AI summaryⓘ
The authors study a communication method called NoiseMod, which sends data by changing the amount of noise-like signal power. They focus on handling interference when multiple users share the same channel and propose using fluid antennas (FAMA) to select the best antenna port for decoding. They mathematically analyze how interference varies with user signals and fading, providing exact formulas to pick ports minimizing errors. Their work also examines the effects of spatial correlation among antennas and shows that more spatial diversity allows more users to communicate simultaneously. Finally, they develop a way to estimate detection difficulty under idealized conditions, offering theoretical insights rather than practical algorithms.
Noise ModulationFluid Antenna Multiple AccessNoncoherent DetectionMaximum Likelihood DetectionBit Error ProbabilityMoment-Generating FunctionSpatial CorrelationJakes ModelVariance-Domain SensingInterference Management
Authors
Hadi Zayyani, Felipe A. P. de Figueiredo
Abstract
Noise modulation (NoiseMod) encodes information in the variance of a noise-like waveform. Its noncoherent structure enables low-complexity links, but co-channel users are challenging because interference affects the same variance statistic used for detection. We study fluid antenna multiple access (FAMA) for two-level NoiseMod with independent random bits from each interferer, producing two possible nonzero interference variances. We adopt exact finite-sample maximum-likelihood energy detection from prior NoiseMod/TNM work. We derive the exact single-port moment-generating function and moments of the random-bit aggregate interference, including heterogeneous user powers, and prove that the port minimizing conditional BEP is $\arg\max_k H_k/(σ_w^2+J_k)$, where $H_k$ is desired-link power and $J_k$ is instantaneous bit-state-aware interference variance. For independent fading ports, the common interferer-bit vector couples per-port decision metrics; conditioning on the number of high-state interferers yields an exact binomial-mixture order-statistic representation and avoids the generally invalid rule $F_{Z_{\max}}=F_Z^{N_p}$. For spatially correlated operation, an integer-$μ$ $κ$-$μ$ benchmark uses full pairwise Jakes correlation in scattered fields, with channel-averaged BEP evaluated by conditional Monte Carlo. A standard-error-aware load study shows increased admissible co-channel users as spatial degrees of freedom grow. Finally, a finite-sample variance-domain sensing diagnostic quantifies the oracle gap under frozen channels and interferer states, providing a lower-bound warning on fast-FAMA sensing difficulty rather than a deployable acquisition protocol.