arXiv subjects

João P. Vilela

Publications and source records attributed to João P. Vilela.

12 recordsLinked to original sources

arXiv2026

Computation-Limited Signals in Practice: A Throughput Anomaly in Software-Defined O-RAN PHY Layers

We formalize a performance anomaly in software-based wireless physical-layer (PHY) implementations, a key enabling technology for Open Radio Access Network (O-RAN) systems. For a fixed offered load, the number of PHY symbols required to transmit the load decreases as the nominal bit rate increases because each symbol conveys more bits. Shorter bursts, however, provide fewer opportunities to exploit pipelined modulation, transmission, and demodulation. Processing latency therefore represents a larger fraction of the total delivery time and may cause a nominally faster PHY configuration to deliver lower effective throughput. We call the operating condition in which computational latency governs effective throughput the computation-limited regime. Its most pronounced manifestation occurs when the offered load fits in a single symbol: intersymbol pipelining cannot be exploited, and modulation and demodulation latencies cannot be amortized. Their combined latency may exceed the symbol duration even when transmitter and receiver processing independently meet their real-time deadlines. Because PHY processing complexity often scales superlinearly with the resources allocated to each symbol, a higher-rate configuration may consequently deliver lower throughput than a slower one. We validate this anomaly using processing-latency measurements reported for established software-defined PHY implementations. The results show that PHY processing latency should be incorporated into link-adaptation algorithms for very-high-throughput services in future 6G wireless networks.

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arXiv2026

ISAC Privacy: Challenges and Solutions for 6G

Integrated sensing and communication (ISAC) is a promising feature of future communication networks. While spatial sensing can improve network performance and enable external services, it also creates privacy challenges that go beyond the confidentiality of communication content. Future networks using millimeter-wave (mmWave) and sub-terahertz (THz) frequencies may collect or infer detailed information about people, devices, bystanders, passive objects, and environments in a sixth-generation (6G) deployment area. Such sensing can reveal location and environment data, support behavioral profiling such as movement or activity recognition, and, in advanced cases, expose physiological information such as breathing frequency or heart-rate-related data. Thus, the capabilities of spatial sensing must be controlled to satisfy privacy requirements. In this work, we organize privacy-sensitive ISAC data into three sensing levels: location and environment data, behavioral data, and physiological data, and use this classification as the organizing principle throughout the paper. Based on this classification, we discuss internal and external ISAC applications, identify privacy challenges related to consent, transparency, data ownership, profiling, bystander exposure, and sensitive sensing data, review representative solution directions, and outline future research directions for privacy-preserving ISAC.

cs.IT↗

arXiv2025

Fast Computation of the Discrete Fourier Transform Rectangular Index Coefficients

In~\cite{sic-magazine-2025}, the authors show that the square index coefficients (SICs) of the $N$-point discrete Fourier transform (DFT) -- that is, the coefficients $X_{k\sqrt{N}}$ for $k = 0, 1, \ldots, \sqrt{N} - 1$ -- can be losslessly compressed from $N$ to $\sqrt{N}$ points, thereby accelerating the computation of these specific DFT coefficients accordingly. Following up on that, in this article we generalize SICs into what we refer to as rectangular index coefficients (RICs) of the DFT, formalized as $X_{kL}, k=0,1,\cdots,C-1$, in which the integers $C$ and $L$ are generic roots of $N$ such that $N=LC$. We present an algorithm to compress the $N$-point input signal $\mathbf{x}$ into a $C$-point signal $\mathbf{\hat{x}}$ at the expense of $\mathcal{O}(N)$ complex sums and no complex multiplication. We show that a DFT on $\mathbf{\hat{x}}$ is equivalent to a DFT on the RICs of $\mathbf{x}$. In cases where specific frequencies of $\mathbf{x}$ are of interest -- as in harmonic analysis -- one can conveniently adjust the signal parameters (e.g., frequency resolution) to align the RICs with those frequencies, and use the proposed algorithm to compute them significantly faster. If $N$ is a power of two -- as required by the fast Fourier transform (FFT) algorithm -- then $C$ can be any power of two in the range $[2, N/2]$ and one can use our algorithm along with FFT to compute all RICs in $\mathcal{O}(C\log C)$ time complexity.

