Cell-Free mmWave Massive MIMO Communication
While the fifth generation (5G) of wireless communication systems is being commercialized and implemented, academia is already thinking about what comes next. In order to deliver even higher data rates while remaining energy efficient, sixth generation (6G) wireless systems are expected to exploit technologies already used in 5G such as massive MIMO and distributed antenna systems (DASs), but at higher carrier frequencies. Specifically, the combination of cell-free massive MIMO and millimeter wave (mmWave) communication promises to be the true enabler of the wireless Gbits/s era . A cell-free system is a network formed by distributed access points (APs) over a large area connected to a central processing unit (CPU) and serving all users in the same time-frequency resource. Channels are estimated in each AP and the information is sent to the CPU, which performs detection, precoding, and power allocation. Instead of operating at sub-6-GHz, as conventional cellular or cell-free systems , cell-free mmWave systems will operate at frequencies exceeding 28 GHz .
Fusing cell-free massive MIMO with mmWave has been barely explored until now. In , channel estimation in this specific scenario is carefully studied. Low-complexity hybrid precoders/decoders for cell-free mmWave systems have been studied in . Last, to solve the lack of usage of all APs in this type of system,  presents an energy efficient AP sleep mode-technique that is able to deal with different traffic load scenarios. The technique promises to improve the achievable energy efficiency of cell-free mmWave systems.
In this project, we will address some of the most prominent implementation challenges of cell-free mmWave massive MIMO Systems. Concretely, we will develop efficient algorithms for power control, data detection, and multiuser precoding, considering fronthaul capacity constraints, and user selection strategies. The main objective is to develop new solutions that maximize energy and spectral efficiency, while being computationally efficient and ready for the future generation of wireless networks.
 Y. Jin, J. Zhang, S. Jin and B. Ai, "Channel Estimation for Cell-Free mmWave Massive MIMO Through Deep Learning," in IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10325-10329, Oct. 2019, doi: 10.1109/TVT.2019.2937543. Link
 H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, "Cell-Free Massive MIMO Versus Small Cells," in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017, doi: 10.1109/TWC.2017.2655515. Link
 G. Femenias and F. Riera-Palou, "Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity," in IEEE Access, vol. 7, pp. 44596-44612, 2019, doi: 10.1109/ACCESS.2019.2908688. Link
 J. García-Morales, G. Femenias and F. Riera-Palou, "Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density," in IEEE Access, vol. 8, pp. 137587-137605, 2020, doi: 10.1109/ACCESS.2020.3012199. Link
- Student: Jannik Brun
- Date: Spring Semester 2022
- Supervision: Victoria Menescal Tupper Palhares Gian Marti
- Communication Systems (recommended)
- 20% Literature Research
- 80% System Development
Detailed Task Description
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