whether or not the to-be-estimated channel connectivity pattern is known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> . When it is known, we formulate the TPA problem as a low-rank matrix completion problem that can be solved by a simple alternating projection algorithm. Otherwise, we formulate it as a sequential maximum weight induced matching problem that can be solved by either a mixed integer linear program or a simple yet efficient greedy algorithm. With respect to two different formulations of the TPA problem, we evaluate the efficiency of the proposed algorithms under the cell-free massive MIMO setting.

Coverage and Spectral Efficiency of Network Assisted Full Duplex in a Millimeter Wave System

To cope with the growing trend of asymmetric data traffic, we introduce a novel network assisted full duplex (NAFD) for a millimeter wave system. NAFD can dynamically allocate the number of remote radio heads in the uplink mode or in the downlink mode, which can facilitate simultaneous uplink and downlink communications. In this manuscript, we use stochastic geometry to analyze the distribution of the signal-to-interference-plus-noise ratio and the data rate in a NAFD system. The numerical results verify the analysis and show that the NAFD outperforms the dynamic time division duplex system and the traditional flexible duplex system in terms of spectral efficiency.

End-to-end CNN-based dueling deep Q-Network for autonomous cell activation in Cloud-RANs

The fifth generation (5G) technology is expected to support a rapid increase in infrastructure and mobile user subscriptions with an increase in the number of remote radio heads (RRHs) per unit area using cloud radio access networks (C-RANs). From the economic point of view, minimizing the amount of energy consumption of the RRHs is a challenging issue. From the environmental point of view, achieving “greenness” in wireless networks is one of the many goals of telecommunication operators. This paper proposes a framework to balance the energy consumption of RRHs and quality of service (QoS) satisfaction of users in cellular networks using a convolutional neural network (CNN)-based relational dueling deep Q-Network (DQN) scheme. Firstly, we formulate the cell activation/deactivation problem as a Markov decision process (MDP) and set up a two-layer CNN which takes raw captured images in the environment as its input. Then, we develop a dueling DQN-based autonomous cell activation scheme to dynamically turn RRHs on or off based on the energy consumption and QoS requirements of users in the network. Finally, we decouple a customized physical resource allocation for rate-constrained users and delay-constrained users from the cell activation scheme and formulate the problem as a convex optimization problem to ensure the QoS requirements of users are achieved with the minimum number of active RRHs under varying traffic conditions. Extensive simulations reveal that the proposed algorithm achieves faster rate of convergence than nature DQN, Q-learning and dueling DQN schemes. Our algorithm also achieves stability in mobility scenarios compared with DQN and dueling DQN without CNN. We also observe a slight improvement in balancing energy consumption and QoS satisfaction compared with DQN and dueling DQN schemes.

Delay-Optimal Joint Processing in Computation-Constrained Fog Radio Access Networks

With fog radio access networks (F-RANs), the computation capability is provided in the physical proximity of the users, which can significantly lower the delay and mitigate the heavy traffic over backhaul links. As the number of remote radio head (RRH) increases, the computational complexity becomes a severe issue and it is necessary to group the RRHs into multiple clusters. In this paper, we optimize the joint processing strategy, including the RRH clustering and the RRH-server matching to minimize the delay for F-RANs. We model the delay-optimal joint processing problem in computation-constrained F-RANs as a Markov decision process (MDP) problem. By deriving the optimality condition of this MDP, we obtain a per-slot weighted sum rate maximization problem, in which the RRH clustering and the RRH-server matching are solved jointly. Specifically, we transform the weighted sum rate maximization problem into a combinatorial auction problem (CAP). Since the CAP is NP-hard, we greedily obtain an initial solution of CAP and gradually improve the performance by adopting local α-good improvement algorithm based on the weighted independent set problem (WISP). Furthermore, we theoretically derive the bound of the performance of the proposed algorithm.

Energy Efficient Resource Allocation for Energy Harvesting Aided H-CRAN

With an exponentially increasing number of users and their data rate demands, current 4G cellular networks need to be evolved to next fifth-generation networks. A heterogeneous cloud radio access network (H-CRAN) is the promising architecture for future high data rate enabled, energy efficient networks. The H-CRAN differs from today’s cellular system by addition of an extra number of remote radio heads (RRHs) within the vicinity of one macro base station. This provides high data rates to users with minimized interference by centrally controlling the resource allocation. On the other hand, increased density of hardware in the area, the H-CRAN also consumes more grid power of the system. To mitigate the greater power requirements for this type of a dense network, energy harvesting (EH) techniques are used to minimize the consumption of grid power. In EH, energy is harvested from ambient sources, such as solar and wind. By maximizing the harvested energy usage instead of grid power, the system’s energy efficiency (EE) can be improved significantly. In this paper, the EE of an H-CRAN consisting of several green RRHs powered by EH modules is explored. A mixed integer non-linear programming problem is formulated, which maximizes the EE of the system. A mesh adaptive direct search algorithm is used to optimize the problem. As a result of this optimization, efficient power and resource allocation are done and higher EE is achieved with low complexity and lower consumption of grid power.

