Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33\% compared with the one attained without optimizing power sharing factors

A model-driven deep reinforcement learning heuristic algorithm for resource allocation in ultra-dense cellular networks

Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but also significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness

D3.2 First performance results for multi -node/multi -antenna transmission technologies

This deliverable describes the current results of the multi-node/multi-antenna technologies investigated within METIS and analyses the interactions within and outside Work Package 3. Furthermore, it identifies the most promising technologies based on the current state of obtained results. This document provides a brief overview of the results in its first part. The second part, namely the Appendix, further details the results, describes the simulation alignment efforts conducted in the Work Package and the interaction of the Test Cases. The results described here show that the investigations conducted in Work Package 3 are maturing resulting in valuable innovative solutions for future 5G systems.Fantini. R.; Santos, A.; De Carvalho, E.; Rajatheva, N.; Popovski, P.; Baracca, P.; Aziz, D.... (2014). D3.2 First performance results for multi -node/multi -antenna transmission technologies. http://hdl.handle.net/10251/7675

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

Cell-Free Massive MIMO and Millimeter Wave Channel Modelling for 5G and Beyond

Huge demand for wireless throughput and number of users which are connected to the base station (BS) has been observed in the last decades. Massive multiple-input multiple-output (MIMO) is a promising technique for 5G for the following reasons; 1) high throughput; 2) serving large numbers of users at the same time; 3) energy efficiency. However, the low throughput of cell-edge users remains a limitation in realistic multi-cell massive MIMO systems. In cell-free massive MIMO, on the other hand, distributed access points (APs) are connected to a central processing unit (CPU) and jointly serve distributed users. This thesis investigates the performance of cell-free Massive MIMO with limited-capacity fronthaul links from the APs to the CPU which will be essential in practical 5G networks. To model the limited-capacity fronthaul links, we exploit the optimal uniform quantization. Next, closed-form expressions for spectral and energy efficiencies are presented. Numerical results investigate the performance gap between limited fronthaul and perfect fronthaul cases, and demonstrate that exploiting a relatively few quantization bits, the performance of limited-fronthaul cell-free Massive MIMO closely approaches the perfect fronthaul performance. Next, the energy efficiency maximization problem and max-min fairness problems are considered with per-user power and fronthaul capacity constraints. We propose an iterative procedure which exploits a generalized eigen vector problem and geometric programming (GP) to solve the max-min optimization problem. Numerical results indicate the superiority of the proposed algorithms over the case of equal power allocation. On the other hand, the performance of communication systems depends on the propagation channel. To investigate the performance of MIMO systems, an accurate small scale fading channel model is necessary. Geometry-based stochastic channel models (GSCMs) are mathematically tractable models to investigate the performance of MIMO systems

Scaling up virtual MIMO systems

Multiple-input multiple-output (MIMO) systems are a mature technology that has been incorporated into current wireless broadband standards to improve the channel capacity and link reliability. Nevertheless, due to the continuous increasing demand for wireless data traffic new strategies are to be adopted. Very large MIMO antenna arrays represents a paradigm shift in terms of theory and implementation, where the use of tens or hundreds of antennas provides significant improvements in throughput and radiated energy efficiency compared to single antennas setups. Since design constraints limit the number of usable antennas, virtual systems can be seen as a promising technique due to their ability to mimic and exploit the gains of multi-antenna systems by means of wireless cooperation. Considering these arguments, in this work, energy efficient coding and network design for large virtual MIMO systems are presented. Firstly, a cooperative virtual MIMO (V-MIMO) system that uses a large multi-antenna transmitter and implements compress-and-forward (CF) relay cooperation is investigated. Since constructing a reliable codebook is the most computationally complex task performed by the relay nodes in CF cooperation, reduced complexity quantisation techniques are introduced. The analysis is focused on the block error probability (BLER) and the computational complexity for the uniform scalar quantiser (U-SQ) and the Lloyd-Max algorithm (LM-SQ). Numerical results show that the LM-SQ is simpler to design and can achieve a BLER performance comparable to the optimal vector quantiser. Furthermore, due to its low complexity, U-SQ could be consider particularly suitable for very large wireless systems. Even though very large MIMO systems enhance the spectral efficiency of wireless networks, this comes at the expense of linearly increasing the power consumption due to the use of multiple radio frequency chains to support the antennas. Thus, the energy efficiency and throughput of the cooperative V-MIMO system are analysed and the impact of the imperfect channel state information (CSI) on the system’s performance is studied. Finally, a power allocation algorithm is implemented to reduce the total power consumption. Simulation results show that wireless cooperation between users is more energy efficient than using a high modulation order transmission and that the larger the number of transmit antennas the lower the impact of the imperfect CSI on the system’s performance. Finally, the application of cooperative systems is extended to wireless self-backhauling heterogeneous networks, where the decode-and-forward (DF) protocol is employed to provide a cost-effective and reliable backhaul. The associated trade-offs for a heterogeneous network with inhomogeneous user distributions are investigated through the use of sleeping strategies. Three different policies for switching-off base stations are considered: random, load-based and greedy algorithms. The probability of coverage for the random and load-based sleeping policies is derived. Moreover, an energy efficient base station deployment and operation approach is presented. Numerical results show that the average number of base stations required to support the traffic load at peak-time can be reduced by using the greedy algorithm for base station deployment and that highly clustered networks exhibit a smaller average serving distance and thus, a better probability of coverage

