RF Coverage Planning And Analysis With Adaptive Cell Sectorization In Millimeter Wave 5G Networks

The advancement of Fifth Generation Network (5G) technology is well underway, with Mobile Network Operators (MNOs) globally commencing the deployment of 5G networks within the mid-frequency spectrum range (3GHz–6GHz). Nevertheless, the escalating demands for data traffic are compelling MNOs to explore the high-frequency spectrum (24GHz–100GHz), which offers significantly larger bandwidth (400MHz-800 MHz) compared to the mid-frequency spectrum (3GHz–6GHz), which typically provides 50MHz-100MHz of bandwidth. However, it is crucial to note that the higher-frequency spectrum imposes substantial challenges due to exceptionally high free space propagation loss, resulting in 5G cell site coverage being limited to several hundred meters, in contrast to the several kilometers achievable with 4G. Consequently, MNOs are faced with the formidable task of accurately planning and deploying hundreds of new 5G cells to cover the same areas served by a single 4G cell.This dissertation embarks on a comprehensive exploration of Radio Frequency (RF) coverage planning for 5G networks, initially utilizing a conventional three-sector cell architecture. The coverage planning phase reveals potential challenges, including coverage gaps and poor Signal-to-Interference-plus-Noise Ratio (SINR). In response to these issues, the dissertation introduces an innovative cell site architecture that embraces both nine and twelve sector cells, enhancing RF coverage through the adoption of an advanced antenna system designed with subarrays, offering adaptive beamforming and beam steering capabilities. To further enhance energy efficiency, the dissertation introduces adaptive higher-order cell-sectorization (e.g., nine sector cells and twelve sector cells). In this proposed method, all sectors within a twelve-sector cell remain active during peak hours (e.g., daytime) and are reduced to fewer sectors (e.g., nine sectors or six sectors per cell) during off-peak hours (e.g., nighttime). This dynamic adjustment is facilitated by an advanced antenna system utilizing sub-array architecture, which employs adaptive beamforming and beam steering to tailor the beamwidth and radiation angle of each active sector. Simulation results unequivocally demonstrate significant enhancements in RF coverage and SINR with the implementation of higher-order cell-sectorization. Furthermore, the proposed adaptive cell-sectorization method significantly reduces energy consumption during off-peak hours. In addition to addressing RF coverage planning, this dissertation delves into the numerous challenges associated with deploying 5G networks in the higher frequency spectrum (30GHz-300GHz). It encompasses issues such as precise cell site planning, location acquisition, propagation modeling, energy efficiency, backhauling, and more. Furthermore, the dissertation offers valuable insights into future research directions aimed at effectively surmounting these challenges and optimizing the deployment of 5G networks in the high-frequency spectrum

Aspects of capacity enhancement techniques in cellular networks

Frequency spectrum is the scarce resource. From mobile operator’s point of view, efficient utilization of the radio resources is needed while providing maximum coverage, and ensuring good quality of service with minimal infrastructure. In high capacity demanding areas, multilayer networks with multiband and multi radio access technologies are deployed, in order to meet the capacity requirements. In his doctoral thesis, Usman Sheikh has proposed a “Smart Traffic Handling” strategy, which is based on user’s required service type and location. Smart traffic handling scheme efficiently utilizes the different layers of the network, balances the load among them, and improves the system capacity. Power resources at base station are also limited. Usman Sheikh’s proposed “Power Control Scheme for High Speed Downlink Packet Access (HSDPA) network” improves the cell edge user experience, while maintaining the fairness among the other users in a cell. With the help of a proposed power control scheme, a user far from the base station can also enjoy the better quality of service. Generally, mobile operators use macro cells with wide beam antennas for wider coverage in the cell, but future capacity demands cannot be achieved by using only them. “Higher Order Sectorization” is one possible way to increase the system capacity. Usman Sheikh proposed new network layouts called “Snowflake” and “Flower” tessellations, for 6-sector and 12-sector sites, respectively. These tessellations can be used as a basis for making a nominal network plan for sites with higher order sectorization. These tessellations would be helpful for simulation purposes. Through his work, he has also tried to highlight the importance of deploying “Adaptive MIMO Switching” in Long Term Evolution (LTE) system, the fourth generation of wireless networks. In future, the fifth generation of wireless networks is expected to offer thousand times more capacity compared to LTE. The novel concept of “Single Path Multiple Access (SPMA)” given by Usman Sheikh is a revolutionary idea, and gives a possibility to increase the system capacity by a giant margin. SPMA can be considered as a right step towards 5G technology. Usman Sheikh’s work is of high importance not only from mobile operator’s point of view; rather his contributions to the scientific community will also lead to better user (customer) experience. His work will definitely benefit the mankind in utilizing the limited resources in an optimum and efficient way

