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In many practical parameter estimation problems, prescreening and parameter selection are performed prior to estimation. In this paper, we consider the problem of estimating a preselected unknown deterministic parameter chosen from a…

Information Theory · Computer Science 2016-09-21 Tirza Routtenberg , Lang Tong

This paper studies the multilevel Monte-Carlo estimator for the expectation of a maximum of conditional expectations. This problem arises naturally when considering many stress tests and appears in the calculation of the interest rate…

Computational Finance · Quantitative Finance 2021-04-14 Aurélien Alfonsi , Adel Cherchali , Jose Arturo Infante Acevedo

This paper studies predictor functions motivated by maximizing a measure of agreement with the predictand. Specifically, it examines distributional properties and predictive performance of the estimated maximum agreement linear predictor…

Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. However, due to the adoption of fully analog or hybrid analog/digital architectures, as well as non-ideal hardware or…

Signal Processing · Electrical Eng. & Systems 2021-02-19 Yu Zhang , Muhammad Alrabeiah , Ahmed Alkhateeb

This work provides a comprehensive scaling law and performance analysis for multi-user massive MIMO relay networks, where the relay is equipped with massive antennas and uses MRC/MRT for low-complexity processing. CSI error is considered.…

Networking and Internet Architecture · Computer Science 2016-06-27 Qian Wang , Yindi Jing

Multipactor is a nonlinear electron avalanche phenomenon that can severely impair the performance of high-power radio frequency (RF) devices and accelerator systems. Accurate prediction of multipactor susceptibility across different…

Accelerator Physics · Physics 2025-07-25 Asif Iqbal , John Verboncoeur , Peng Zhang

Automatic modulation recognition (AMR) critically contributes to spectrum sensing, dynamic spectrum access, and intelligent communications in cognitive radio systems. The introduction of deep learning has greatly improved the accuracy of…

Signal Processing · Electrical Eng. & Systems 2024-12-12 Shuo Wang , Kuojun Yang , Zelin Ji , Qinchuan Zhang , Huiqing Pan

Estimation of autocorrelations and spectral densities is of fundamental importance in many fields of science, from identifying pulsar signals in astronomy to measuring heart beats in medicine. In circumstances where one is interested in…

Methodology · Statistics 2013-01-22 C. H. Fleming , J. M. Calabrese

In this paper, a two-stage channel estimation scheme for two-way MIMO relay systems with a single relay antenna is proposed. The backward channel is estimated by using linear minimum mean square estimator (LMMSE) at the first stage, where…

Information Theory · Computer Science 2021-12-15 Huiming Chen , Xiaohan Zhong

Grant-free massive random access (RA) is a promising protocol to support the massive machine-type communications (mMTC) scenario in 5G and beyond networks. In this paper, we focus on the error rate analysis in grant-free massive RA, which…

Information Theory · Computer Science 2022-05-12 Xinyu Bian , Yuyi Mao , Jun Zhang

Future wireless communication systems are envisioned to share radio frequency (RF) spectrum, with other services such as radars, in order to meet the growing spectrum demands. In this paper, we consider co-channel spectrum sharing between…

Information Theory · Computer Science 2016-11-18 Awais Khawar , Ahmed Abdelhadi , T. Charles Clancy

In this paper, we derive the information theoretic performance bounds on communication data rates and errors in parameter estimation, for a joint radar and communication (JRC) system. We assume that targets are semi-passive, i.e. they use…

Information Theory · Computer Science 2021-07-08 Ganesan Thiagarajan , Sanjeev Gurugopinath

The max-log-map (MLM) receiver is an approximated version of the well-known, Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. The MLM algorithm is attractive due to its implementation simplicity. In practice, sliding-window implementations are…

Information Theory · Computer Science 2013-04-12 Fabian Lim , Aleksandar Kavcic

In this paper, we investigate the spectral efficiency (SE) of massive multiple-input multiple-output (MIMO) systems with a large number of antennas at the base station (BS) accounting for physical space constraints. In contrast to the vast…

Information Theory · Computer Science 2016-11-17 Jiayi Zhang , Linglong Dai , Michail Matthaiou , Christos Masouros , Shi Jin

In conventional dual-function radar-communication (DFRC) systems, the radar and communication channels are routinely estimated at fixed time intervals based on their worst-case operation scenarios. Such situation-agnostic repeated…

Information Theory · Computer Science 2023-03-02 Jie Chen , Xiabin Wang , Ying-Chang Liang

Linear Least Squares is a very well known technique for parameter estimation, which is used even when sub-optimal, because of its very low computational requirements and the fact that exact knowledge of the noise statistics is not required.…

Statistics Theory · Mathematics 2018-10-16 Michael Krikheli , Amir Leshem

In many statistical and econometric applications, we gather individual samples from various interconnected populations that undeniably exhibit common latent structures. Utilizing a model that incorporates these latent structures for such…

Methodology · Statistics 2023-09-19 Archer Gong Zhang , Jiahua Chen

Line-of-sight (LoS) multiple-input multiple-output (MIMO) gives full spatial-multiplexing gain when the antenna array geometry and orientation are designed based on the inter-terminal distance. These known design methodologies, that hold…

Information Theory · Computer Science 2016-11-10 Lakshmi Natarajan , Yi Hong , Emanuele Viterbo

With some regularity conditions maximum likelihood estimators (MLEs) always produce asymptotically optimal (in the sense of consistency, efficiency, sufficiency, and unbiasedness) estimators. But in general, the MLEs lead to non-robust…

Methodology · Statistics 2024-02-22 Chudamani Poudyal

Sparse Bayesian Learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors. The hyperparameters in SBL models are crucial for the model's performance, but they are…

Machine Learning · Computer Science 2024-01-08 Feng Yu , Lixin Shen , Guohui Song