arXiv subjects

Benjamin Koon Kei Ng

Publications and source records attributed to Benjamin Koon Kei Ng.

3 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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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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