Channel-Aware Resource Allocation for Energy-Efficient Cloud Radio Access Networks Under Outage Specifications

This paper investigates a cloud radio access network (C-RAN) architecture for future wireless network, which focuses on centralized processing for spatially distributed remote radio heads (RRHs). Our main goal is to promote an energy efficient C-RAN under the consideration of multiple access interference (MAI) and imperfect channel state information at the transmitter (CSIT) via outage-aware resource allocation. The closed-form expression of fading-induced outage probability for each transceiver pair will be established by analyzing the statistical property of signal-to-interference-plus-noise. In the course of finding feasible solutions to enhance energy efficiency, the optimization procedures named C-RAN-based energy efficient power allocation (CEEPA) and low-complexity CEEPA are proposed. As the number of RRHs grows to infinity, the deterministic equivalents of performance metrics can be derived by applying recent results from random matrix theorem, which lead to an efficient way to obtain the asymptotic-optimal resource allocation policy. Also, the regularized zero-forcing precoding technique is adopted to mitigate MAI and can tackle the impact of imperfect CSIT thanks to the derivation of deterministic equivalents. Numerical simulations show that the proposed resource allocation schemes can provide better energy efficiency and the accuracy of asymptotic expressions is also verified. Furthermore, the merit of a distributed antenna system is demonstrated through the comparison with centralized antenna system.

Fronthaul-Limited Uplink OFDMA in Ultra-Dense CRAN With Hybrid Decoding

In an ultra-dense cloud radio access network (UD-CRAN), a large number of remote radio heads (RRHs), typically employed as simple relay nodes, are distributed in a target area, which could even outnumber their served users. However, one major challenge is that the large amount of information required to be transferred between each RRH and the central processor (CP) for joint signal processing can easily exceed the capacity of the fronthaul links connecting them. This motivates our study in this paper on a new hybrid decoding scheme where in addition to quantizing and forwarding the received signals for joint decoding at the CP, i.e. forward-and-decode (FaD) as in the conventional CRAN, the RRHs can locally decode-and-forward (DaF) the user messages to save the fronthaul capacity. In particular, we consider the uplink transmission in an orthogonal frequency division multiple access (OFDMA)-based UD-CRAN, where the proposed hybrid decoding is performed on each OFDMA sub-channel (SC). We study a joint optimization of the processing mode selections (DaF or FaD), user-SC assignments and the users' transmit power allocations over all SCs to maximize their weighted-sum-rate subject to the RRHs' individual fronthaul capacity constraints and the users' individual power constraints. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. Further, we propose a low-complexity greedy algorithm which is shown to achieve close to the optimal performance under practical setups. Simulation results show the promising throughput gains of the proposed designs with hybrid decoding, compared to the existing schemes that perform either DaF or FaD at all SCs.

The Distributed MIMO Scenario: Can Ideal ADCs Be Replaced by Low-Resolution ADCs?

This letter considers the architecture of a distributed antenna system, which is made up of a massive number of single-antenna remote radio heads (RRHs), some with full-resolution but others with low-resolution analog-to-digital converter (ADC) receivers. This architecture is greatly motivated by its high energy efficiency and low-cost implementation. We derive the worst-case uplink spectral efficiency (SE) of the system assuming a frequency-flat channel and maximum-ratio combining, and reveal that the SE increases as the number of quantization bits for the low-resolution ADCs increases, and the SE converges as the number of RRHs with low-resolution ADCs grows. Our results furthermore demonstrate that a great improvement can be obtained by adding a majority of RRHs with low-resolution ADC receivers, if sufficient quantization precision and an acceptable proportion of high-to-low resolution RRHs are used.

TDM-PON for Accommodating TDD-Based Fronthaul and Secondary Services

A time division multiplexing-passive optical network (TDM-PON) has been attracting attention with a view to realizing the cost-effective mobile fronthaul (MFH) of a mobile base station (BS). A cost-effective MFH is needed because a lot of BSs will be densely deployed in fifth generation mobile networks (5G). A TDM-PON is capable of reducing the network cost as the number of subscribers increases. However, the number of remote radio heads that can be accommodated in a TDM-PON is limited since a 5G MFH has a throughput of more than 1 Gbps. The accommodation of not only the MFH but also multiple services will enable us to construct a more cost-effective network that will in turn achieve cost-effective 5G MFH. Therefore, we propose the accommodation of the fronthaul and secondary services in a TDM-PON. Our proposed TDM-PON allows us to increase the number of accommodated ONUs. We use a traffic monitor to capture the MFH signal and then estimate the unallocated interval in the PON domain by using the characteristics of the MFH signal. The signals of the secondary services are inserted in the unallocated interval of the MFH signal. We estimate the unallocated interval by using this characteristic. In this paper, we report experimental and the theoretical results for our proposed technique obtained with a 10 Gigabit-Ethernet PON prototype. Moreover, we also discuss the variation in throughput when we increase the number of ONUs.

