Precoding Design and Power Control for SINR Maximization of MISO System With Nonlinear Power Amplifiers

Jee, Jeongju; Kwon, Girim; Park, Hyuncheol

doi:10.1109/tvt.2020.3026752

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…2. 8PSK, 'non-strict phase rotation', [28] which gains further performance improvements, and the strict phase rotation is suboptimal. In addition, both the strict phase rotation and the non-strict phase rotation can only be applied to PSK modulation, but not to QAM modulation.…”

Section: Constructive Interferencementioning

confidence: 99%

“…[27] investigated a PA-aware precoding scheme in massive MU-MIMO downlink system, and developed an efficient algorithm to reduce the MUI and PA nonlinearity. In a single-user MISO communication system with nonlinear PAs at BS, [28] developed a power control method and a precoding scheme that maximized the received SINR, where an iterative precoding algorithm was presented. [29] jointly optimized the precoding and power allocation strategy to maximize the achievable sum rate of MU-MIMO systems.…”

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confidence: 99%

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Exploiting Power Amplifier Nonlinearities Through Symbol-Level Interference Exploitation Precoding in the MU-MIMO Downlink

Wei,

Li,

Masouros

2024

IEEE Trans. Wireless Commun.

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In this paper, we study the interference exploitation precoding in the presence of distortion from nonlinear power amplifiers (PAs) in multi-user multiple-input single-output (MU-MISO) downlink communication systems. We consider the memoryless polynomial model of nonlinear PAs, which is incorporated into the symbol-level precoding (SLP) design to allow the PA nonlinearities in the constructive interference (CI) exploitation. The optimization problem that aims to enhance the signal-tointerference-plus-noise ratio (SINR) without investing additional transmit signal power is formulated for both PSK and QAM signaling. Since the original optimization problem is nonconvex, we first introduce auxiliary variables to transform the optimization problem and adopt the alternating optimization framework for the new optimization problem. For non-convex subproblems, additional auxiliary variables are introduced and several approximations are employed to transform the problem into a semidefinite programming (SDP) form, where the semidefinite relaxation (SDR) method is adopted to obtain feasible solutions. In order to reduce the computational cost of the iterative algorithm, we further propose a low-complexity algorithm for the original PA-aware SLP optimization problem. Numerical results verify the superiority of our proposed PA-aware SLP approach in the presence of nonlinear PAs in the MU-MISO downlink in terms of the error-rate performance over the state-of-the-art.

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Section: Constructive Interferencementioning

confidence: 99%

mentioning

confidence: 99%

Exploiting Power Amplifier Nonlinearities Through Symbol-Level Interference Exploitation Precoding in the MU-MIMO Downlink

Wei,

Li,

Masouros

2024

IEEE Trans. Wireless Commun.

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“…The quantity of disappointments in both calculations is comparative in this strategy with the slightest quantity of purchasers and most limited term. In [11] We discovered the upward thrust to pick out up transmission diagram that can finish the fundamental remarkable SINR of a nonlinear MISO device because it had been at the point of neighborhood ideal transmit manage via examination of the flag to obstructions also commotion extent (SINR). After we decided an evaluated perfect transmit control for the nonlinear MISO system.…”

Section: IImentioning

confidence: 99%

Radio Resource Allocation for 5G Network Using Deep Reinforcement Learning

Umesh¹,

N²,

Santhosh³

et al. 2023

IJRASET

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Resource allocation is a critical task in 5Gnetworks that determines how network resources are assigned to different devices and services. Traditional methods rely on predefined rules or heuristics, which may not always be optimal. Deep reinforcement learning (DRL)is a promising approach for radio resource allocation in 5Gnetworks as it can learn to optimize resource allocation based on feedback from the network. In DRL, an agent learns to make decisions based on rewards and penalties received from the environment. In radio resource allocation, the agent would learn to allocate resources, such as frequency bands and power levels, to different devices and services to maximize some performance metric, such asthroughput or energy efficiency. The main challenge in applying DRL to radio resource allocation is designing an appropriate reward function that incentivizes the agent to improve the performance metric while avoiding undesirable behavior. Additionally, the radio resource allocation problem is complex, requiring the agent to consider many variables and constraints, such as channel conditions, interference, and QoS requirements. To address this, researchers have proposed various techniques such as hierarchical RL, multi-agent RL, and curriculum learning. Despite the challenges, DRL has shown promising results inradio resource allocation for 5G networks. It has outperformed traditional methods in some scenarios, especially when network conditions are dynamic and unpredictable. However, further research is necessary to explore the scalability and robustness of DRL-based approaches in practical 5G networks. In this method we suggest an algorithm for voice and data carriers in sub-6 GHz and millimeter wave (mmWave) frequencies respectively. The mmWave ranges between 30GHz to 300GHz

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“…The proposed method maximizes the received SINR while utilizing an iterative precoding algorithm. Finally, Jee et al [51] optimized both precoding and power allocation strategies jointly to maximize the achievable sum rate of MU-MIMO systems.…”

Section: Nonlinearity-aware Precodingmentioning

confidence: 99%

Spatial Multiplexing for MIMO/Massive MIMO

Wang¹,

Li²

2023

MIMO Communications - Fundamental Theory, Propagation Channels, and Antenna Systems

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In this chapter, we will discuss how to achieve spatial multiplexing in multiple-input multiple-output (MIMO) communications through precoding design, for both traditional small-scale MIMO systems and massive MIMO systems. The mathematical description for MIMO communications will first be introduced, based on which we discuss both block-level precoding and the emerging symbol-level precoding techniques. We begin with simple and closed-form block-level precoders such as maximum ratio transmission (MRT), zero-forcing (ZF), and regularized ZF (RZF), followed by the classic symbol-level precoding schemes such as Tomlinson-Harashima precoder (THP) and vector perturbation (VP) precoder. Subsequently, we introduce optimization-based precoding solutions, including power minimization, SINR balancing, symbol-level interference exploitation, etc. We extend our discussion to massive MIMO systems and particularly focus on precoding designs for hardware-efficient massive MIMO systems, such as hybrid analog-digital precoding, low-bit precoding, nonlinearity-aware precoding, etc.

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Precoding Design and Power Control for SINR Maximization of MISO System With Nonlinear Power Amplifiers

Cited by 11 publications

References 16 publications

Exploiting Power Amplifier Nonlinearities Through Symbol-Level Interference Exploitation Precoding in the MU-MIMO Downlink

Exploiting Power Amplifier Nonlinearities Through Symbol-Level Interference Exploitation Precoding in the MU-MIMO Downlink

Radio Resource Allocation for 5G Network Using Deep Reinforcement Learning

Spatial Multiplexing for MIMO/Massive MIMO

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