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Researcher profile

Li You

Li You contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SPInformation Theorymath.ITcond-mat.quant-gasquant-phphysics.atom-phMachine LearningArtificial Intelligencegr-qchep-thmath.PRmath.STNetworking and Internet Architecturephysics.opticsStatistics Theory

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Published work

23 published item(s)

preprint2026arXiv

Coherent Two-State Oscillations in False Vacuum Decay Regimes

Coherent two-state oscillations are observed in numerical simulations of the one-dimensional transverse-longitudinal-field Ising model (TLFIM) within false vacuum decay regimes. Starting from the false vacuum (a nearly fully polarized ferromagnetic state), we show that in moderate-sized systems, at resonances $h\approx 2J/n$ (with longitudinal field $h$, transverse field $J$, and an integer $n$), the expected decay can give way to coherent oscillations between the false vacuum and a symmetric resonant state. The oscillation frequency, i.e., the tunneling splitting, is observed notably to exhibit a superradiant-like $\sqrt{L}$ enhancement, as confirmed by a Schrieffer-Wolff analysis. In large chains, coherence remains for $n\gtrsim L/2$ due to bubble-size blockade and is robust against stronger transverse fields; for small $n$, long-range interactions can stabilize the oscillations by lifting multi-bubble degeneracies, establishing a robust many-body coherence mechanism beyond perturbative and finite-size limits.

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preprint2026arXiv

Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework

Channel fingerprint (CF) is considered a key enabler for facilitating the acquisition of channel state information (CSI) in massive multiple-input multiple-output (MIMO) communication systems. In this work, we investigate a novel type of CF that stores statistical CSI (sCSI) at each potential location, referred to as statistical CF (sCF). Specifically, we reveal the relationship between sCSI, namely the channel spatial covariance matrix (CSCM), and the channel power angular spectrum (CPAS). Building on this foundation, we construct a unified tensor representation of the sCF and further reduce its dimension by exploiting the eigenvalue decomposition of the CSCM and its correlation with the PAS. Considering the practical constraints imposed by measurement cost, privacy, and security, we focus on three representative scenarios and uniformly formulate them as tensor restoration tasks. To this end, we propose a unified tensor-based learning architecture, termed LPWTNet. The architecture incorporates a closed-form Laplacian pyramid (LP) decomposition and reconstruction framework that replaces the traditional encoder-decoder structure, enabling efficient inference while capturing multi-scale frequency subband characteristics of the sCF. Additionally, a shared mask learning strategy is introduced to adaptively refine high-frequency sCF components through level-wise adjustments. To achieve a larger receptive field without over-parameterization, we further propose a small-kernel convolution mechanism based on the wavelet transform (WT), which decouples convolution across different frequency components of the sCF and enhances feature extraction efficiency. Extensive experiments show that the proposed approach delivers competitive reconstruction accuracy and computational efficiency across various sCF construction scenarios when compared with state-of-the-art baselines.

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preprint2025arXiv

Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach

Accurate channel state information (CSI) acquisition for massive multiple-input multiple-output (MIMO) systems is essential for future mobile communication networks. Channel fingerprint (CF), also referred to as channel knowledge map, is a key enabler for intelligent environment-aware communication and can facilitate CSI acquisition. However, due to the cost limitations of practical sensing nodes and test vehicles, the resulting CF is typically coarse-grained, making it insufficient for wireless transceiver design. In this work, we introduce the concept of CF twins and design a conditional generative diffusion model (CGDM) with strong implicit prior learning capabilities as the computational core of the CF twin to establish the connection between coarse- and fine-grained CFs. Specifically, we employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CF conditioned on the coarse-grained CF, enabling the CGDM to learn the complicated distribution of the target data. During the denoising neural network optimization, the coarse-grained CF is introduced as side information to accurately guide the conditioned generation of the CGDM. To make the proposed CGDM lightweight, we further leverage the additivity of network layers and introduce a one-shot pruning approach along with a multi-objective knowledge distillation technique. Experimental results show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines. Additionally, zero-shot testing on reconstruction tasks with different magnification factors further demonstrates the scalability and generalization ability of the proposed approach.

