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Pedro M. R. Pereira

Publications and source records attributed to Pedro M. R. Pereira.

3 recordsLinked to original sources

Fluid Antenna-Aided Noise Modulation: Spatial Diversity for Variance-Based Wireless Communication

Noise modulation (NoiseMod) encodes information in the \emph{variance} of a transmitted noise-like waveform rather than in its amplitude, phase, or frequency, and is attractive for ultra-low-power and covert links. Its main weakness is that, unlike classical modulation, it exhibits \emph{no} diversity under Rayleigh fading: its bit error probability (BEP) decays only as $1/(N_sδ)$, where $N_s$ is the number of noise samples per bit and $δ$ the useful-to-thermal noise variance ratio. Independently, fluid antenna systems (FAS) have been shown to recover substantial selection diversity from a single radiating element that switches among $N_p$ closely spaced ports, without extra radio-frequency chains. This paper combines the two: we equip a NoiseMod receiver with a fluid antenna and derive its average BEP. For idealized, mutually independent ports, we obtain an exact closed-form BEP via order statistics of the port envelopes. For the physically accurate, spatially correlated case governed by Jake's model, we build a semi-analytical BEP using the two-stage channel approximation of Khammassi \emph{et al.} We validate both regimes against full signal-level Monte Carlo simulation and show that (i) FAS restores a diversity order that grows with the number of ports $N_p$ when ports are weakly correlated, (ii) this gain saturates once the fluid-antenna aperture $Wλ$ is fixed and $N_p$ grows, mirroring the outage-probability saturation reported for FAS, now observed for BEP, and (iii) an intrinsic, correlation-independent (and $δ$-independent) BEP floor set only by $N_s$ and the variance ratio $α$ persists regardless of the antenna diversity order.

cs.IT↗

On the Practical Performance of Noise Modulation for Ultra-Low-Power IoT: Limitations, Capacity, and Energy Trade-offs

Ultra-low-power (ULP) Internet of Things (IoT) applications demand communication architectures with minimal energy consumption. Noise Modulation (NoiseMod) addresses this by encoding data through the statistical variance of a noise-like signal, eliminating the need for a coherent carrier. To bridge the gap between theoretical potential and practical deployment, this paper benchmarks NoiseMod against standard modulations like BPSK and NC-FSK. We analytically derive the optimal detection threshold and Bit Error Rate (BER) for AWGN and Rayleigh fading channels. Our results show that non-coherent NoiseMod suffers a catastrophic error floor in fading environments, making architectural additions like channel state information (CSI) estimation and 2-antenna selection diversity desirable. Using an ADC-aware energy model, we reveal that NoiseMod's oversampling severely bottlenecks capacity and imposes an 8 dB SNR penalty compared to NC-FSK for a $10^{-3}$ BER in AWGN. Despite its oscillator-free design drastically reducing baseline circuit power, these limitations establish a critical energy crossover distance, which decreases with frequency. Below this distance, NoiseMod offers superior energy efficiency; beyond it, the radiated power needed to overcome its SNR penalty makes coherent schemes like BPSK vastly superior.

cs.IT↗

Fast-TurboQuant: A Multiplier-Free Online Vector Quantization Approach

As large language models scale, memory bandwidth for key-value caches and retrieval-augmented generation systems becomes a critical bottleneck. While 1-bit quantization addresses this constraint, recent TurboQuant relies on dense random rotation matrices to condition the vector distribution before quantization. This projection demands millions of floating-point multiplications per embedding, making it difficult to deploy on constrained edge silicon. We introduce Fast-TurboQuant, a multiplier-free projection architecture that replaces the dense matrix with a structured fast Johnson-Lindenstrauss transform. By applying a Rademacher phase inversion followed by a fast Walsh-Hadamard transform (FWHT), the method leverages sub-Gaussian concentration to satisfy the prerequisites of scalar Lloyd-Max quantization without Gaussian projections. This substitution reduces the arithmetic complexity to only additions, eliminating hardware multipliers. Evaluation on DBpedia OpenAI-3 Large embeddings demonstrates a 19.7 times algorithmic speedup under sequential execution. Furthermore, the dimension expansion due to the FWHT zero-padding reduces the mean squared error and improves Recall@10.

cs.LG↗