Random Access Protocol Design and Analysis for Neural Interfaces Under Non-Saturated Regime

Neural interfaces are essential in biomedical engineering as they establish communication pathways between the brain and computers. Integration of wireless neural interfaces with random access protocols offers an efficient method for recording and analyzing neural activities. However, current access protocols, such as IEEE 802.15.4 and IEEE 802.15.6, do not appropriately prioritize data types in neural interface networks. This paper proposes a random access protocol that distinguishes access priorities for different data types. We provide analytical models for the proposed random access algorithm, and accordingly by optimizing parameters, such as the Exclusive Access Phase (EAP) duration in the superframe, the reliability and latency performance is improved. The performance of the proposed random access protocol is evaluated using NS3 simulator. Results indicate that the theoretical analysis matches experimental results, with a transmission successful rate over 95% and a latency below 50ms when there are 15 nodes with average arrival interval no less than 100ms.