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preprint2025arXiv

Specific Absorption Rate-Aware Multiuser MIMO Assisted by Fluid Antenna System

With the development of the upcoming sixth-generation (6G) wireless networks, there is a pressing need for innovative technologies capable of satisfying heightened performance indicators. Fluid antenna system (FAS) is proposed recently as a promising technique to achieve higher data rates and more diversity gains by dynamically changing the positions of the antennas to form a more desirable channel. However, worries regarding the possibly harmful effects of electromagnetic (EM) radiation emitted by devices have arisen as a result of the rapid evolution of advanced techniques in wireless communication systems. Specific absorption rate (SAR) is a widely adopted metric to quantify EM radiation worldwide. In this paper, we investigate the SAR-aware multiuser multiple-input multiple-output (MIMO) communications assisted by FAS. In particular, a two-layer iterative algorithm is proposed to minimize the SAR value under signal-to-interference-plus-noise ratio (SINR) and FAS constraints. Moreover, the minimum weighted SINR maximization problem under SAR and FAS constraints is studied by finding its relationship with the SAR minimization problem. Simulation results verify that the proposed SAR-aware FAS design outperforms the adaptive backoff and fixed-position antenna designs.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Spectral Efficiency Analysis of Near-Field Holographic MIMO over Ricean Fading Channels

With the denser distribution of antenna elements, stronger mutual coupling effects would kick in among antenna elements, which would eventually affect the communication performance. Meanwhile, as the holographic array usually has large physical size, the possibility of near-field communication increases. This paper investigates a near-field multi-user downlink HMIMO system and characterizes the spectral efficiency (SE) under the mutual coupling effect over Ricean fading channels. Both perfect and imperfect channel state information (CSI) scenarios are considered. (i) For the perfect CSI case, the mutual coupling and radiation efficiency model are first established. Then, the closed-form SE is derived under maximum ratio transmission (MRT). By comparing the SE between the cases with and without mutual coupling, it is unveiled that the system SE with mutual coupling might outperform that without mutual coupling in the low transmit power regime for a given aperture size. Moreover, it is also unveiled that the inter-user interference cannot be eliminated unless the physical size of the array increases to infinity. Fortunately, the additional distance term in the near-field channel can be exploited for the inter-user interference mitigation, especially for the worst case, where the users' angular positions overlap to a great extent. (ii) For the imperfect CSI case, the channel estimation error is considered for the derivation of the closed-form SE under MRT. It shows that in the low transmit power regime, the system SE can be enhanced by increasing the pilot power and the antenna element density, the latter of which will lead to severe mutual coupling. In the high transmit power regime, increasing the pilot power has a limited effect on improving the system SE. However, increasing the antenna element density remains highly beneficial for enhancing the system SE.

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preprint2023arXiv

Fluid Antenna-Assisted MIMO Transmission Exploiting Statistical CSI

In conventional multiple-input multiple-output (MIMO) communication systems, the positions of antennas are fixed. To take full advantage of spatial degrees of freedom, a new technology called fluid antenna (FA) is proposed to obtain higher achievable rate and diversity gain. Most existing works on FA exploit instantaneous channel state information (CSI). However, in FA-assisted systems, it is difficult to obtain instantaneous CSI since changes in the antenna position will lead to channel variation. In this letter, we investigate a FA-assisted MIMO system using relatively slow-varying statistical CSI. Specifically, in the criterion of rate maximization, we propose an algorithmic framework for transmit precoding and transmit/receive FAs position designs with statistical CSI. Simulation results show that our proposed algorithm in FA-assisted systems significantly outperforms baselines in terms of rate performance.

