Journal
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume 10, Issue 2, Pages 281-285Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2020.3028111
Keywords
Bandwidth; Wireless communication; MIMO communication; Optimization; Frequency modulation; Central Processing Unit; Uplink; Cell-free MIMO; non-orthogonal; wireless backhaul; integrated computation and communication; deep learning
Categories
Funding
- National Nature Science Foundation of China [61771051, 62071038]
Ask authors/readers for more resources
This study proposes the use of non-orthogonal transmissions in a wireless backhaul system, optimizing the detection computation mappings between APs and CPUs through a deep neural network to reduce backhaul bandwidth cost and improve transmission accuracy.
In cell-free massive multiple-input-multiple-output system with wireless backhaul, the distributed access points (APs) and the center processing unit (CPU) are connected via wireless links. Hence, the limited backhaul bandwidth becomes a critical challenge to uplink transmission. To save the bandwidth while maintaining high transmission accuracy, we propose to deploy non-orthogonal transmissions in backhaul link and jointly optimize the detection computation mappings at the APs and the CPU under the non-orthogonal backhaul. First, we formulate the joint design problem subject to backhaul bandwidth constraint aiming at a better end-to-end transmission accuracy. Then, the non-trivial problem is parameterized and solved with a novel model-driven deep neural network, where wireless backhaul is integrated as a neural computing layer by exploiting the reciprocity between non-orthogonal transmission and additive operation. Evaluations show that, the proposed integration method outperforms the conventional approaches by a margin in both backhaul bandwidth cost and the symbol error rate.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available