wireless system designers

Abstract: Network Digital Twins (NDTs) are important enablers of 6G and future networks. However, there is a lack of studies regarding practical aspects, such as the impact of simultaneously changing twinning rate, fidelity, and other NDT operational parameters. For instance, works often consider the impact of operational parameters in isolation or with physical twin (PTwin) implementations relying on simulations. Therefore, the main contribution of this work is to provide an in-depth study on the performance impacts of both twinning rate and fidelity, using PTwins that rely on measurements obtained from hardware. We also investigate the optimization of these two operational parameters to minimize energy consumption, exploring a Bayesian Optimization (BO) method and what-if analysis. In this work, the NDT models an indoor propagation environment to optimize beam management. The virtual twin (VTwin) was implemented with the Sionna ray tracing (RT) simulator and the PTwin with our in-house setup composed of customized Wi-Fi radios with 32 antenna elements operating at 60 GHz. The study reveals important aspects of wireless channel NDTs, suggesting that fidelity levels vary throughout the experiment and with the what-if difficulty. Moreover, the twinning rate can be adjusted using sample-efficient methods, such as BO, even at lower fidelity levels.

Abstract: A strong edge colouring of a graph $G$ is an edge colouring in which every colour class is an induced matching. The minimum number of colours is the strong chromatic index $χ'_s(G)$. If each edge $e$ is assigned a list $L'(e)$ and its colour must belong to $L'(e)$, the corresponding parameter is the strong list chromatic index $χ'_{s,\ell}(G)$. From the definitions, $χ'_s(G)\leχ'_{s,\ell}(G)$. Barrett et al. gave an $8$-approximation for strong edge colouring on unit disk graphs and Grelier et al. improved the approximation factor to $6$. Our first result extends this factor-$6$ guarantee from unit disk graphs to the strictly larger class of disk graphs. In another direction, Erdős and Nešetřil conjectured that the strong chromatic index of a graph of maximum degree $Δ$ is asymptotically at most $1.25Δ^2$. The best published general asymptotic upper bound has leading coefficient $1.772$, due to Hurley et al. For unit disk graphs, Dębski et al. proved that $χ'_s(G) \leq 1.625 Δ^2$. Our second result improves this leading coefficient to $225/142 \approx 1.5845$. In fact, the proof establishes a stronger bound $χ'_{s,\ell}(G)\le\frac{225}{142} Δ^2+O(Δ)$ for unit disk graphs.

Abstract: The wireless physical (PHY) layer has enabled successive generations of cellular systems through advances in modulation, coding, waveforms, and multiple-input multiple-output (MIMO) transmission. This article assesses whether these techniques are now approaching maturity and where substantial further gains remain possible. Field measurements and quantitative evaluations indicate that many link-level refinements, including constellation shaping, channel-code evolution, reduced-complexity receivers, and waveform enhancements, remain valuable but typically provide bounded gains that must be balanced against implementation complexity and overhead. In contrast, massive MIMO and distributed MIMO offer a more scalable system-level opportunity by increasing the number and quality of usable spatial channels. Their effectiveness relies on time-division duplex reciprocity for scalable channel state information (CSI) acquisition, while practical limitations include calibration, pilot reuse, channel aging, weak pilot reception from cell-edge users, and the fronthaul and synchronization requirements of coherent distributed operation. Artificial intelligence and machine learning (AI/ML) provide complementary opportunities for further PHY layer innovations. The PHY layer is therefore not dead; its most consequential advances will come from deployable spatial processing and CSI acquisition, complemented by targeted link-level and AI/ML-based refinements.

Abstract: Robots in a LiDAR-equipped swarm mutually interfere when their optical ranging codes collide. Existing mitigations either assign codes statically -- requiring $L=N$ distinguishable codes for $N$ robots -- or react to detected interference without a scalable, coordinated assignment rule beneath them; prior work explicitly identifies the code-assignment scaling problem as unsolved. We propose a decentralized protocol in which robots dynamically reassign spatial reuse codes based on a live, beacon-maintained interference-neighborhood graph, and prove that the number of codes required grows as $O(\log N/\log\log N)$ under constant robot density -- an unbounded improvement over the $Θ(N)$ growth of static assignment. We validate this result under conditions substantially beyond the idealized proof -- robot mobility, imperfect beacon-based detection, and reactive reassignment -- via Monte Carlo simulation (30 seeds per condition, 95% confidence intervals): the advantage over static assignment widens from roughly $2\times$ at 15 robots to $12\times$ at 120. Against a structurally faithful, fairly constructed model of an existing coordination-free approach, our protocol achieves both substantially greater code-reuse efficiency and 30--40% lower collision risk under an identical, constrained code budget, demonstrating that coordination -- not merely reactivity -- is what closes the scaling gap.