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preprint2022arXiv

Downlink Transmit Design for Massive MIMO LEO Satellite Communications

This paper investigates the downlink (DL) transmit design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication systems, where only the slow-varying statistical channel state information is exploited at the transmitter. The channel model for the DL massive MIMO LEO satellite system is established, in which both the satellite and the user terminals (UTs) are equipped with uniform planar arrays. Observing the rank-one property of the channel matrices, we show that the single-stream precoding for each UT is the optimal choice that maximizes the ergodic sum rate. This favorable result simplifies the complicated design of transmit covariance matrices into that of precoding vectors without any loss of optimality. Then, an efficient algorithm is devised to compute the precoding vectors. Furthermore, we formulate an approximate transmit design based on the upper bound on the ergodic sum rate, for which the optimality of single-stream precoding still holds. We show that, in this case, the design of precoding vectors can be simplified into that of scalar variables, for which an effective algorithm is developed. In addition, a low-complexity learning framework is proposed for optimizing the scalar variables. Simulation results demonstrate that the proposed approaches can achieve significant performance gains over the existing schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Energy Efficiency Maximization of Massive MIMO Communications With Dynamic Metasurface Antennas

Future wireless communications are largely inclined to deploy massive numbers of antennas at the base stations (BSs) by leveraging cost- and energy-efficient as well as environmentally friendly antenna arrays. The emerging technology of dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. The goal of this paper is the optimization of the energy efficiency (EE) performance of DMA-assisted massive multiple-input multiple-output (MIMO) wireless communications. Focusing on the uplink, we propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMA tuning strategy at the BS to maximize the EE performance, considering the availability of either instantaneous or statistical channel state information (CSI). Specifically, the proposed framework is shaped around Dinkelbach's transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design for the multiple-antenna case. Our numerical results verify the good convergence behavior of the proposed algorithms, and showcase the considerable EE performance gains of the DMA-assisted massive MIMO transmissions over the baseline schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hybrid Analog/Digital Precoding for Downlink Massive MIMO LEO Satellite Communications

Massive multiple-input multiple-output (MIMO) is promising for low earth orbit (LEO) satellite communications due to the potential in enhancing the spectral efficiency. However, the conventional fully digital precoding architectures might lead to high implementation complexity and energy consumption. In this paper, hybrid analog/digital precoding solutions are developed for the downlink operation in LEO massive MIMO satellite communications, by exploiting the slow-varying statistical channel state information (CSI) at the transmitter. First, we formulate the hybrid precoder design as an energy efficiency (EE) maximization problem by considering both the continuous and discrete phase shift networks for implementing the analog precoder. The cases of both the fully and the partially connected architectures are considered. Since the EE optimization problem is nonconvex, it is in general difficult to solve. To make the EE maximization problem tractable, we apply a closed-form tight upper bound to approximate the ergodic rate. Then, we develop an efficient algorithm to obtain the fully digital precoders. Based on which, we further develop two different efficient algorithmic solutions to compute the hybrid precoders for the fully and the partially connected architectures, respectively. Simulation results show that the proposed approaches achieve significant EE performance gains over the existing baselines, especially when the discrete phase shift network is employed for analog precoding.

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preprint2022arXiv

Hybrid RIS and DMA Assisted Multiuser MIMO Uplink Transmission With Electromagnetic Exposure Constraints

In the fifth-generation and beyond era, reconfigurable intelligent surface (RIS) and dynamic metasurface antennas (DMAs) are emerging metamaterials keeping up with the demand for high-quality wireless communication services, which promote the diversification of portable wireless terminals. However, along with the rapid expansion of wireless devices, the electromagnetic (EM) radiation increases unceasingly and inevitably affects public health, which requires a limited exposure level in the transmission design. To reduce the EM radiation and preserve the quality of communication service, we investigate the spectral efficiency (SE) maximization with EM constraints for uplink transmission in hybrid RIS and DMA assisted multiuser multiple-input multiple-output systems. Specifically, alternating optimization is adopted to optimize the transmit covariance, RIS phase shift, and DMA weight matrices. We first figure out the water-filling solutions of transmit covariance matrices with given RIS and DMA parameters. Then, the RIS phase shift matrix is optimized via the weighted minimum mean square error, block coordinate descent and minorization-maximization methods. Furthermore, we solve the unconstrainted DMA weight matrix optimization problem in closed form and then design the DMA weight matrix to approach this performance under DMA constraints. Numerical results confirm the effectiveness of the EM aware SE maximization transmission scheme over the conventional baselines.

