Sumit Roy

Wide-sense stationary, Uncorrelated Scattering (WSSUS) channel models with corresponding Doppler shift/spread and coherence time profiles have been fundamental staples underlying stochastic models in terrestrial networking with end-point mobility. In this letter that focusses on low Earth orbit (LEO) satellite links, we highlight need for greater accuracy based on the distinct transmitter–scatterer–receiver geometry in such scenarios. This contribution re-derives the Doppler spread for LEO downlink based on typical geometry-driven local scattering scenarios and highlights gaps with prior art and alignment with recent observations.

This letter studies the latency–power trade-off of various multi-link operation (MLO) traffic-allocation mechanisms – probability-based, greedy, and single link (SL) – using ns-3 simulations under both symmetric and asymmetric link conditions. We jointly consider mean and 99th-percentile (p99) end-to-end (E2E) latency as well as average power consumption of multi-link devices (MLDs). Results show that the greedy scheme achieves the lowest latency in symmetric traffic scenarios by exploiting simultaneous multi-link access, albeit with moderately higher power consumption. Conversely, under asymmetric link quality or background load, the probability-based method effectively collapses to single-link operation, attaining both minimal latency and lowest power. Our analysis sheds important light on adaptive traffic allocation for latency-sensitive and power-constrained Wi-Fi 7 applications.

This perspective submission–from academic researcher and WiFi industry analyst–casts an eye on the recently introduced multi-user features in WiFi 7 with a view to improving key performance metrics (throughput and latency) via Multi-User MIMO and OFDMA. Despite the initial promise and predictions of significant benefits, current evidence from actual deployment suggests much more modest and somewhat uncertain gains particularly in networks with strong legacy usage, suggesting that achieving more fuller benefits may lie in future via greenfield 6 GHz usage.

Downlink (DL) Multi-User (MU) Multiple Input Multiple Output (MU-MIMO) is a key technology that allows multiple concurrent data transmissions from an Access Point (AP) to a selected sub-set of clients for higher network efficiency in IEEE 802.11be (Wi-Fi 7). However, DL MU-MIMO feature is typically turned off as the default setting in AP vendors’ products, that is, turning on the DL MU-MIMO may not help increase the network efficiency, which is counter-intuitive. In this article, we provide a sufficiently deep understanding of the interplay between the various underlying factors, i.e., CSI overhead and spatial correlation, which result in negative results when turning on the DL MU-MIMO. Furthermore, we provide a fundamental guideline as a function of operational scenarios to address the fundamental question “when the DL MU-MIMO should be turned on/off”.

As a groundbreaking feature in IEEE 802.11be, multi-link operation (MLO) is expected to support emerging applications that are strongly delay-sensitive. A key to the effective use of MLO for such cases rests on the optimal allocation of application traffic across multiple links. Our initial simulation experiments in ns-3 reveal that the proposed traffic allocation policies in prior art are significantly sub-optimal in terms of achievable delay performance of multi-link devices (MLDs), particularly in the presence of legacy single-link devices (SLDs). In this work, we first develop a new analytical model for the mean end-to-end (E2E) delay, delay jitter and worst-case percentile latency performance of MLD-SLD-coexisting Wi-Fi 7 networks (largely unexplored to date) with saturated and unsaturated SLD traffic. Subsequently, the optimal traffic allocation strategies for minimizing the mean E2E delay and delay jitter are obtained and validated by ns-3 simulation results. It is shown that with the optimal policy, MLDs can achieve significantly better mean E2E delay, delay jitter, worst-case latency and delay cumulative distribution function (CDF) compared to existing solutions.

Anti-collision assistance, integral to the current drive towards increased vehicular autonomy, relies heavily on precise detection and localization of moving targets in the vehicle’s vicinity. A crucial step towards achieving this is the removal of static objects from the scene, thereby enhancing the detection and localization of dynamic targets—a pivotal aspect in augmenting overall system performance. In this paper, we propose a static background removal algorithm tailored for automotive scenarios, designed for common frequency-modulated continuous wave (FMCW) radars. This algorithm effectively eliminates reflections corresponding to static backgrounds from radar images through a two-step process: 4-dimensional (4D) radar imaging and filtering in the azimuth-elevation-Doppler domain. Our proposed approach is underpinned by a model customized for FMCW radar signals, incorporating a time-division multiplexing-based multiple-input multiple-output scheme on the non-uniform radar array. Furthermore, our filtering process requires knowledge of the 3-dimensional (3D) radar ego-motion velocity, typically obtained from an external sensor. To address scenarios where such sensors are unavailable, we introduce a self-contained 3D ego-motion estimation approach. Finally, we evaluate the performance of our algorithm using both simulated and real-world data, analyzing its sensitivity and time complexity in comparison to established baselines.

This paper considers mutual interference mitigation among automotive radars using frequency-modulated continuous wave (FMCW) signal and multiple-input multiple-output (MIMO) virtual arrays. For the first time, we derive a spatial-domain interference signal model that accounts for not only the time-frequency incoherence (e.g., different FMCW parameters and time offsets) but also the slow-time MIMO code incoherence and array configuration differences between the victim and interfering radars. Using the explicit interference signal model with the standard MIMO-FMCW object signal model, we turn the interference mitigation into a spatial-domain object detection under incoherent MIMO-FMCW interference. By exploiting the structural property of the derived interference model at both transmit and receive steering vector space, we derive a detector via beamforming optimization to achieve good detection performance and further propose an adaptive version of this detector to enhance its practical applicability. Performance evaluation using analytical closed-form expressions, synthetic-level simulation and system-level simulation confirms the effectiveness of our proposed detectors over selected baseline methods.

Sidelink is a 3GPP-standardized technology that enables direct UE-to-UE communication. Initially designed for licensed 4G spectrum (Release 15), it expanded to 5G NR (Release 16, 17). However, due to the recent availability of large swaths of unlicensed spectrum (notably 6 GHz in U.S.), 3GPP has initiated studies to support sidelink operation in such unlicensed bands (denoted as SL-U). SL-U specifications ride on adaptation of 5G NR operation from licensed to unlicensed bands (NR-U). This paper provides a first analytical framework to quantify SL-U network performance in saturation conditions, for comparative evaluation to NR-U. A Markov chain model for NR-U is first presented, as a basis for necessary (significant) modifications needed to capture SL-U-specific features - the representation of gap states in the SL-U model to account for the restriction that SL-U transmissions only occur at SL slot boundaries. The resulting analysis highlights the differences and consequent (negative) impact of current 3GPP-defined SL-U channel access rulemaking; the analytical estimates are validated by results from a custom simulation of channel access. The primary takeaway is that the SL-U throughput under current 3GPP access rules suffers significantly relative to NR-U, and thus needs amelioration for successful marketplace adoption in the future.