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arXiv2025

Computational Complexity-Constrained Spectral Efficiency Analysis for 6G Waveforms

In this work, we present a tutorial on how to account for the computational time complexity overhead of signal processing in the spectral efficiency (SE) analysis of wireless waveforms. Our methodology is particularly relevant in scenarios where achieving higher SE entails a penalty in complexity, a common trade-off present in 6G candidate waveforms. We consider that SE derives from the data rate, which is impacted by time-dependent overheads. Thus, neglecting the computational complexity overhead in the SE analysis grants an unfair advantage to more computationally complex waveforms, as they require larger computational resources to meet a signal processing runtime below the symbol period. We demonstrate our points with two case studies. In the first, we refer to IEEE 802.11a-compliant baseband processors from the literature to show that their runtime significantly impacts the SE perceived by upper layers. In the second case study, we show that waveforms considered less efficient in terms of SE can outperform their more computationally expensive counterparts if provided with equivalent high-performance computational resources. Based on these cases, we believe our tutorial can address the comparative SE analysis of waveforms that operate under different computational resource constraints.

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arXiv2024

Fast Computation of the Discrete Fourier Transform Square Index Coefficients

The $N$-point discrete Fourier transform (DFT) is a cornerstone for several signal processing applications. Many of these applications operate in real-time, making the computational complexity of the DFT a critical performance indicator to be optimized. Unfortunately, whether the $\mathcal{O}(N\log_2 N)$ time complexity of the fast Fourier transform (FFT) can be outperformed remains an unresolved question in the theory of computation. However, in many applications of the DFT -- such as compressive sensing, image processing, and wideband spectral analysis -- only a small fraction of the output signal needs to be computed because the signal is sparse. This motivates the development of algorithms that compute specific DFT coefficients more efficiently than the FFT algorithm. In this article, we show that the number of points of some DFT coefficients can be dramatically reduced by means of elementary mathematical properties. We present an algorithm that compacts the square index coefficients (SICs) of DFT (i.e., $X_{k\sqrt{N}}$, $k=0,1,\cdots, \sqrt{N}-1$, for a square number $N$) from $N$ to $\sqrt{N}$ points at the expense of $N-1$ complex sums and no multiplication. Based on this, any regular DFT algorithm can be straightforwardly applied to compute the SICs with a reduced number of complex multiplications. If $N$ is a power of two, one can combine our algorithm with the FFT to calculate all SICs in $\mathcal{O}(\sqrt{N}\log_2\sqrt{N})$ time complexity.

cs.DS↗

arXiv2024

Computation-Limited Signals: A Channel Capacity Regime Constrained by Computational Complexity

In this letter, we introduce the computational-limited (comp-limited) signals, a communication capacity regime in which the signal time computational complexity overhead is the key constraint -- rather than power or bandwidth -- to the overall communication capacity. We present the Spectro-Computational (SC) analysis, a novel mathematical framework that enhances classic concepts of information theory -- such as throughput, spectral efficiency and capacity -- to account for the signal processing computational complexity overhead. We consider a specific Shannon regime under which capacity is expected to get arbitrarily large as channel resources grow. Under that regime, we identify the conditions under which the time complexity overhead causes capacity to decrease rather than increasing, thereby creating the case for the comp-limited regime. We also provide examples of the SC analysis and show the OFDM waveform is comp-limited unless the lower-bound computational complexity of the $N$-point DFT problem verifies as $Ω(N)$, which remains an open challenge.

cs.IT↗

arXiv2022

Is FFT Fast Enough for Beyond-5G Communications?

In this paper, we study the impact of computational complexity on the throughput limits of the {\color{black}fast Fourier transform (FFT)} algorithm for {\color{black}orthogonal frequency division multiplexing(OFDM)} waveforms. Based on the spectro-computational {\color{\corcorrecao}complexity} (SC) analysis, {\color{\corcorrecao} we verify that the complexity of an $N$-point FFT grows faster than the number of bits in the OFDM symbol.} Thus, we show that FFT nullifies the OFDM throughput on $N$ unless the $N$-point discrete Fourier transform (DFT) problem verifies as $Ω(N)$, which remains a "fascinating" open question in theoretical computer science. Also, because FFT demands $N$ to be a power of two $2^i$ ($i>0$), the spectrum widening leads to an exponential complexity on $i$, i.e. $O(2^ii)$. To overcome these limitations, {\color{\corcorrecao} we consider the alternative frequency-time transform formulation of vector OFDM (V-OFDM), in which an $N$-point FFT is replaced by $N/L$ ($L$$>$$0$) smaller {\color{\corcorrecao}$L$-point} FFTs to mitigate the cyclic prefix overhead of OFDM. Building on that, we replace FFT by the straightforward DFT algorithm to release the V-OFDM parameters from growing as powers of two and to benefit from flexible numerology (e.g., $L=3$, $N=156$). Besides, by setting $L$ to $Θ(1)$, the resulting solution can run linearly on $N$ (rather than exponentially on $i$) while sustaining a non null throughput as $N$ grows. }

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arXiv2020

Bringing Network Coding into SDN: A Case-study for Highly Meshed Heterogeneous Communications