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preprint2022arXiv

Massive MIMO Hybrid Precoding for LEO Satellite Communications With Twin-Resolution Phase Shifters and Nonlinear Power Amplifiers

The massive multiple-input multiple-output (MIMO) transmission technology has recently attracted much attention in the non-geostationary, e.g., low earth orbit (LEO) satellite communication (SATCOM) systems since it can significantly improve the energy efficiency (EE) and spectral efficiency. In this work, we develop a hybrid analog/digital precoding technique in the massive MIMO LEO SATCOM downlink, which reduces the onboard hardware complexity and power consumption. In the proposed scheme, the analog precoder is implemented via a more practical twin-resolution phase shifting (TRPS) network to make a meticulous tradeoff between the power consumption and array gain. In addition, we consider and study the impact of the distortion effect of the nonlinear power amplifiers (NPAs) in the system design. By jointly considering all the above factors, we propose an efficient algorithmic approach for the TRPS-based hybrid precoding problem with NPAs. Numerical results show the EE gains considering the nonlinear distortion and the performance superiority of the proposed TRPS-based hybrid precoding scheme over the baselines.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nonlinear interferometry beyond classical limit facilitated by cyclic dynamics

Time-reversed evolution has substantial implications in physics, including prominent applications in refocusing of classical waves or spins and fundamental researches such as quantum information scrambling. In quantum metrology, nonlinear interferometry based on time reversal protocols supports entanglement-enhanced measurements without requiring low-noise detection. Despite the broad interest in time reversal, it remains challenging to reverse the quantum dynamics of an interacting many-body system as is typically realized by an (effective) sign-flip of the system's Hamiltonian. Here, we present an approach that is broadly applicable to cyclic systems for implementing nonlinear interferometry without invoking time reversal. Inspired by the observation that the time-reversed dynamics drives a system back to its starting point, we propose to accomplish the same by slaving the system to travel along a 'closed-loop' instead of explicitly tracing back its antecedent path. Utilizing the quasi-periodic spin mixing dynamics in a three-mode $^{87}$Rb atom spinor condensate, we implement such a 'closed-loop' nonlinear interferometer and achieve a metrological gain of $3.87_{-0.95}^{+0.91}$ decibels over the classical limit for a total of 26500 atoms. Our approach unlocks the high potential of nonlinear interferometry by allowing the dynamics to penetrate into deep nonlinear regime, which gives rise to highly entangled non-Gaussian state. The idea of bypassing time reversal may open up new opportunities in the experimental investigation of researches that are typically studied by using time reversal protocols.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A concise review of Rydberg atom based quantum computation and quantum simulation

Quantum information processing based on Rydberg atoms emerged as a promising direction two decades ago. Recent experimental and theoretical progresses have shined exciting light on this avenue. In this concise review, we will briefly introduce the basics of Rydberg atoms and their recent applications in associated areas of neutral atom quantum computation and simulation. We shall also include related discussions on quantum optics with Rydberg atomic ensembles, which are increasingly used to explore quantum computation and quantum simulation with photons.

