arXiv subjects

Amjad Iqbal

Publications and source records attributed to Amjad Iqbal.

3 recordsLinked to original sources

arXiv2026

Heterogeneous Space Data Centers: A Key Paradigm for Future Non-Terrestrial and In-Orbit Services

Lower space-launch cost and advances in AI are driving space-based data centers (SDCs) that provide AI-centric computing and communication services to in-orbit and non-terrestrial assets. However, architectural principles, service models, and energy provisioning for such SDCs, particularly in low-Earth orbit (LEO), remain largely unexplored. We introduce heterogeneous SDCs composed of large and small nodes, differentiated by computing capability, size, and power budget, to support applications spanning ground, aerial, and in-orbit users. We define use cases, node sizing and placement strategies, and interconnection topologies integrating space-to-space and space-to-ground links. Focusing on dawn-dusk LEO configurations, we analyze solar power availability, visibility windows, and latency-aware coverage. Using baseline workloads for typical scenarios, we evaluate latency and service reliability. We quantify energy requirements of different SDC roles and deployment scales, highlighting trade-offs among node size, orbit selection, and service portfolio, and provide design guidance and future research directions for AI infrastructure in space.

eess.SY↗

arXiv2026

Constellation-Level Power Allocation for LEO Space-Based Solar Power

Space-based solar power (SBSP) has recently gained renewed attention as an appealing technological advancement for providing continuous clean energy using space-based infrastructure. However, the potential of low-Earth orbit (LEO) satellite constellations for SBSP remains largely unexplored and lacks detailed simulation-based studies. In this paper, we introduce a novel LEO SBSP system model and conduct a 24-hour system-level simulation of a Walker 4x5 LEO SBSP constellation at an altitude of 450 km, beaming 2.45 GHz microwave power to eight ground stations (GSs) under a greedy allocation policy. The model includes orbital propagation, eclipse cycles, the satellite power chain, Goubau-Brown beam coupling, ITU-R P.618 atmospheric attenuation, and onboard battery dynamics. The results confirm that the peak DC power delivered reaches 1.986 MW, while the mean per-site delivery at the served GS ranged from 40 to 75 kW. Two of the eight GSs received no service during the run, as their passes were consistently ranked lower under the greedy policy than competing links at the same step. The incident peak power density (PD) at the rectenna remained within the 3.35-5.72 W/m$^2$ range, below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) general-public exposure limit. For a 20-satellite Walker LEO at this altitude, realistic per-site delivery is 50-100 kW, and the rectenna should be sized to the operational incident PD of order 5 W/m$^2$ rather than to a Geostationary Earth Orbit (GEO)-era 100 W/m$^2$ rating.

eess.SY↗

arXiv2025

Machine Learning in Near-Field Communication for 6G: A Survey

6G wireless communication networks are expected to use extremely large-scale antenna arrays (ELAAs) to support higher throughput, massive connectivity, and improved system performance. ELAAs would fundamentally alter wave characteristics, transforming them from plane waves into spherical waves, thereby operating in the near field. Near-field communications (NFC) offer unique advantages to enhance system performance, but also present significant challenges in channel modeling, computational complexity, and beamforming design. The use of machine learning (ML) is emerging as a powerful approach to tackle such challenges and has the capabilities to enable intelligent, secure, and efficient 6G wireless communications. In this survey, we discuss ML-driven approaches for NFC. We first outline the fundamental concepts of NFC and ML. We then discuss ML applications in channel estimation, beamforming design, and security enhancement. We also highlight key challenges (e.g., data privacy and computational overhead). Finally, we discuss open issues and future directions to emphasize the role of advanced ML techniques in near-field system design.

cs.ET↗