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Naveed Khan

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Review Open access 2026

Deep Space Communication Systems: Foundations, Networking Architectures, and a Roadmap Toward the Interplanetary Internet

Deep Space Communication (DSC) is a critical enabler for reliable data exchange between Earth-based infrastructure and spacecraft operating beyond lunar orbit. This paper presents a comprehensive and up-to-date survey of DSC systems, encompassing architectural foundations, enabling technologies, and emerging research challenges. In particular, the structure and operation of Deep Space Communication Networks (DSCNs) are examined, highlighting the functional interactions among deep space stations, communication complexes, signal processing centers, and mission control centers under severe propagation delays and intermittent connectivity. The paper provides a systematic review of traditional Radio Frequency (RF) communication and emerging Free-Space Optical (FSO) technologies, including hybrid RF/FSO architectures, and analyzes their trade-offs in terms of robustness, bandwidth efficiency, power consumption, and operational complexity. Recent mission demonstrations and international technology roadmaps are discussed to illustrate the ongoing transition toward high-capacity optical links for future lunar, Martian, and deep-space missions. Furthermore, advances at the physical and link layers are surveyed, covering modulation techniques, forward error correction schemes, and adaptive link optimization, with particular emphasis on Low-Density Parity-Check (LDPC) codes, Polar codes, hybrid forward error correction (FEC) schemes, and Adaptive Coding and Modulation (ACM) for operation under low signal-to-noise ratios and time-varying channels. At the networking layer, the paper reviews Consultative Committee for Space Data Systems (CCSDS) standards and Delay/Disruption-Tolerant Networking (DTN) protocols, identifying key open research challenges related to scalability, routing, buffering, quality-of-service support, and autonomous operation. By integrating physical-layer technologies, networking protocols, and system-level considerations, this work outlines emerging trends, including AI-native communication architectures, that are expected to shape the design of scalable, autonomous, and interoperable interplanetary communication networks.

Maryam Alshehhi, Doaa Mahmoud, Sara N. Ahmad et al. · 0 citations
Open access 2026

Scalable IRS Panel Selection for UAV-Assisted Uplink Networks

This paper investigates a UAV-assisted multi-panel intelligent reflecting surface (IRS) architecture to enhance link reliability and spectral efficiency in next-generation wireless networks. Unlike conventional IRS deployments with fixed geometries, UAV mobility introduces rapidly varying propagation conditions that challenge classical diversity and combining schemes. To address this, a Maximal Ratio Combining with Generalized Selection (MRC-GS) framework is proposed, in which the UAV optimizes its aerial position while IRS panels are selectively activated based on their contribution to the received signal. A joint optimization problem is formulated to design the IRS phase shifts, UAV position, and receiver combining weights. Hardware impairments are explicitly incorporated in the receiver combining design via impairments-aware noise covariance. The IRS phase optimization adopts a tractable effective-channel formulation and establishes the equivalence between maximizing the effective channel gain and signal-to-impairments-plus-noise ratio (SINR) under the adopted hardware impairment model. By activating only a dominant subset of IRS panels, the proposed approach improves energy efficiency and reduces computational overhead compared to full-activation schemes. Moreover, a low-complexity closed-form phase update is also provided for large-scale deployments. Simulation results demonstrate that the proposed MRC-GS-enabled UAV-IRS system reduces bit error rate (BER) and enhances spectral efficiency compared to benchmark single-IRS and full-combining schemes.

Nasir Saeed, Shumaila Javaid, Naveed Khan et al. · 0 citations

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