Sharp Analysis of RLS-based Digital Precoder with Limited PAPR in Massive MIMO
This paper focuses on the performance analysis of a class of limited peak-to-average power ratio (PAPR) precoders for downlink multi-user massive multiple-input multiple-output (MIMO) systems. Contrary to conventional precoding approaches based on simple linear precoders such as maximum ratio transmission (MRT) and regularized zero-forcing (RZF), the precoders in this paper are obtained by solving a convex optimization problem. To be specific, these precoders are designed so that the power of each precoded symbol entry is restricted, and the PAPR at each antenna is tunable. By using the Convex Gaussian Min-max Theorem (CGMT), we analytically characterize the empirical distribution of the precoded vector and the joint empirical distribution between the distortion and the intended symbol vector. This allows us to study the performance of these precoders in terms of per-antenna power, per-user distortion power, signal-to-noise and distortion ratio (SINAD), and bit error probability. We show that for this class of precoders, there is an optimal transmit per-antenna power that maximizes the system performance in terms of SINAD and bit error probability.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Limited-Resolution Hybrid Analog-Digital Precoding Design Using MIMO Detection Methods
While fully digital precoding achieves superior performance in massive multiple-input multiple-output (MIMO) systems, it comes with significant drawbacks in terms of computational complexity and power consumption, partic…
A Novel Hybrid Precoder With Low-Resolution Phase Shifters and Fronthaul Capacity Limitation
In massive MIMO systems, fully digital precoding offers high performance but has significant implementation complexity and energy consumption, particularly at millimeter frequencies and beyond. Hybrid analog-digital arch…
QuantizationA Comprehensive Study of PAPR Reduction Techniques for Deep Joint Source Channel Coding in OFDM Systems
Recently, deep joint source channel coding (DJSCC) techniques have been extensively studied and have shown significant performance with limited bandwidth and low signal to noise ratio. Most DJSCC work considers discrete-…
Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non-Linear PA Distortion
Massive MIMO systems are typically designed assuming linear power amplifiers (PAs). However, PAs are most energy efficient close to saturation, where non-linear distortion arises. For conventional precoders, this distort…
Graph Neural NetworkLow PAPR Pilot for Delay-Doppler Domain Modulation
This paper studies the low PAPR pilot design in delay-Doppler domain modulation. We adopt a sequence based pilot design instead of the conventional pulse pilot, to mitigate the PAPR issue. We develop simple channel estim…