On the performance of hybrid beamforming for millimeter wave wireless networks

The phenomenal growth in the demand for mobile wireless data services is pushing the boundaries of modern communication networks. Developing new technologies that can provide unprecedented data rates to support the pervasive and exponentially increasing demand is therefore of prime importance in wireless communications. In existing communication systems, physical layer techniques are commonly used to improve capacity. Nevertheless, the limited available resources in the spectrum are unable to scale up, fundamentally restricting further capacity increase. Consequently, alternative approaches which exploit both unused and underutilised spectrum bands are highly attractive. This thesis investigates the use of the millimeter wave (mmWave) spectrum as it has the potential to provide unlimited bandwidth to wireless communication systems. As a first step toward realising mmWave wireless communications, a cloud radio access network using mmWave technology in the fronthaul and access links is proposed to establish a feasible architecture for deploying mmWave systems with hybrid beamforming. Within the context of a multi-user communication system, an analytical framework of the downlink transmission is presented, providing insights on how to navigate across the challenges associated with high-frequency transmissions. The performance of each user is measured by deriving outage probability, average latency and throughput in both noise-limited and interference-limited scenarios. Further analysis of the system is carried out for two possible user association configurations. By relying on large antenna array deployment in highly dense networks, this architecture is able to achieve reduced outages with very low latencies, making it ideal to support a growing number of users. The second part of this work describes a novel two-stage optimisation algorithm for obtaining hybrid precoders and combiners that maximise the energy efficiency (EE) of a general multi-user mmWave multiple-input, multiple-output (MIMO) interference channel network involving internet of things (IoT) devices. The hybrid transceiver design problem considers both perfect and imperfect channel state information (CSI). In the first stage, the original non-convex multivariate EE maximization problem is transformed into an equivalent univariate problem and the optimal single beamformers are then obtained by exploiting the correlation between parametric and fractional programming problems and the relationship between weighted sum rate (WSR) and weighted minimum mean squared error (WMMSE) problems. The second stage involves the use of an orthogonal matching pursuit (OMP)-based algorithm to obtain the energy-efficient hybrid beamformers. This approach produces results comparable to the optimal beam-forming strategy but with much lower complexity, and further validates the use of mmWave networks in practice to support the demand from ubiquitous power-constrained smart devices. In the third part, the focus is on the more practical scenario of imperfect CSI for multi-user mmWave systems. Following the success of hybrid beamforming for mmWave wireless communication, a non-traditional transmission strategy called Rate Splitting (RS) is investigated in conjunction with hybrid beamforming to tackle the residual multi-user interference (MUI) caused by errors in the estimated channel. Using this technique, the transmitted signal is split into a common message and a private message with the transmitted power dynamically divided between the two parts to ensure that there is interference-free transmission of the common message. An alternating maximisation algorithm is proposed to obtain the optimal common precoder. Simulation results show that the RS transmission scheme is beneficial to multi-user mmWave transmissions as it enables remarkable rate gains over the traditional linear transmission methods. Finally, the fourth part analyses the spectral efficiency (SE) performance of a mmWave system with hybrid beamforming whilst accounting for real-life practice transceiver hardware impairments. An investigation is conducted into three major hardware impairments, namely, the multiplicative phase noise (PN), the amplified thermal noise (ATN) and the residual additive transceiver hardware impairments (RATHI). The hybrid precoder is designed to maximise the SE by the minimisation of the Euclidean distance between the optimal digital precoder and the noisy product of the hybrid precoders while the hybrid combiners are designed by the minimisation of the mean square error (MSE) between the transmitted and received signals. Multiplicative PN was found to be the most critical of the three impairments considered. It was observed that the additive impairments could be neglected for low signal-to-noise-ratio (SNR) while the ATNs caused a steady degradation to the SE performance