A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives

The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain

Fast and efficient user pairing and power allocation algorithm for non-orthogonal multiple access in cellular networks

Non-orthogonal multiple access (NOMA) is emerging as a promising multiple access technology for the fifth generation cellular networks to address the fast growing mobile data traffic. It applies superposition coding in transmitters, allowing simultaneous allocation of the same frequency resource to multiple intra-cell users. Successive interference cancellation is used at the receivers to cancel intra-cell interference. User pairing and power allocation (UPPA) is a key design aspect of NOMA. Existing UPPA algorithms are mainly based on exhaustive search method with extensive computation complexity, which can severely affect the NOMA performance. A fast proportional fairness (PF) scheduling based UPPA algorithm is proposed to address the problem. The novel idea is to form user pairs around the users with the highest PF metrics with pre-configured fixed power allocation. Systemlevel simulation results show that the proposed algorithm is significantly faster (seven times faster for the scenario with 20 users) with a negligible throughput loss than the existing exhaustive search algorithm

Spectral Efficiency of One-Bit Sigma-Delta Massive MIMO

We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta ( \Sigma \Delta ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the \Sigma \Delta approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the \Sigma \Delta array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the \Sigma \Delta approach for both MRC and zero-forcing receivers

Unsupervised Massive MIMO Channel Estimation with Dual-Path Knowledge-Aware Auto-Encoders

In this paper, an unsupervised deep learning framework based on dual-path model-driven variational auto-encoders (VAE) is proposed for angle-of-arrivals (AoAs) and channel estimation in massive MIMO systems. Specifically designed for channel estimation, the proposed VAE differs from the original VAE in two aspects. First, the encoder is a dual-path neural network, where one path uses the received signal to estimate the path gains and path angles, and another uses the correlation matrix of the received signal to estimate AoAs. Second, the decoder has fixed weights that implement the signal propagation model, instead of learnable parameters. This knowledge-aware decoder forces the encoder to output meaningful physical parameters of interests (i.e., path gains, path angles, and AoAs), which cannot be achieved by original VAE. Rigorous analysis is carried out to characterize the multiple global optima and local optima of the estimation problem, which motivates the design of the dual-path encoder. By alternating between the estimation of path gains, path angles and the estimation of AoAs, the encoder is proved to converge. To further improve the convergence performance, a low-complexity procedure is proposed to find good initial points. Numerical results validate theoretical analysis and demonstrate the performance improvements of our proposed framework

Two-stage time-domain pilot contamination elimination in large-scale multiple-antenna aided and TDD based OFDM systems

Pilot contamination (PC) is a major impediment of large-scale multi-cell multiple-input multiple-output (MIMO) systems. Hence we propose an optimal pilot design for timedomain channel estimation, which is capable of completely eliminating PC. More specifically, a sophisticated combination of downlink training and ‘scheduled’ uplink training is designed with the aid of the optimal pilot set. Given the optimal pilot set, every user acquires its unique downlink time-domain channel state information (CSI) through downlink training. The estimated downlink CSIs are then embedded in the uplink training. As a result, PC can be completely eliminated, at the cost of a slight increase in training computational complexity. Our simulation results demonstrate the power of the proposed scheme. Most significantly, our scheme imposes a modest training overhead of (L + 3), training-phase durations corresponding to the number of OFDM symbols, where L is the number of cells, which is substantially lower than that imposed by some of the existing PC elimination schemes. Therefore, it imposes a less stringent requirement on the channel’s coherence time. Finally, our scheme does not need any information exchange between base stations