Energy-Efficient BBU Allocation for Green C-RAN

Cloud radio access network (C-RAN) separates the baseband processing from the radio transmissions in the base station. The baseband processing is done at the baseband unit (BBU), centrally for a huge number of remote radio head (RRH). The BBU pool works virtually and significantly to reduce power consumption in the cellular networks. Efficient coordination among these BBUs can improve network performance in terms of resource utilization and power consumption. In this letter, we propose a BBU and RRH association scheme based on graph partitioning and rejoining to minimize the power consumption due to communication overhead. Simulation results show that the proposed algorithm improves power consumption up to 20% and reduces handover overhead up to 30% compared with the traditional approach.

How Much Computing Capability Is Enough to Run a Cloud Radio Access Network?

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.

Joint Design of Fronthaul and Access Links for C-RAN With Wireless Fronthauling

This letter studies a joint design of fronthaul and radio access links for a cloud radio access network (C-RAN) with wireless fronthauling, where a baseband unit (BBU) controls a number of remote radio heads (RRHs) via wireless fronthaul links to communicate with user equipments. We first review a basic approach based on a single cell concept, whereby the RRHs operate as decode-and-forward (DF) relays. Then, a cooperative transmission from the RRHs based on decompress-and-forward (DCF) relaying is proposed, which is a standard concept in C-RAN. For both strategies, a problem of jointly optimizing the BBU and RRH operations is tackled with the goal of maximizing the weighted sum-rate subject to the BBU and per-RRH power constraints. For each formulated problem, an iterative algorithm is derived that achieves a sequence of monotonically nondecreasing objective values at each iteration. It is confirmed via numerical results that the DCF-based cooperative scheme significantly outperforms the DF-based single-cell approach.

Inter-Tier Interference Suppression in Heterogeneous Cloud Radio Access Networks

Incorporating cloud computing into heterogeneous networks, the heterogeneous cloud radio access network (H-CRAN) has been proposed as a promising paradigm to enhance both spectral and energy efficiencies. Developing interference suppression strategies is critical for suppressing the inter-tier interference between remote radio heads (RRHs) and a macro base station (MBS) in H-CRANs. In this paper, inter-tier interference suppression techniques are considered in the contexts of collaborative processing and cooperative radio resource allocation (CRRA). In particular, interference collaboration (IC) and beamforming (BF) are proposed to suppress the inter-tier interference, and their corresponding performance is evaluated. Closed-form expressions for the overall outage probabilities, system capacities, and average bit error rates under these two schemes are derived. Furthermore, IC- and BF-based CRRA optimization models are presented to maximize the RRH-accessed users’ sum rates via power allocation, which is solved with convex optimization. Simulation results demonstrate that the derived expressions for these performance metrics for IC and BF are accurate, and the relative performance between IC and BF schemes depends on system parameters such as the number of antennas at the MBS, the number of RRHs, and the target signal-to-interference-plus-noise ratio threshold. Furthermore, it is seen that the sum rates of IC and BF schemes increase almost linearly with the transmit power threshold under the proposed CRRA optimization solution.

Robust Low-Complexity MMSE Precoding Algorithm for Cloud Radio Access Networks

In this paper, the robust transceiver design for multi-cell distributed antenna systems is investigated, which is of great importance for the future mobile communication network architecture namely cloud radio access networks (C-RAN). In order to make the algorithm suitable for the multi-cell network with a large number of remote radio heads (RRHs), a low complexity robust design is proposed based on alternating direction method of multipliers (ADMM) algorithm. Exploiting the elegant properties of ADMM algorithm, the complicated relationship between the optimization objective and the per-antenna power constraint is decoupled by introducing an auxiliary variable. Meanwhile the complexity of the proposed algorithm is also significantly reduced. Finally, it is demonstrated by the simulation that the proposed robust algorithm has better performance than the existing non-robust design.

Location‐assisted clustering and scheduling for coordinated homogeneous and heterogeneous cellular networks

ABSTRACTThe multiple‐input multiple‐output (MIMO) downlink with transmitter coordination in a cellular network is considered. The transmitters are assumed to be either neighbouring base stations (homogeneous) or a base station with a number of remote radio heads that form picocells in its coverage area (heterogeneous). In centralized coordinated transmission from a cluster of nodes, the channel state information (CSI) of users needs to be sent to a central processor for precoding and resource allocation. Real‐time CSI feedback from the users to their home base station and from the base stations to the central processor is a serious challenge from a practical point of view. In this work, efficient location‐assisted limited‐feedback schemes for homogeneous and heterogeneous cellular networks are proposed. First, a hybrid mode transmission scheme with reduced feedback requirement is proposed for a homogeneous network, in which on the basis of the location of users, some are served using a single‐cell multiuser MIMO approach and some using a network MIMO approach. Next, for a heterogeneous network, a location‐assisted clustering and scheduling scheme is proposed for the case of joint reference signals, in which multiple transmission nodes that share the reference signals cannot be distinguished from each other. We evaluate the performance of our schemes with a series of simulations. In the homogeneous scenario, we compare with the case of full CSI, and in the heterogeneous scenario, we compare with joint transmission from all nodes in a cell. Copyright © 2012 John Wiley &amp; Sons, Ltd.