Randomized Channel Sparsifying Hybrid Precoding for FDD Massive MIMO Systems

Tian, Chang; Liu, An; Khalilsarai, Mahdi Barzegar; Caire, Giuseppe; Luo, Wu; Zhao, Minjian

doi:10.1109/twc.2020.2993442

Cited by 11 publications

(8 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Minimizing the MSE over the DL TS matrix X p for a given TS duration τ p and training power P p in the DL FDD mMIMO systems equates to the optimization problem defined in (20) under the transmit energy constraint.…”

Section: B Formulation Of the Mse Minimization Problemmentioning

confidence: 99%

See 1 more Smart Citation

Sum Rate Maximization Versus MSE Minimization in FDD Massive MIMO Systems With Short Coherence Time

et al. 2021

View full text Add to dashboard Cite

The increasing demand for higher data rates motivates the exploration of advanced techniques for future wireless networks. To this end, massive multiple-input multiple-output (mMIMO) is envisioned as the most essential technique to meet this demand. However, the expansion of the number of antennas in mMIMO systems with short coherence time makes the downlink channel estimation (DCE) overhead potentially overwhelming. As such, the number of training sequence (TS) needs to be significantly reduced. However, reducing the number of TS reduces the mean-squared error (MSE) accuracy significantly and to date it is not clear to what extend can this TS reduction affects the achievable sum rate performance. Therefore, this paper develops a low complexity and tractable TS solution for DCE and establishes an analytical framework for the optimum TS. Furthermore, the tradeoff between the achievable sum rate maximization criteria and the MSE minimization criteria is investigated. This investigation is essential to characterize the optimum TS length and the actual performance of mMIMO systems when the channel exhibits a limited coherence time. To this end, the statistical structure of mMIMO channels is exploited. In addition, this paper utilizes a random matrix theory (RMT) method to characterize the downlink achievable sum rate and MSE in a closed-form. This paper shows that maximizing the downlink sum rate criterion is more important than minimizing the MSE of the SINR only, which is typically considered in the conventional MIMO systems and/or in the time division duplex (TDD) mMIMO systems. The results demonstrate that a feasible downlink achievable sum rate can be achieved in an frequency division duplex (FDD) mMIMO system. This finding is necessary to extend the benefit of mMIMO systems to high frequency bands such as millimeter-wave (mmWave) and Terahertz (THZ) communications.INDEX TERMS Massive MIMO transmission, downlink channel estimation, achievable sum rate maximization, frequency division duplex operation mode, second order channel statistics, random matrix theory, mean square error minimization.

show abstract

Section: B Formulation Of the Mse Minimization Problemmentioning

confidence: 99%

“…In practice, however, the transceiver hardware impairments and calibration error can be considered as a major restriction with TDD mode of operation [15]- [18]. Therefore, there is an essential commercial interest in enabling FDD operation mode, thus making mMIMO systems compatible with the currently deployed cellular networks [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Sum Rate Maximization Versus MSE Minimization in FDD Massive MIMO Systems With Short Coherence Time

et al. 2021

View full text Add to dashboard Cite

show abstract

“…3) lim t→∞ βt αt = 0 Note that the condition 1 αt ≤ O (t κ ) for some κ ∈ (0, 1) is almost the same as t α t = ∞, which is a common assumption in stochastic optimization algorithms [29], [30]. A typical choice of {α t } and {β t } satisfying Assumption 2 is α t = t −κ1 and β t = t −κ2 , where κ 1 ∈ (0.5, 1), κ 2 ∈ (0.5, 1] and κ 1 < κ 2 .…”

Section: Convergence Analysismentioning

confidence: 99%

“…We adopt a geometry-based channel model as in [30] with a half-wavelength spaced uniform linear array (ULA) for simulations. The channel vector of user k can be expressed as h k = Np i=1 ᾱk,i a (ϕ k,i ), where a (ϕ) = 1, e jπ sin(ϕ) , .…”

Section: A Delay-constrained Power Control For Downlink Mu-mimomentioning

confidence: 99%

See 1 more Smart Citation

Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learning

Tian,

Liu,

Huang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We propose a successive convex approximation based off-policy optimization (SCAOPO) algorithm to solve the general constrained reinforcement learning problem, which is formulated as a constrained Markov decision process (CMDP) in the context of average cost. The SCAOPO is based on solving a sequence of convex objective/feasibility optimization problems obtained by replacing the objective and constraint functions in the original problems with convex surrogate functions. At each iteration, the convex surrogate problem can be efficiently solved by Lagrange dual method even the policy is parameterized by a high-dimensional function. Moreover, the SCAOPO enables to reuse old experiences from previous updates, thereby significantly reducing the implementation cost when deployed in the real-world engineering systems that need to online learn the environment. In spite of the time-varying state distribution and the stochastic bias incurred by the off-policy learning, the SCAOPO with a feasible initial point can still provably converge to a Karush-Kuhn-Tucker (KKT) point of the original problem almost surely.

show abstract

Fairness-based Bidding Model for Renewable Portfolio Standard Policy in South Korea Standard Policy in South Korea

Kim

Seok

2022

J. Electr. Eng. Technol.

View full text Add to dashboard Cite

Recent studies have shown impressive performance in Lip-tospeech synthesis that aims to reconstruct speech from visual information alone. However, they have been suffering from synthesizing accurate speech in the wild, due to insufficient supervision for guiding the model to infer the correct content. Distinct from the previous methods, in this paper, we develop a powerful Lip2Speech method that can reconstruct speech with correct contents from the input lip movements, even in a wild environment. To this end, we design multitask learning that guides the model using multimodal supervision, i.e. text and audio, to complement the insufficient word representations of acoustic feature reconstruction loss. Thus, the proposed framework brings the advantage of synthesizing speech containing the right content of multiple speakers with unconstrained sentences. We verify the effectiveness of the proposed method using LRS2, LRS3, and LRW datasets.

show abstract

Randomized Channel Sparsifying Hybrid Precoding for FDD Massive MIMO Systems

Cited by 11 publications

References 33 publications

Sum Rate Maximization Versus MSE Minimization in FDD Massive MIMO Systems With Short Coherence Time

Sum Rate Maximization Versus MSE Minimization in FDD Massive MIMO Systems With Short Coherence Time

Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learning

Fairness-based Bidding Model for Renewable Portfolio Standard Policy in South Korea Standard Policy in South Korea

Contact Info

Product

Resources

About