Site adaptive method lowers errors in massive MIMO detectors
SiteEvo: An LLM Agent for Autonomous Discovery of Site-Adaptive Massive MIMO Detectors
Information Theory
Summary
Detecting signals more accurately in wireless networks can improve communication quality. The authors present SiteEvo, an approach that automatically creates custom signal detectors tailored to specific locations instead of just adjusting existing designs. SiteEvo uses a large language model to generate new detector programs based on local wireless data and prior knowledge. Tests show SiteEvo finds detectors that cut error rates by more than half while reducing computing cost, and it can operate efficiently without ongoing AI assistance.
What this means in practice
- •For wireless system engineers: Develop customized signal detection algorithms optimized for specific deployment sites to improve network reliability and reduce errors.
- •For communication device manufacturers: Integrate site-specific massive MIMO detectors created by AI agents to enhance device performance in diverse wireless environments.$Commercial implications: Enables branded wireless devices with superior detection tailored per location, serving customers requiring robust connectivity.
Authors
Jiajia Guo, Chao-Kai Wen, Shi Jin, Jun Zhang
Abstract
Designing a massive multiple-input multiple-output (MIMO) detector that is tailored to each specific deployment site can improve detection performance, yet most existing detectors, including deep-unfolding methods, adapt mainly within human-prescribed algorithmic structures. This letter proposes SiteEvo, a site-adaptive agentic discovery framework that extends adaptation from parameter tuning to detector-algorithm discovery. Rather than selecting or recombining predefined architectures, the large language model (LLM) agent synthesizes complete executable detector programs under a common design contract by combining its pretrained communication knowledge, site-specific wireless evidence, and experience accumulated during the search. Each proposal defines its own processing structure and trainable parameterization, and the parameters are optimized by a communication-system backend. The resulting high-performance detector is further structurally refined to reduce block-level computational cost under a bit error rate (BER)-preservation constraint and is finally frozen for deployment without online LLM inference. Simulations across three sites demonstrate BER reductions by factors of 2.6 to 6.0 over the best-performing competing detector at each site at 6 dB, while structural refinement reduces block-level computational cost by 16.7-34.1% under a validation-BER constraint.