On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed

A distributed user-cell association for spectral and energy efficiency tradeoff in massive MIMO UDHNs

Massive MIMO enabled ultra-dense heterogeneous networks (UDHNs) have been considered as the indispensable and emerging approach to meet the demand on growing data traffic for next generation networks. Although deployment of large number of antennas causes high circuit power consumption in massive MIMO UDHNs, there is always a tradeoff between energy efficiency (EE) and spectral efficiency (SE). Therefore, an energy efficient and spectral efficient user-cell association will become crucial and challenging in massive MIMO UDHNs. In this paper, we address a user-cell association problem for EE and SE tradeoff. To this end, we formulate a convex multi-objective optimization problem (MOP) and convert it into a single-objective optimization problem (SOP) where a priority is assigned for EE and SE with a weighting factor which means the problem can be adjusted whether priority is on EE or SE. The problem aims to maximize the weighted sum of the EE and SE. As a solution, Lagrange duality analysis is performed and a distributed game theoretical user-cell association (GTUCA) algorithm considering the fairness among users is developed. The results confirm that the proposed algorithm outperforms the baseline algorithm, namely maximum rate-based cell selection in terms of EE and SE, when the weighting factor is set properly.IEEE; Turkcell; Ericsson; Huawei; Rohde & Schwarz; Aselsan; National Science Foundation (NSF

On the Performance Enhancement of Beamspace MIMO and Non-orthogonal Multiple Access for Future Cellular Networks

The ever-growing demand for higher data rates and greater data capacity at lower cost has led the mobile cellular industry to investigate new physical layer techniques and possible utilization of unused spectrums at higher frequencies for next-generation cellular networks. Thus, exploitation of the millimeter-wave (mmWave) spectrum and non-orthogonal multiple-access (NOMA) have been envisioned as the most promising enablers in meeting capacity demand. Due to the smaller wavelengths offered in mmWave frequencies, it is possible to deploy many antennas into a relatively smaller physical space in mmWave frequencies. This property leads to a promising integration between mmWave and massive multiple-input multiple-output (M-MIMO) architecture to surmount the severe free-space pathloss thanks to the high directional beamforming gain. MmWave M-MIMO also offers significantly improved spectral efficiency by allowing simultaneous transmission of multiple data streams and utilizing the abundant and large bandwidth. However, conventional digital precoding causes excessive power consumption and hardware cost when directly adopted for mmWave M-MIMO since each antenna element necessitates its own radio-frequency (RF) chain. This problem can be addressed by beamspace MIMO (B-MIMO) because it can reduce the required RF chain by taking advantage of inherent sparsity in mmWave channels and applying a proper beam selection. On the other hand, NOMA enhances spectral efficiency by multiplexing multiple users\u27 signals in the power domain using the same time and frequency resources, where the detection of multiple users\u27 signals is performed by successive interference cancellation (SIC). The research has concentrated on the beam selection problem, precoding design in B-MIMO, and spectral/energy efficiency enhancement in mmWave M-MIMO and NOMA. Specifically, the dissertation addresses the following: First, we investigate the complexity reduction of the existing beam selection algorithms with incremental QR precoder (I-QR-P) and decremental QR precoder (D-QR-P). The proposed two-stage and three-stage algorithms reduce the complexity of D-QR-P and I-QR-P, respectively. Both aim to lower complexity by decreasing the candidate beam size by eliminating the beams with no contribution to any user and using matrix perturbation theory to update QR decompositions. Second, we propose a hybrid precoding algorithm for the lens antenna subarray (LAS)-MIMO architecture in mmWave to control the LAS design efficiently. The precoding problem is formulated as a sparse reconstruction problem due to the inherent sparsity of the mmWave channel. The proposed algorithm is an iterative process developed jointly using artificial bee colony (ABC) optimization with orthogonal matching pursuit (OMP) algorithms. In each iteration, the algorithm randomly selects the switches for each lens using ABC and then uses OMP to approximate optimal unconstrained precoders. Third, we investigate the spectral efficiency and energy efficiency tradeoff in downlink NOMA with the consideration of the quality of service (QoS) requirements. The non-convex multi-objective optimization problems are solved using population-based multi-objective evolutionary algorithms (MOEAs). Finally, we propose an algorithm for the user-cell association problem in M-MIMO ultra-dense heterogeneous networks (UDHNs)., where the spectral and energy efficiency tradeoff is addressed. To this end, we formulate a convex multi-objective optimization problem and convert it into a single objective optimization problem where a priority is assigned for the spectral efficiency and energy efficiency with a weighting factor. The problem aims to maximize the weighted sum of spectral efficiency and energy efficiency. As a solution, Lagrange duality analysis is performed, and a distributed game theoretical user-cell association (GTUCA) algorithm is developed, considering the fairness among users