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preprint2021arXiv

Faster State Preparation across Quantum Phase Transition Assisted by Reinforcement Learning

An energy gap develops near quantum critical point of quantum phase transition in a finite many-body (MB) system, facilitating the ground state transformation by adiabatic parameter change. In real application scenarios, however, the efficacy for such a protocol is compromised by the need to balance finite system life time with adiabaticity, as exemplified in a recent experiment that prepares three-mode balanced Dicke state near deterministically [PNAS {\bf 115}, 6381 (2018)]. Instead of tracking the instantaneous ground state as unanimously required for most adiabatic crossing, this work reports a faster sweeping policy taking advantage of excited level dynamics. It is obtained based on deep reinforcement learning (DRL) from a multi-step training scheme we develop. In the absence of loss, a fidelity $\ge 99\%$ between prepared and the target Dicke state is achieved over a small fraction of the adiabatically required time. When loss is included, training is carried out according to an operational benchmark, the interferometric sensitivity of the prepared state instead of fidelity, leading to better sensitivity in about half of the previously reported time. Implemented in a Bose-Einstein condensate of $\sim 10^4$ $^{87}$Rb atoms, the balanced three-mode Dicke state exhibiting an improved number squeezing of $13.02\pm0.20$ dB is observed within 766 ms, highlighting the potential of DRL for quantum dynamics control and quantum state preparation in interacting MB systems.

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preprint2021arXiv

Universal relations between atomic dipolar relaxation and van der Waals interaction

Dipolar relaxation happens when one or both colliding atoms flip their spins exothermically inside a magnetic ($B$) field. This work reports precise measurements of dipolar relaxation in a Bose-Einstein condensate of ground state $^{87}$Rb atoms together with in-depth theoretical investigations. Previous perturbative treatments fail to explain our observations except at very small $B$-fields. By employing quantum defect theory based on analytic solutions of asymptotic van der Waals interaction $-C_6/R^6$ ($R$ being interatomic spacing), we significantly expand the applicable range of perturbative treatment. We find the $B$-dependent dipolar relaxation lineshapes are largely universal, determined by the coefficient $C_6$ and the associated $s$-wave scattering lengths $a_{\rm sc}$ of the states before and after spin flips. This universality, which applies generally to other atomic species as well, implicates potential controls of dipolar relaxation and related cold chemical reactions by tuning $a_{\rm sc}$.

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preprint2020arXiv

A divergent volume for black holes calls for no "firewall"

The presumption that Hawking radiations are thermally distributed can be considered to result from their entanglement with the internal degrees of freedom for a black hole. This leads to the "firewall" paradox if unitary evolution continues into Page's time when a black hole evaporates away half of its initial entropy. However, if the interior of a black hole houses sufficient degrees of freedom to maintain entanglement with the outside at all times, unitarity can be preserved during the complete radiation process and no firewall will be required. This paper proposes a scenario that rescinds firewall by introducing the concept of volume for a black hole. Based on the operational definition by Christodoulou and Rovelli [1], we show that the volume and its associated entropy for a collapsed black hole diverges if the final evaporation stage is treated using noncommutative space. This implicates the interior of a black hole possesses adequate space to store information for a black hole of any mass, like the inside of a "magician's bag".

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preprint2020arXiv

Double Degenerate Bose-Fermi Mixture of Strontium and Lithium

We report on the attainment of a degenerate Fermi gas of $\rm^{6}Li$ in contact with a Bose-Einstein condensate (BEC) of $^{84}$Sr. A degeneracy of $T/T_F=0.33(3)$ is observed with $1.6\times10^5$ $^{6}$Li atoms in the two lowest energy hyperfine states together with an almost pure BEC of $3.1\times10^5$ $^{84}$Sr atoms. The elastic s-wave scattering length between $^6$Li and $^{84}$Sr is estimated to be $|a_{\rm^{6}Li-\rm^{84}Sr}|=(7.1_{-1.7}^{+2.6})a_0$ ($a_0$ being the Bohr radius) from measured interspecies thermalization rates in an optical dipole trap.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Energy Efficiency and Spectral Efficiency Tradeoff in RIS-Aided Multiuser MIMO Uplink Transmission

