As applications of Internet-of-things (IoT) rapidly
expand, unscheduled multiple user access with low latency and
low cost communication is attracting growing more interests. To
recover the multiple uplink signals without strict access control
under dynamic co-channel interference environment, the problem
of blind demixing emerges as an important obstacle for us to
overcome. Without channel state information, successful blind
demixing can recover multiple user signals more effectively by
leveraging prior information on signal characteristics such as
constellations and distribution. This work studies how forward
error correction (FEC) codes in Galois Field can generate more
effective blind demixing algorithms. We propose a constrained
Wirtinger flow algorithm by defining a valid signal set based on
FEC codewords. Specifically, targeting the popular polar codes
for FEC of short IoT packets, we introduce signal projections
within iterations of Wirtinger Flow based on FEC code information.
Simulation results demonstrate stronger robustness of
the proposed algorithm against noise and practical obstacles and
also faster convergence rate compared to regular Wirtinger flow
algorithm.
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A Riemannian Geometric Approach to Blind SignalRecovery for Grant Free Radio Network Access
We propose a new nonconvex framework for blind multiple signal demixing and recovery. The proposed Riemann geometric approach extends the well known constant modulus
algorithm to facilitate grant-free wireless access. For multiple signal demixing and recovery, we formulate the problem as non-convex problem optimization problem with signal orthogonality constraint in the form of Riemannian Orthogonal CMA(ROCMA). Unlike traditional stochastic gradient solutions that require large data samples, parameter tuning, and careful initialization, we leverage Riemannian geometry and transform the orthogonality requirement of recovered signals into a Riemannian manifold optimization. Our solution demonstrates full recovery of multiple access signals without large data sample size or special initialization with high probability of success.
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- NSF-PAR ID:
- 10281830
- Date Published:
- Journal Name:
- IEEE transactions on signal processing
- ISSN:
- 1941-0476
- Page Range / eLocation ID:
- Submitted
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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