Modern communications have moved away from point-to-point models to increasingly heterogeneous network models. In this article, we propose a novel controller-based protocol to deploy adaptive causal network coding in heterogeneous and highly-meshed communication networks. Specifically, we consider using Software-Defined-Network (SDN) as the main controller. We first present an architecture for the highly-meshed heterogeneous multi-source multi-destination networks that represents the practical communication networks encountered in the fifth generation of wireless networks (5G) and beyond. Next, we present a promising solution to deploy network coding over the new architecture. In fact, we investigate how to generalize adaptive and causal random linear network coding (AC-RLNC), proposed for multipath multi-hop (MP-MH) communication channels, to a protocol for the new multi-source multi-destination network architecture using controller. To this end, we present a modularized implementation of AC-RLNC solution where the modules work together in a distributed fashion and perform the AC-RLNC technology. We also present a new controller-based setting through which the network coding modules can communicate and can attain their required information. Finally, we briefly discuss how the proposed architecture and network coding solution provide a good opportunity for future technologies, e.g., distributed coded computation and storage, mmWave communication environments, and innovative and efficient security features.

cs.NI↗

arXiv2020

Optimal Mapper for OFDM with Index Modulation: A Spectro-Computational Analysis

In this work, we present an optimal mapper for OFDM with index modulation (OFDM-IM). By optimal we mean the mapper achieves the lowest possible asymptotic computational complexity (CC) when the spectral efficiency (SE) gain over OFDM maximizes. We propose the spectro-computational (SC) analysis to capture the trade-off between CC and SE and to demonstrate that an $N$-subcarrier OFDM-IM mapper must run in exact $Θ(N)$ time complexity. We show that an OFDM-IM mapper running faster than such complexity cannot reach the maximal SE whereas one running slower nullifies the mapping throughput for arbitrarily large $N$. We demonstrate our theoretical findings by implementing an open-source library that supports all DSP steps to map/demap an N-subcarrier complex frequency-domain OFDM-IM symbol. Our implementation supports different index selector algorithms and is the first to enable the SE maximization while preserving the same time and space asymptotic complexities of the classic OFDM mapper.

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arXiv2020

Maximal Spectral Efficiency of OFDM with Index Modulation under Polynomial Space Complexity

In this letter, we demonstrate a mapper that enables all waveforms of OFDM with Index Modulation (OFDM-IM) while preserving polynomial time and space computational complexities. Enabling all OFDM-IM waveforms maximizes the spectral efficiency (SE) gain over the classic OFDM but, as far as we know, the computational overhead of the resulting mapper remains conjectured as prohibitive across the OFDM-IM literature. We show that the largest number of binomial coefficient calculations performed by the original OFDM-IM mapper is polynomial on the number of subcarriers, even under the setup that maximizes the SE gain over OFDM. Also, such coefficients match the entries of the so-called Pascal's triangle (PT). Thus, by assisting the OFDM-IM mapper with a PT table, we show that the maximum SE gain over OFDM can be achieved under polynomial (rather than exponential) time and space complexities.

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arXiv2015

Analysis of Short Blocklength Codes for Secrecy

In this paper we provide secrecy metrics applicable to physical-layer coding techniques with finite blocklengths over Gaussian and fading wiretap channel models. Our metrics go beyond some of the known practical secrecy measures, such as bit error rate and security gap, so as to make lower bound probabilistic guarantees on error rates over short blocklengths both preceding and following a secrecy decoder. Our techniques are especially useful in cases where application of traditional information-theoretic security measures is either impractical or simply not yet understood. The metrics can aid both practical system analysis, and practical system design for physical-layer security codes. Furthermore, these new measures fill a void in the current landscape of practical security measures for physical-layer security coding, and may assist in the wide-scale adoption of physical-layer techniques for security in real-world systems. We also show how the new metrics provide techniques for reducing realistic channel models to simpler discrete memoryless wiretap channel equivalents over which existing secrecy code designs may achieve information-theoretic security.

cs.CR↗

arXiv2008

Network Coding Security: Attacks and Countermeasures

By allowing intermediate nodes to perform non-trivial operations on packets, such as mixing data from multiple streams, network coding breaks with the ruling store and forward networking paradigm and opens a myriad of challenging security questions. Following a brief overview of emerging network coding protocols, we provide a taxonomy of their security vulnerabilities, which highlights the differences between attack scenarios in which network coding is particularly vulnerable and other relevant cases in which the intrinsic properties of network coding allow for stronger and more efficient security solutions than classical routing. Furthermore, we give practical examples where network coding can be combined with classical cryptography both for secure communication and secret key distribution. Throughout the paper we identify a number of research challenges deemed relevant towards the applicability of secure network coding in practical networks.

cs.CR↗