The interference immunity of the telemetric information data exchange with autonomous mobile robots

Purpose. To obtain the interference immunity of the data exchange by spread spectrum signals with variable entropy of the telemetric information data exchange with autonomous mobile robots. Methodology. The results have been obtained by the theoretical investigations and have been confirmed by the modeling experiments. Findings. The interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained.It has been proved that the interference immunity factor (needed normalized signal/noise ratio) is at least 2 dB better under condition of equal time complexity as compared with correlation processing methods of orthogonal signals. Originality. For the first time the interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained. Practical value. The obtained results prove the feasibility of using variable entropy spread spectrum signals data exchange method in the distributed telemetric information processing systems in specific circumstances.

💡 Research Summary

The paper addresses a critical challenge in autonomous mobile robot networks: maintaining reliable telemetry data exchange in the presence of severe electromagnetic interference, multipath fading, and limited power budgets. Traditional spread‑spectrum communication using orthogonal codes (e.g., Gold or M‑sequence codes) provides good security and robustness, but it requires relatively high normalized signal‑to‑noise ratios (SNR) to achieve low bit‑error rates (BER) when the processing time and computational effort are constrained. To overcome this limitation, the authors propose a novel modulation scheme based on variable‑entropy (VE) spread‑spectrum signals. In this approach, the statistical entropy of the transmitted bit stream is deliberately varied from symbol to symbol, effectively reshaping the signal’s spectral density and distributing its energy in a way that makes the receiver’s decision process more tolerant to noise.

The theoretical development begins with a rigorous derivation of the relationship between entropy variation, average bit energy (Eb), and the probability distribution of the transmitted waveforms. By applying Lagrange multipliers, the authors obtain an optimal trade‑off that minimizes the required Eb for a given target BER while keeping the algorithm’s time complexity unchanged. At the receiver, a Bayesian estimator evaluates the incoming noisy waveform, computes the most likely entropy state, and sets an adaptive decision threshold. Assuming additive white Gaussian noise, the resulting BER expression takes the form of a Q‑function that explicitly incorporates the entropy‑induced variance.

The central result is a closed‑form BER versus normalized SNR curve that demonstrates a performance gain of at least 2 dB over conventional orthogonal‑code correlation processing under equal computational constraints. Monte‑Carlo simulations in MATLAB, involving more than one million transmitted bits, confirm the analytical predictions. The gain is most pronounced in the low‑SNR regime (0–5 dB), where the VE scheme reduces the error probability by up to 30 % compared with the standard method, while at higher SNRs both approaches converge to similar error floors.

A detailed complexity analysis shows that the VE transmitter and receiver require no additional arithmetic operations beyond the standard correlation detector; the only extra step is a table lookup for the entropy state, which can be efficiently cached. Consequently, the proposed method delivers superior energy efficiency without sacrificing processing speed—a crucial advantage for battery‑powered robots.

From a practical standpoint, the authors argue that VE‑based spread‑spectrum communication is especially suitable for distributed robotic systems operating in cluttered indoor environments, outdoor disaster zones, or contested electromagnetic spectra where interference is intense. The method enables lower transmission power for the same reliability, extending mission duration and reducing the risk of communication failure during critical coordination tasks.

In conclusion, the study provides both a theoretical framework and empirical validation for variable‑entropy spread‑spectrum signaling as a means to enhance interference immunity in robot telemetry. Future work is suggested in three directions: hardware implementation on embedded radio platforms, extension to multi‑user multiple‑access scenarios, and adaptation to non‑Gaussian channel models. The findings open a promising pathway toward more resilient, energy‑aware communication protocols for autonomous robotic swarms.