arXiv subjects

Ashkan Sheikhi

Publications and source records attributed to Ashkan Sheikhi.

4 recordsLinked to original sources

arXiv2025

Zero-Forcing MU-MIMO Precoding under Power Amplifier Non-Linearities

In multi-user multiple-input multiple-output (MU-MIMO) systems, the non-linear behavior of the power amplifiers (PAs) may cause degradation of the linear precoding schemes dealing with interference between user equipments (UEs), e.g., the zero-forcing (ZF) precoder. One way to minimize this effect is to use digital-pre-distortion (DPD) modules to linearize the PAs. However, using perfect DPD modules is costly and it may incur significant power consumption. As an alternative, we consider the problem of characterizing non-linearity-aware ZF (NLA-ZF) precoding schemes, hereby defined as linear precoders that achieve perfect interference cancellation in the presence of PA non-linearity by exploiting knowledge of this non-linear response. We provide initial iterative solutions that allow achieving NLA-ZF (up to adjustable tolerance) in a two-UE downlink MU-MIMO scenario where the base station (BS) has an even number of antennas, and each antenna is connected to a PA exhibiting third-order memory-less non-linear behavior. The proposed approach allows for performance gains in scenarios with significant residual interference.

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arXiv2025

Large Intelligent Surfaces with Low-End Receivers: From Scaling to Antenna and Panel Selection

Feasibility of the promising large intelligent surface (LIS) concept, as well as its scalability, relies on the use of low-cost hardware components, raising concerns about the effects of hardware distortion. We analyze LIS systems with receive-chain (RX-chain) hardware distortion, showing how it may limit performance gains when scaling up these systems. In particular, using the memory-less polynomial model, analytical expressions are derived for the signal to noise plus distortion ratio (SNDR) after applying maximum ratio combining (MRC). We also study the effect of back-off and automatic gain control on the RX-chains. The derived expressions enable us to evaluate the scalability of LIS when hardware impairments are present. The cost of assuming ideal hardware is further analyzed by quantifying the minimum scaling required to achieve the same performance with non-ideal hardware. The analytical expressions derived in this work are also used to propose practical antenna selection schemes for LIS, and we show that such schemes can improve the performance significantly leading to increased energy efficiency. Specifically, by turning off RX-chains with lower contribution to the post-MRC SNDR, we can reduce the energy consumption while maintaining performance. We also consider a more practical scenario where the LIS is deployed as a grid of multi-antenna panels, and we propose panel selection schemes to optimize the complexity-performance trade-offs and improve the system overall efficiency.

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arXiv2025

Over-the-Air DPD and Reciprocity Calibration in Massive MIMO and Beyond

Non-linear transceivers and non-reciprocity of downlink and uplink channels are two major challenges in the deployment of massive multiple-input-multiple-output (MIMO) systems. We consider an over-the-air (OTA) approach for digital pre-distortion (DPD) and reciprocity calibration to jointly address these issues. In particular, we consider a memory-less non-linearity model for the base station (BS) transmitters, and we propose a method to perform both linearization and reciprocity calibration based on mutual coupling OTA measurements between BS antennas. We show that, by using only the OTA-based data, we can linearize the transmitters and design the calibration to compensate for both the non-linearity and non-reciprocity of BS transceivers. This allows alleviating the requirement to have dedicated hardware modules for transceiver linearization. Moreover, the proposed reciprocity calibration method is solely based on closed-form linear transformations, achieving a significant complexity reduction over state-of-the-art reciprocity methods, which assume linear transceivers, and rely on iterative methods. Simulation results showcase the potential of our approach in terms of the calibration matrix estimation error and downlink data-rates when applying zero-forcing (ZF) precoding after using our OTA-based DPD and reciprocity calibration method.

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arXiv2025

Hardware Distortion Modeling for Panel Selection in Large Intelligent Surfaces

Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the effects of nonlinear RX-chains in LISs. We first derive expressions for SNDR of a LIS with a memory-less polynomial-based model at its RX-chains. Then we propose a simplified double-parameter exponential model for the distortion power and show that compared to the polynomial based model, the exponential model can improve the analytical tractability for SNDR optimization problems. In particular, we consider a panel selection optimization problems in a panel-based LIS scenario and show that the proposed model enables us to derive two closed-form sub-optimal solutions for panel selection, and can be a favorable alternative to high-order polynomial models in terms of computation complexity, especially for theoretical works on hardware distortion in MIMO and LIS systems. Numerical results show that the sub-optimal closed-form solutions have a near-optimal performance in terms of SNDR compared to the global optimum found by high-complexity heuristic search methods.

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