Backhaul-aware multicell beamforming for downlink cloud radio access network

Physical layer (PHY) security is recently regarded as a promising technique to improve the security performance of wireless communication networks. Current developments in PHY security are often based on the assumption of perfect channel state information (CSI). In this paper, both security and reliability performance for the downlink cloud radio access network with optimal remote radio heads (RRHs) node selection are investigated in a practical scenario by considering channel estimation (CE) errors. In particular, a three-phase transmission scheme is proposed and the linear minimum mean-square error (MMSE) estimation method is utilized to obtain the CSI. Based on the CSI estimates and the statistics of CE errors, the outage probability and intercept probability are derived in closed-form expression to evaluate the security and reliability performance, respectively. In addition, two possible cases (with or without intercepting signals from baseband unit) are considered for the eavesdropper. It is found that the suggested optimal RRHs selection scheme outperforms the nonselection scheme, and that the increasing number of RRHs can lower the outage probability as well as the intercept probability. It is also shown that there exists an optimal training number to minimize the sum of the outage probability and intercept probability. Finally, simulation results are provided to corroborate our proposed studies.

In this paper, the power allocation (PA) for maximizing sum rate in downlink cloud radio access networks based on non-orthogonal multiple access (C-RAN-NOMA) is explored. Specifically, we consider a downlink C-RAN which includes two paired NOMA-users served by several uniformly distributed remote radio heads (RRHs) simultaneously. Based on the C-RAN-NOMA model, a linear programming (LP) algorithm is proposed to maximize the sum rate of downlink, and the corresponding optimal PA scheme is developed for C-RAN-NOMA. Simulation results illustrate that the developed power allocation scheme can achieve higher sum rate than the conventional equal power allocation scheme, and has the same sum rate as the exhaustive search algorithm but with lower complexity. Thus, the effectiveness of the proposed scheme and the corresponding algorithm are well verified.

Uplink and downlink cloud radio access networks are modeled as two-hop ${K}$ -user ${L}$ -relay networks, whereby small base-stations act as relays for end-to-end communications and are connected to a central processor via orthogonal fronthaul links of finite capacities. Simplified versions of network compress–forward (or noisy network coding) and distributed decode–forward are presented to establish inner bounds on the capacity region for uplink and downlink communications, that match the respective cutset bounds to within a finite gap independent of the channel gains and signal to noise ratios. These approximate capacity regions are then compared with the capacity regions for networks with no capacity limit on the fronthaul. Although it takes infinite fronthaul link capacities to achieve these “fronthaul-unlimited” capacity regions exactly, these capacity regions can be approached approximately with finite-capacity fronthaul. The total fronthaul link capacities required to approach the fronthaul-unlimited sum-rates (for uplink and downlink) are characterized. Based on these results, the capacity scaling law in the large network size limit is established under certain uplink and downlink network models, both theoretically and via simulations.

In this work, we consider the joint optimization of virtual cell association and the precoder design for a multi-user downlink cloud radio access network (C-RAN). The optimal design of cell associations and precoders are particularly relevant due to the anticipated ultra-dense networks suffering from heavy interference. As the resulting optimization is a non-convex and combinatorial problem, we propose an iterative low complexity algorithm, employing the recently developed penalty dual decomposition (PDD) framework. In contrast to predominantly used sparsity inducing methods, the proposed method incorporates the penalty/re-weighting strategy into the algorithm, which is then solved as a sequence of convex second-order cone programs. The aforementioned feature leads to a high convergence rate and subsequently a low computational cost for the proposed algorithm. Furthermore, the proposed algorithm meets the requirements to converge to a KKT solution. Numerical simulations verify the superior performance of our proposed method in ultra-dense networks, in comparison to commonly used methods, such as the reweighted $\ell _1$ -norm, in terms of system energy efficiency.

