Spatially-Aware Channel Estimation via Discrete Channel Knowledge Maps
Year: 2026
Type: article
Abstract: Communication system users increasingly anticipate faster and more reliable services. Communication system operators can meet these expectations by incorporating geographical information to improve bandwidth utilization. Through combining geographical data, such as the position of the user and surrounding objects the necessity to transmit known symbols (pilots) for channel estimation can be reduced. One method for removing channel estimation is to store the Channel’s Impulse Response (CIR) for every possible location, however, this is impractical. In this paper, we introduce an innovative technique for creating a Coherence Distance Region (CDR). A CDR contains a geographic contour and representation of the expected multipath channel, such that multiple users can leverage this information for accurate channel estimation, regardless of their position within the contour. These CDRs can be combined to form a Discrete Channel Knowledge Map (DCKM) to cover an entire area. To illustrate the effectiveness of our approach, we conducted two case studies in different environments using numerical simulations. Our findings demonstrate that utilizing a DCKM leads to an improved bit error rate compared to methods which do not incorporate environmental information, such as equalization methods reliant on pilots. These results highlight the potential of using DCKMs to improve bandwidth utilization through improved channel estimation.
Source: IEEE Access
Authors: Daniel Brown, Brian Salmon, Evan Franklin
Institution: University of Tasmania
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W7131406429
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