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

Joint Fronthaul Link Selection and Transmit Precoding for Energy Efficiency Maximization of Multiuser MIMO-Aided Distributed Antenna Systems

We jointly select the fronthaul links and optimize the transmit precoding matrices for maximizing the energy efficiency of a multiuser multiple-input multiple-output aided distributed antenna system . The fronthaul link’s power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints some of the users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the energy efficiency optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Then, a triple-layer iterative algorithm is proposed for solving the approximated energy efficiency optimization problem, which is proved to converge to the Karush-Kuhn-Tucker conditions of the smoothened energy efficiency optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms is capable of achieving an order of magnitude higher energy efficiency than its conventional counterpart operating without considering link selection

A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks

The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non- Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions

Enabling Efficient Communications Over Millimeter Wave Massive MIMO Channels Using Hybrid Beamforming

The use of massive multiple-input multiple-output (MIMO) over millimeter wave (mmWave) channels is the new frontier for fulfilling the exigent requirements of next-generation wireless systems and solving the wireless network impending crunch. Massive MIMO systems and mmWave channels offer larger numbers of antennas, higher carrier frequencies, and wider signaling bandwidths. Unleashing the full potentials of these tremendous degrees of freedom (dimensions) hinges on the practical deployment of those technologies. Hybrid analog and digital beamforming is considered as a stepping-stone to the practical deployment of mmWave massive MIMO systems since it significantly reduces their operating and implementation costs, energy consumption, and system design complexity. The prevalence of adopting mmWave and massive MIMO technologies in next-generation wireless systems necessitates developing agile and cost-efficient hybrid beamforming solutions that match the various use-cases of these systems. In this thesis, we propose hybrid precoding and combining solutions that are tailored to the needs of these specific cases and account for the main limitations of hybrid processing. The proposed solutions leverage the sparsity and spatial correlation of mmWave massive MIMO channels to reduce the feedback overhead and computational complexity of hybrid processing. Real-time use-cases of next-generation wireless communication, including connected cars, virtual-reality/augmented-reality, and high definition video transmission, require high-capacity and low-latency wireless transmission. On the physical layer level, this entails adopting near capacity-achieving transmission schemes with very low computational delay. Motivated by this, we propose low-complexity hybrid precoding and combining schemes for massive MIMO systems with partially and fully-connected antenna array structures. Leveraging the disparity in the dimensionality of the analog and the digital processing matrices, we develop a two-stage channel diagonalization design approach in order to reduce the computational complexity of the hybrid precoding and combining while maintaining high spectral efficiency. Particularly, the analog processing stage is designed to maximize the antenna array gain in order to avoid performing computationally intensive operations such as matrix inversion and singular value decomposition in high dimensions. On the other hand, the low-dimensional digital processing stage is designed to maximize the spectral efficiency of the systems. Computational complexity analysis shows that the proposed schemes offer significant savings compared to prior works where asymptotic computational complexity reductions ranging between $80\%$ and $98\%$. Simulation results validate that the spectral efficiency of the proposed schemes is near-optimal where in certain scenarios the signal-to-noise-ratio (SNR) gap to the optimal fully-digital spectral efficiency is less than $1$ dB. On the other hand, integrating mmWave and massive MIMO into the cellular use-cases requires adopting hybrid beamforming schemes that utilize limited channel state information at the transmitter (CSIT) in order to adapt the transmitted signals to the current channel. This is so mainly because obtaining perfect CSIT in frequency division duplexing (FDD) architecture, which dominates the cellular systems, poses serious concerns due to its large training and excessive feedback overhead. Motivated by this, we develop low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency (RF) analog precoders from statistically skewed DFT-based codebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to interference noise ratio for the single-user and multi-user cases, respectively. Mathematical analysis shows that the proposed algorithms are asymptotically optimal as the number of transmit antennas goes to infinity and the mmWave channel has a limited number of paths. Moreover, it shows that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the mmWave channel or equivalently the transmit channel covariance matrix when only the statistical channel knowledge is available at the transmitter. Further, we verify the performance of the proposed algorithms numerically where the obtained results illustrate that the spectral efficiency of the proposed algorithms can approach that of the optimal precoder in certain scenarios. Furthermore, these results illustrate that the proposed hybrid precoding schemes have superior spectral efficiency performance while requiring lower (or at most comparable) channel feedback overhead in comparison with the prior art

Joint Design of Wireless Fronthaul and Access Links in Massive MIMO CRANs

Cloud radio access network (CRAN) has emerged as a promising mobile network architecture for the current 5th generation (5G) and beyond networks. This thesis focuses on novel architectures and optimization approaches for CRAN systems with massive multiple-input multiple-output (MIMO) enabled in the wireless fronthaul link. In particular, we propose a joint design of wireless fronthaul and access links for CRANs and aim to maximize the network spectral efficiency (SE) and energy efficiency (EE). Regarding downlink transmission in massive MIMO CRANs, the precoding designs of the access link are optimized by accounting for both perfect instantaneous channel state information (CSI) and stochastic CSI of the access link separately. The system design adopts a decompress-and-forward (DCF) scheme at the remote radio heads (RRHs), with optimization of the multivariate compression covariance noise. Constrained by the maximum power budgets set for the central unit (CU) and RRHs, we aim to maximize the network sum-rate and minimize the total transmit power for all user equipments (UEs). Moreover, we present a separate optimization design and compare its performance, feasibility, and computational efficiency with the proposed joint design. Considering the uplink transmission, we utilize a compress-and-forward (CF) scheme at the RRHs. Assuming that perfect CSI is available at the CU, our objective is to optimize the precoding matrix of the access link while adopting conventional precoding methods for the fronthaul link. This thesis also proposes an unmanned aerial vehicle (UAV)-enabled CRAN architecture with a massive MIMO CU as a supplement system to the terrestrial communication networks. The locations of UAVs are optimized along with compression noise, precoding matrices, and transmit power. To tackle the non-convex optimization problems described above, we employ efficient iterative algorithms and conduct a thorough exploration of practical simulations, yielding promising results that outperform benchmark schemes. In summary, this thesis explores future wireless CRAN architectures, leveraging promising technologies including massive MIMO and UAV-enabled communications. Furthermore, this work presents comprehensive optimization designs aimed at further enhancing the network efficiency