The emergence of reconfigurable intelligent surfaces (RISs) enables us to establish programmable radio wave propagation that caters for wireless communications, via employing low-cost passive reflecting units. This work studies the non-trivial tradeoff between energy efficiency (EE) and spectral efficiency (SE) in multiuser multiple-input multiple-output (MIMO) uplink communications aided by a RIS equipped with discrete phase shifters. For reducing the required signaling overhead and energy consumption, our transmission strategy design is based on the partial channel state information (CSI), including the statistical CSI between the RIS and user terminals (UTs) and the instantaneous CSI between the RIS and the base station. To investigate the EE-SE tradeoff, we develop a framework for the joint optimization of UTs' transmit precoding and RIS reflective beamforming to maximize a performance metric called resource efficiency (RE). For the design of UT's precoding, it is simplified into the design of UTs' transmit powers with the aid of the closed-form solutions of UTs' optimal transmit directions. To avoid the high complexity in computing the nested integrals involved in the expectations, we derive an asymptotic deterministic objective expression. For the design of the RIS phases, an iterative mean-square error minimization approach is proposed via capitalizing on the homotopy, accelerated projected gradient, and majorization-minimization methods. Numerical results illustrate the effectiveness and rapid convergence rate of our proposed optimization framework.

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preprint2020arXiv

Energy Efficiency Optimization for Downlink Massive MIMO With Statistical CSIT

We investigate energy efficiency (EE) optimization for single-cell massive multiple-input multiple-output (MIMO) downlink transmission with only statistical channel state information (CSI) available at the base station. We first show that beam domain transmission is favorable for energy efficiency in the massive MIMO downlink, by deriving a closed-form solution for the eigenvectors of the optimal transmit covariance matrix. With this conclusion, the EE optimization problem is reduced to a real-valued power allocation problem, which is much easier to tackle than the original large-dimensional complex matrix-valued precoding design problem. We further propose an iterative water-filling-structured beam domain power allocation algorithm with low complexity and guaranteed convergence, exploiting the techniques from sequential optimization, fractional optimization, and random matrix theory. Numerical results demonstrate the near-optimal performance of our proposed statistical CSI aided EE optimization approach.

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preprint2020arXiv

High-resolution imaging of Rydberg atoms in optical lattices using an aspheric-lens objective in vacuum

We present a high-resolution, simple and versatile system for imaging ultracold Rydberg atoms in optical lattices. The imaging objective is a single aspheric lens (with a working distance of 20.6 mm and a numerical aperture (NA) of 0.51) placed inside the vacuum chamber. Adopting a large-working-distance lens leaves room for electrodes and electrostatic shields to control electric fields around Rydberg atoms. With this setup, we achieve an Rayleigh resolution of 1.10 $μ$m or $1.41λ$ ($λ=780$ nm), limited by the NA of the aspheric lens. For systems of highly excited Rydberg states with blockade radii greater than a few $μ$m, the resolution achieved is sufficient for studying many physical processes of interest.

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preprint2020arXiv

Massive MIMO Transmission for LEO Satellite Communications

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive MIMO techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

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preprint2020arXiv

Multi-parameter estimation with multi-mode Ramsey interferometry

Estimating multiple parameters simultaneously is of great importance to measurement science and application. For a single parameter, atomic Ramsey interferometry (or equivalently optical Mach-Zehnder interferometry) is capable of providing the precision at the standard quantum limit (SQL) using unentangled probe states as input. In such an interferometer, the first beam splitter represented by unitary transformation $U$ generates a quantum phase sensing superposition state, while the second beam splitter $U^{-1}$ recombines the phase encoded paths to realize interferometric sensing in terms of population measurements. We prove that such an interferometric scheme can be directly generalized to estimation of multiple parameters (associated with commuting generators) to the SQL precision using multi-mode unentangled states, if (but not iff) $U$ is orthogonal, i.e. a unitary transformation with only real matrix elements. We show that such a $U$ can always be constructed experimentally in a simple and scalable manner. The effects of particle number fluctuation and detection noise on such multi-mode interferometry are considered. Our findings offer a simple solution for estimating multiple parameters corresponding to mutually commuting generators.

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preprint2020arXiv

Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission with Statistical CSIT

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.

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