arXiv subjects

Mohd Saif Ali Khan

Publications and source records attributed to Mohd Saif Ali Khan.

6 recordsLinked to original sources

arXiv2026

Adaptive Local Combining with Decentralized Decoding for Distributed Massive MIMO

Efficient uplink processing in distributed massive multiple-input multiple-output (D-mMIMO) systems requires both effective local combining and scalable decoding to significantly mitigate inter-user interference. Recent zero-forcing (ZF)-based combining schemes, such as partial full-pilot ZF (PFZF) and protected weak PFZF (PWPFZF), rely on heuristic threshold-based user grouping that may lead to inefficient utilization of spatial degrees of freedom across access points (APs). To address this limitation, we propose adaptive pilot-aware local combining strategies, generalized PFZF (G-PFZF) and generalized PWPFZF (G-PWPFZF), that dynamically allocate spatial degrees of freedom based on local channel conditions and replace heuristic grouping with a decentralized pilot-level optimization framework. Thus providing substantial performance gains over conventional PFZF and PWPFZF. Further, centralized decoding has recently emerged as a promising technique for interference suppression in D-mMIMO systems. However, it incurs substantial fronthaul overhead and computational costs. We develop a decentralized large-scale fading decoding (d-LSFD) scheme in which each AP computes LSFD weights using only locally available channel statistics. We derive a lower bound on the signal-to-interference-plus-noise ratio that explicitly quantifies the performance gap between the proposed d-LSFD scheme and centralized LSFD (c-LSFD), and identifies conditions under which the proposed decentralized solution approaches the centralized optimum. Numerical results demonstrate that the proposed generalized combining and the d-LSFD scheme together achieve significantly higher sum spectral efficiency in comparison to any combination of existing local combining and decoding schemes, while also substantially reducing the computational cost and fronthaul overhead.

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arXiv2026

Energy-and Spectral-Efficiency Trade-off in Distributed Massive-MIMO Networks

This paper investigates the energy efficiency (EE) and spectral efficiency (SE) trade-off in uplink distributed massive multiple-input multiple-output (D-mMIMO) systems. Unlike conventional approaches where power consumption focuses primarily on transmit power, we use a comprehensive system-level power consumption framework which incorporates consumption due to fronthaul signaling, distributed processing, and circuit level power, which are, themselves, critically influenced by the dynamic access point (AP) activation (ON-/OFF decisions), and AP-user equipment (UE) association strategies. Consequently, we analyze the EE-SE trade-off through the joint optimization of transmit power allocation, AP activation, and AP-UE association. We formulate an optimization problem that maximizes EE while satisfying sum-SE constraints, per-user minimum SE requirements, and fronthaul capacity limits. Our solution uses a fractional programming-based approach to simultaneously determine transmit power levels, dynamic AP-UE associations, and AP activation strategies. Numerical results demonstrate that dynamic AP activation and association substantially impact the EE-SE trade-off, revealing optimal operating points that balance spectral performance with energy consumption. The findings provide practical guidelines for energyefficient D-mMIMO deployment in next generation wireless networks, highlighting the importance of adaptive resource allocation in achieving sustainable high-performance communications.

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arXiv2025

Pilot Assignment for Distributed Massive MIMO Based on Channel Estimation Error Minimization

Pilot contamination remains a major bottleneck in realizing the full potential of distributed massive MIMO systems. We propose two dynamic and scalable pilot assignment schemes designed for practical deployment in such networks. First, we present a low-complexity centralized scheme that sequentially assigns pilots to user equipments (UEs) to minimize the global channel estimation errors across serving access points (APs). This improves the channel estimation quality and reduces interference among UEs, enhancing the spectral efficiency. Second, we develop a fully distributed scheme that uses a priority-based pilot selection approach. In this scheme, each selected AP minimizes the channel estimation error using only local information and offers candidate pilots to the UEs. Every UE then selects a suitable pilot based on its AP priority. This approach ensures consistency and minimizes interference while significantly reducing pilot contamination. The method requires no global coordination, maintains low signaling overhead, and adapts dynamically to the UE deployment. Numerical simulations demonstrate the superiority of the proposed schemes in terms of network throughput when compared to the existing state-of-the-art schemes.

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arXiv2024

Distributed Pilot Assignment for Distributed Massive-MIMO Networks

Pilot contamination is a critical issue in distributed massive MIMO networks, where the reuse of pilot sequences due to limited availability of orthogonal pilots for channel estimation leads to performance degradation. In this work, we propose a novel distributed pilot assignment scheme to effectively mitigate the impact of pilot contamination. Our proposed scheme not only reduces signaling overhead, but it also enhances fault-tolerance. Extensive numerical simulations are conducted to evaluate the performance of the proposed scheme. Our results establish that the proposed scheme outperforms existing centralized and distributed schemes in terms of mitigating pilot contamination and significantly enhancing network throughput.

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arXiv2024

Joint AP-UE Association and Power Factor Optimization for Distributed Massive MIMO

The uplink sum-throughput of distributed massive multiple-input-multiple-output (mMIMO) networks depends majorly on Access point (AP)-User Equipment (UE) association and power control. The AP-UE association and power control both are important problems in their own right in distributed mMIMO networks to improve scalability and reduce front-haul load of the network, and to enhance the system performance by mitigating the interference and boosting the desired signals, respectively. Unlike previous studies, which focused primarily on addressing these two problems separately, this work addresses the uplink sum-throughput maximization problem in distributed mMIMO networks by solving the joint AP-UE association and power control problem, while maintaining Quality-of-Service (QoS) requirements for each UE. To improve scalability, we present an l1-penalty function that delicately balances the trade-off between spectral efficiency (SE) and front-haul signaling load. Our proposed methodology leverages fractional programming, Lagrangian dual formation, and penalty functions to provide an elegant and effective iterative solution with guaranteed convergence. Extensive numerical simulations validate the efficacy of the proposed technique for maximizing sum-throughput while considering the joint AP-UE association and power control problem, demonstrating its superiority over approaches that address these problems individually. Furthermore, the results show that the introduced penalty function can help us effectively control the maximum front-haul load.

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arXiv2023

Distributed Resource Allocation for D2D Multicast in Underlay Cellular Networks

We address the problem of distributed resource allocation for multicast communication in device-to-device (D2D) enabled underlay cellular networks. The optimal resource allocation is crucial for maximizing the performance of such networks, which are limited by the severe co-channel interference between cellular users (CU) and D2D multicast groups. However, finding such optimal allocation for networks with large number of CUs and D2D users is challenging. Therefore, we propose a pragmatic scheme that allocates resources distributively, reducing signaling overhead and improving network scalability. Numerical simulations establish the efficacy of the proposed solution in improving the overall system throughout, compared to various existing schemes.

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