Abstract: We prove that exact block recovery in the square Gaussian binary MIMO model can be achieved in polynomial time at the same first-order SNR threshold as exhaustive maximum-likelihood detection. Specifically, for $y=\sqrt{ρ/N}Hx^\star+w, \; x^\star\in\{\pm1\}^N,$ and independent standard Gaussian $H\in\mathbb R^{N\times N}$ and $w$, rounded linear MMSE followed by steepest single-bit descent recovers $x^\star$ with failure probability tending to zero, uniformly over every transmitted word and every $ρ\ge2\log N$, using $O(N^3)$ unit-cost exact-real arithmetic operations. The model is a special case of Gaussian random linear estimation, for which AMP state evolution and replica/MMSE formulas rigorously characterize fixed-parameter normalized performance. Those results predict the same $2\log N$ scale, but do not by themselves yield an all-coordinate guarantee in the dimension-dependent regime considered here. To the best of our knowledge, no prior work gives polynomial-time exact block recovery at the ML boundary for this setting; the closest prior square-system theorem, for the box relaxation, has first-order threshold $4\log N$. The proof places the rounded LMMSE estimate at sublinear Hamming distance from the truth, and then establishes, uniformly over every error set the local search can visit, that some wrong bit offers a quantified cost decrease while an objective barrier confines the search path. Conversely, if $0<ρ\le2\log N-\log\log N-s_N$ with $s_N\to\infty$ and $s_N=o(\log N)$, then a one-bit neighbor beats the transmitted word with probability tending to one, so even ML detection fails. Therefore, the statistical and polynomial-time exact-recovery thresholds coincide to first order in the stated arithmetic model.

Abstract: We present a counterexample to the relay channel capacity characterization proposed in a recent work, which is based on properties of typical sequences. The counterexample uses a binary relay channel with a noiseless source-destination component and binary symmetric relay links. An achievable scheme exceeds the proposed capacity characterization, thereby disproving the characterization of the capacity for general relay channels. We highlight that the counterexample is obtained with the assistance of ChatGPT-6 Astra and the proofs are simplified with human effort.

Abstract: Simultaneous wireless information and power transfer (SWIPT) is a critical technology for the future of the Internet of Things (IoT). However, ensuring a stable power supply in such networks remains a significant challenge. This work introduces dynamic polarization control as an additional degree of freedom (DoF) in SWIPT systems. We propose a system where both the base station (BS) and the users can adjust their antenna polarization, a technique known as polarforming. In addition, each user device is capable of splitting the incident signal to perform simultaneous information decoding (ID) and energy harvesting (EH). The resulting non-convex optimization, with many coupled variables, is solved using a graph neural network (GNN) that learns the sub-optimal beamforming, polarization, and power-splitting variables. Simulation results demonstrate that the proposed GNN-based dynamic polarforming optimization significantly outperforms fixed-polarization schemes, particularly under imperfect channel state information (CSI). Moreover, joint polarforming and GNN-based optimization maintain robust SWIPT performance under both polarization mismatch and imperfect CSI.

Abstract: This paper provides a quantitative performance comparison between Orthogonal Time Frequency Space (OTFS) and Orthogonal Frequency-Division Multiplexing (OFDM) modulation schemes, focusing on mobile wireless communication scenarios. We evaluate and compare both schemes based on critical communication scenarios and configurations such as mobility levels, modulation orders, multipath environments, and equalizers. The study systematically identifies conditions where OTFS and OFDM each exhibit optimal performance. Results from simulations demonstrate that OTFS outperforms OFDM consistently for high mobility scenarios and multipath environments. Depending on the modulation order, the performance gap between OTFS and OFDM might be very close or many orders of magnitude. Moreover, at low and mid values of SNR, the non-linear equalizer performs better than traditional linear equalizers for OTFS.

Abstract: We characterize the degree of freedom (DoF) of a point-to-point blockwise memoryless channel without prior channel state information (CSI), with a fixed number $K$ of propagation paths, where the transmitter (Tx) and the receiver (Rx) are equipped with nonuniform linear arrays (NULAs) of $N_t$ and $N_r$ antennas, respectively. The positions of array elements are fixed, known, pairwise distinct, and need not be equally spaced. The uniform linear array (ULA) is a special case. In each block of length $T$, the continuous angles of arrival (AoAs), angles of departure (AoDs), and independent complex Gaussian path gains are redrawn. Both Tx and Rx know the state distributions but are not given the current realizations before transmission. The receiver may estimate the channel from reference signals or decode without explicit channel estimation, with reference symbols counted in $T$ and their energy counted against the power constraint. Under the aforementioned model, we show that the DoF is $1-\frac{1}{T}$ for $K=1$, and $K(1-\frac{3}{2T})$ for $K \geq 2$, when $N_r\ge K+1$, $N_t\ge\max\{K,2\}$, and $T\ge K$. The analytical results are further demonstrated by their applications to the DoF tradeoff analysis in integrated sensing and communication (ISAC). For more general array structures, an achievability result is established, while the converse remains open in general.