Cloud Radio Access Network is a candidate solution in 5G and beyond where we intend to serve as many users as possible with minimum transmit power and minimum fronthaul data transmission. In this study, we aim to maximize a mixed expression of the number of users served, the number of fronthaul links used, and the total transmitted power by beamforming optimization under imperfect channel state information. We find a theoretical upper bound and propose a method based on the bound derivation. We compare its performance with heuristic search and norm approximation methods solving a series of convex sub-problems. The detailed simulations show that the proposed method outperforms other baseline techniques in terms of the number of users served, fronthaul data rate, power consumption, and computational complexity.

It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network, which is a candidate solution in 5G and beyond, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink scenario and aim to minimize total power spent by designing beamformers. We consider the case where perfect channel state information is not available in the CP. The original problem includes discontinuous terms with many constraints. We propose a novel method which transforms the problem into a smooth constraint-free form and a solution is found by the gradient descent approach. As a comparison, we consider the optimal method solving an extensive number of convex sub-problems, a known heuristic search algorithm and some sparse solution techniques. Heuristic search methods find a solution by solving a subset of all possible convex sub-problems. Sparse techniques apply some norm approximation (ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) or convex approximation to make the objective function more tractable. We also derive a theoretical performance bound in order to observe how far the proposed method performs off the optimal method when running the optimal method is prohibitive due to computational complexity. Detailed simulations show that the performance of the proposed method is close to the optimal one, and it outperforms other methods analyzed.

Cloud computing based radio access network (CCRAN) is a promising new paradigm for achieving high spectral efficiency (SE) and energy efficiency (EE) performances through embedding cloud computing into the wireless communication system. Cloud radio access network (C-RAN), heterogeneous cloud radio access network (H-CRAN), and fog computing based radio access network (F-RAN) as three application examples of CC-RANs that have gained significant traction in the research community and industry recently.

The cloud radio access network (Cloud-RAN) is an emerging network architecture for the next generation mobile wireless networks. However, connecting a large number of small base stations (SBSs) through wired backhaul links is inconvenient and cost-prohibitive. Therefore, wireless backhaul has become a promising solution for Cloud-RAN. In this paper, we propose to apply the multicast beamforming technique in wireless backhaul, which is efficient to share each user's message to a cluster of SBSs for collaboratively serving the user through joint beamforming. The joint optimization of multicast beamforming in backhaul links and user-centric clustering in access links is formulated as a weighted sum rate maximization problem. To tackle this intractable problem, several approximation and transformation techniques are introduced based on the semidefinite relaxation method and successive convex approximation approach. A block coordinate descent algorithm is developed to solve the approximate optimization problem iteratively by exploiting its special structure. Simulation results show that the proposed algorithm is guaranteed to converge to the stationary point, and the performance outperforms other wireless backhaul schemes under various network scenes.

Cloud radio access network is one of the most promising cellular networks for the next generation of mobile networks. The basic idea of cloud RAN (radio access network) is virtualizing and centralizing the intelligent part of the base station, the base band unit, and keeping remote radio heads on cell site enabling a centralized processing and management. Offloading data computation to edge cloud was proposed as a solution to deal with resource limitation while keeping a good quality of service. In this paper, we propose a strategy to jointly handle offloading decision and offloading request scheduling in cloud RAN. We aim to improve network quality of service while reducing the scheduling cost expressed in terms of overload, network delay, and migration cost. Numerical results show that the proposed approach is able to reduce the response time of the applications, mobile terminal energy consumption, and total execution cost.

In this paper, we investigate robust energy efficiency design for multicast downlink cloud radio access network (C- RAN) for both data-sharing and compression strategies. The two strategies mainly differ in whether the baseband units pool compresses the user messages before transmitting them to remote radio heads (RRH). The performance of worst-case energy efficiency for both strategies is compared by formulating the robust energy efficiency maximization problem subjected to finite RRH power budgets, limited fronthaul link capacity, and minimum quality-of-service constraints when only imperfect channel state information (CSI) is available at the transmitter. To solve these problems, we cast them into semidefinite program problems and solve them iteratively. Simulation results demon- strate effectiveness of our proposed algorithms and two strategies are compared under imperfect CSI.

