Sparse Beamforming for Real-Time Resource Management and Energy Trading in Green C-RAN

This paper considers cloud radio access network with simultaneous wireless information and power transfer and finite capacity fronthaul, where the remote radio heads are equipped with renewable energy resources and can trade energy with the grid. Due to uneven distribution of mobile radio traffic and inherent intermittent nature of renewable energy resources, the remote radio heads may need real-time energy provisioning to meet the users' demands. Given the amount of available energy resources at remote radio heads, this paper introduces two provisioning strategies to strike an optimum balance among the total power consumption in the fronthaul, through adjusting the degree of partial cooperation among the remote radio heads, the total transmit power and the maximum or the overall real-time energy demand. More specifically, this paper formulates two sparse optimization problems and applies reweighted ℓ 1 -norm approximation for ℓ 0 -norm and semidefinite relaxation to develop two iterative algorithms for the proposed strategies. Simulation results confirm that both of the proposed strategies outperform two other recently proposed schemes in terms of improving energy efficiency and reducing overall energy cost of the network

Energy-efficient resource allocation in limited fronthaul capacity cloud-radio access networks

In recent years, cloud radio access networks (C-RANs) have demonstrated their role as a formidable technology candidate to address the challenging issues from the advent of Fifth Generation (5G) mobile networks. In C-RANs, the modules which are capable of processing data and handling radio signals are physically separated in two main functional groups: the baseband unit (BBU) pool consisting of multiple BBUs on the cloud, and the radio access networks (RANs) consisting of several low-power remote radio heads (RRH) whose functionality are simplified with radio transmission/reception. Thanks to the centralized computation capability of cloud computing, C-RANs enable the coordination between RRHs to significantly improve the achievable spectral efficiency to satisfy the explosive traffic demand from users. More importantly, this enhanced performance can be attained at its power-saving mode, which results in the energy-efficient C-RAN perspective. Note that such improvement can be achieved under an ideal fronthaul condition of very high and stable capacity. However, in practice, dedicated fronthaul links must remarkably be divided to connect a large amount of RRHs to the cloud, leading to a scenario of non-ideal limited fronthaul capacity for each RRH. This imposes a certain upper-bound on each user’s spectral efficiency, which limits the promising achievement of C-RANs. To fully harness the energy-efficient C-RANs while respecting their stringent limited fronthaul capacity characteristics, a more appropriate and efficient network design is essential. The main scope of this thesis aims at optimizing the green performance of C-RANs in terms of energy-efficiency under the non-ideal fronthaul capacity condition, namely energy-efficient design in limited fronthaul capacity C-RANs. Our study, via jointly determining the transmit beamforming, RRH selection, and RRH–user association, targets the following three vital design issues: the optimal trade-off between maximizing achievable sum rate and minimizing total power consumption, the maximum energy-efficiency under adaptive rate-dependent power model, the optimal joint energy-efficient design of virtual computing along with the radio resource allocation in virtualized C-RANs. The significant contributions and novelties of this work can be elaborated in the followings. Firstly, the joint design of transmit beamforming, RRH selection, and RRH–user association to optimize the trade-off between user sum rate maximization and total power consumption minimization in the downlink transmissions of C-RANs is presented in Chapter 3. We develop one powerful with high-complexity and two novel efficient low-complexity algorithms to respectively solve for a global optimal and high-quality sub-optimal solutions. The findings in this chapter show that the proposed algorithms, besides overcoming the burden to solve difficult non-convex problems within a polynomial time, also outperform the techniques in the literature in terms of convergence and achieved network performance. Secondly, Chapter 4 proposes a novel model reflecting the dependence of consumed power on the user data rate and highlights its impact through various energy-efficiency metrics in CRANs. The dominant performance of the results form Chapter 4, compared to the conventional work without adaptive rate-dependent power model, corroborates the importance of the newly proposed model in appropriately conserving the system power to achieve the most energy efficient C-RAN performance. Finally, we propose a novel model on the cloud center which enables the virtualization and adaptive allocation of computing resources according to the data traffic demand to conserve more power in Chapter 5. A problem of jointly designing the virtual computing resource together with the beamforming, RRH selection, and RRH–user association which maximizes the virtualized C-RAN energy-efficiency is considered. To cope with the huge size of the formulated optimization problem, a novel efficient with much lower-complexity algorithm compared to previous work is developed to achieve the solution. The achieved results from different evaluations demonstrate the superiority of the proposed designs compared to the conventional work

Joint Precoding and RRH Selection for User-Centric Green MIMO C-RAN

© 2002-2012 IEEE. This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a user-centric cloud radio access network, where both the RRHs and users have multiple antennas and each user is served by its nearby RRHs. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. In this paper, we propose to solve this problem in two stages. In Stage I, a low-complexity user selection algorithm is proposed to find the largest subset of feasible users. In Stage II, a low-complexity algorithm is proposed to solve the optimization problem with the users selected from Stage I. Specifically, the re-weighted l1-norm minimization method is used to transform the original problem with non-smooth objective function into a series of weighted power minimization (WPM) problems, each of which can be solved by the weighted minimum mean square error (WMMSE) method. The solution obtained by the WMMSE method is proved to satisfy the Karush-Kuhn-Tucker conditions of the WPM problem. Moreover, a low-complexity algorithm based on Newton's method and the gradient descent method is developed to update the precoder matrices in each iteration of the WMMSE method. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side. Moreover, the proposed algorithm is shown to achieve near-optimal performance in terms of NPC

Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has aroused. Specifically, UAVs can be used in cellular networks as aerial users for delivery, surveillance, rescue search, or as an aerial base station (aBS) for communication with ground users in remote uncovered areas or in dense environments requiring prompt high capacity. Aiming to satisfy the high requirements of wireless aerial networks, several multiple access techniques have been investigated. In particular, space-division multiple access(SDMA) and power-domain non-orthogonal multiple access (NOMA) present promising multiplexing gains for aerial downlink and uplink. Nevertheless, these gains are limited as they depend on the conditions of the environment. Hence, a generalized scheme has been recently proposed, called rate-splitting multiple access (RSMA), which is capable of achieving better spectral efficiency gains compared to SDMA and NOMA. In this paper, we present a comprehensive survey of key multiple access technologies adopted for aerial networks, where aBSs are deployed to serve ground users. Since there have been only sporadic results reported on the use of RSMA in aerial systems, we aim to extend the discussion on this topic by modelling and analyzing the weighted sum-rate performance of a two-user downlink network served by an RSMA-based aBS. Finally, related open issues and future research directions are exposed.Comment: 16 pages, 6 figures, submitted to IEEE Journa

On the Performance of Cell-Free Massive MIMO Relying on Adaptive NOMA/OMA Mode-Switching

The downlink (DL) of a non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multipleoutput (MIMO) system is analyzed, where the channel state information (CSI) is estimated using pilots. It is assumed that the users are grouped into multiple clusters. The same pilot sequences are assigned to the users within the same clusters whereas the pilots allocated to all clusters are mutually orthogonal. First, a user’s bandwidth efficiency (BE) is derived based on his/her channel statistics under the assumption of employing successive interference cancellation (SIC) at the users’ end with no DL training. Next, the classic max-min optimization framework is invoked for maximizing the minimum BE of a user under peraccess point (AP) power constraints. The max min user BE of NOMA-based cell-free massive MIMO is compared to that of its orthogonal multiple-access (OMA) counter part, where all users employ orthogonal pilots. Finally, our numerical results are presented and an operating mode switching scheme is proposed based on the average per-user BE of the system, where the mode set is given by Mode = { OMA, NOMA }. Our numerical results confirm that the switching point between the NOMA and OMA modes depends both on the length of the channel’s coherence time and on the total number of users

Performance Optimization of Cloud Radio Access Networks

The exponential growth of cellular data traffic over the years imposes a hard challenge on the next cellular generations. The cloud radio access network (CRAN) is an emerging cellular architecture that is expected to face that challenge effectively. The main difference between the CRAN architecture and the conventional cellular architecture is that the baseband units (BBUs) are aggregated at a centralized baseband unit pool, hence, enabling statistical multiplexing gains. However, to acquire the several advantages offered by the CRAN architecture, efficient optimization algorithms and transmission techniques should be implemented to enhance the network performance. Hence, in this thesis, we consider jointly optimizing user association, resource allocation and power allocation in a two tier heterogeneous cloud radio access network (H-CRAN). Our objective is to utilize all the network resources in the most efficient way to maximize the network average throughput, while keeping some constraints such as the quality of service (QoS), interference protection to the devices associated with the Macro remote radio head (MRRH), and fronthaul capacity. In our system, we propose using coordinated multi-point (CoMP) transmissions to utilize any excess resources to maximize the network performance, in contrast to the literature, in which CoMP is usually used only to support edge users. We divide our joint problem into three sub-problems: user association, radio resource allocation, and power allocation. We propose matching game based low complexity algorithms to tackle the first two sub-problems. For the power allocation sub-problem, we propose a novel technique to convexify the non-convex original problem to obtain the optimal solution. Given the conducted simulations, our proposed algorithms proved to enhance the network average weighted sum rate significantly, compared to the state of the art algorithms in the literature. The high computational complexity of the optimization techniques currently proposed in the literature prevents from totally reaping the benefits of the CRAN architecture. Learning based techniques are expected to replace the conventional optimization techniques due to their high performance and very low online computational complexity. In this thesis, we propose tackling the power allocation in CRAN via an unsupervised deep learning based approach. Different from the previous works, user association is considered in our optimization problem to reflect a real cellular scenario. Additionally, we propose a novel scheme that can enhance the deep learning based power allocation approaches, significantly. We provide intensive analysis to discuss the trade-offs faced when employing our deep learning based approach for power allocation. Simulation results prove that the proposed technique can obtain a very close to optimal performance with negligible computational complexity

Uplink Transceiver Design and Optimization for Transmissive RMS Multi-Antenna Systems

In this paper, a novel uplink communication for the transmissive reconfigurable metasurface (RMS) multi-antenna system is investigated. Specifically, a transmissive RMS-based receiver equipped with a single receiving antenna is first proposed, and a far-near field channel model is also given. Then, in order to maximize the system sum-rate, we formulate a joint optimization problem over subcarrier allocation, power allocation and RMS transmissive coefficient design. Since the coupling of optimization variables, the problem is non-convex, so it is challenging to solve it directly. In order to tackle this problem, the alternating optimization (AO) algorithm is used to decouple the optimization variables and divide the problem into two subproblems to solve. Numerical results verify that the proposed algorithm has good convergence performance and can improve system sum-rate compared with other benchmark algorithms.Comment: arXiv admin note: text overlap with arXiv:2109.0546