An attractive architecture called heterogeneous cloud radio access networks (H‐CRAN) becomes one of the important components of 5G networks, which can provide ubiquitous high‐bandwidth services with flexible network construction. However, massive access nodes increase the risk of cell outages, leading to negative impact on user‐perceived QoS (Quality of Service) and QoE (Quality of Experience). Thus, cell outage management (COM) became a key function proposed in SON (Self‐Organized Networks) use cases. Based on COM, cell outage detection (COD) will be resolved before cell outage compensation (COC). Currently few studies concentrate on COD for 5G H‐CRAN, and we propose self‐organized COD architecture and approach for it. We firstly summarize current COD solutions for LTE/LTE‐A HetNets and then introduce self‐organized architecture and approach suitable for H‐CRAN, which includes COD architecture and procedures, and corresponding key technologies for it. Based on the architecture, we take a use case with handover data analysis using modified LOF (Local Outlier Factor) detection approach to detect outage for different kinds of cells in H‐CRAN. Results show that the proposed approach can identify the outage cell effectively.

Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be promising in the fifth generation (5G) wireless networks. H-CRANs enable users to enjoy diverse services with high energy efficiency, high spectral efficiency, and low-cost operation, which are achieved by using cloud computing and virtualization techniques. However, H-CRANs face many technical challenges due to massive user connectivity, increasingly severe spectrum scarcity and energy-constrained devices. These challenges may significantly decrease the quality of service of users if not properly tackled. Non-orthogonal multiple access (NOMA) schemes exploit non-orthogonal resources to provide services for multiple users and are receiving increasing attention for their potential of improving spectral and energy efficiency in 5G networks. In this article a framework for energy-efficient NOMA H-CRANs is presented. The enabling technologies for NOMA H-CRANs are surveyed. Challenges to implement these technologies and open issues are discussed. This article also presents the performance evaluation on energy efficiency of H-CRANs with NOMA.

In this paper, we consider a downlink cloud radio access network where a hierarchical structure of virtual controllers is in the center and massive remote radio heads (RRHs) in the coverage area are divided into multiple clusters. Each cluster has one local controller and a high-level controller coordinates control plane decisions among local controllers. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute users' tasks. Then the output data is transmitted to their respective users through RRHs. This work aims to minimize the average delay including task execution delay and signal transmission delay by joint optimization of task scheduling and resource allocation under power constraint. Specifically, resource allocation consists of VM allocation and RRH assignment. Due to the NP-hardness of the joint optimization problem, we translate it into a matroid constrained submodular function maximization problem and propose a heuristic algorithm to find solutions with 0.5-approximation. Numerical results show that the proposed algorithm can achieve a better performance than two baseline algorithms, i.e., VM prioritized allocation algorithm and RRH prioritized assignment algorithm.

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This paper studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network architecture. Digital processing is carried out at a baseband processing unit (BBU) in the “cloud,” and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU and 2) imperfect effective CSI. The optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio-frequency (RF) beamforming at the RRHs is a coupled problem since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. The numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors.

User-centric and energy efficient are becoming two foremost design principles in the cloud radio access networks (Cloud-RAN). In this paper, we thus consider the problem of how to assign each user to several preferred remote radio heads (RRHs) and design the corresponding beamforming coefficients in a user-centric and energy efficient manner. We formulate this problem as a joint clustering and beamforming optimization problem, with the objective to maximize the energy efficiency (EE) while satisfying the users' quality of service (QoS) requirement and respecting the RRHs' transmit power limits. We first transform it into an equivalent parametric subtractive problem using the approach in fractional programming, and then it is cast into a tractable convex optimization problem by introducing a lower bound of the objective function. Finally, the structure of the optimal solution is derived and a two-tier iterative scheme is developed to find the clustering pattern and beamforming coefficients that maximize EE. Specially, we derive a RRH-user association threshold, based on which the RRH clustering pattern and the corresponding beamforming coefficients can be simultaneously determined. Through simulations, we show the superior performance of the proposed user-centric clustering and beamforming scheme in Cloud-RAN.