Related papers: Boosting Dynamic TDD in Small Cell Networks by the…
A large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where, in contrast with Time Division Duplexing (TDD), the channel reciprocity does not hold and explicit downlink (DL) probing and uplink…
Dynamic Time Division Duplexing (D-TDD) allows cells to accommodate asymmetric traffic variations with high resource assignment flexibility. However, this feature is limited by two additional types of interference between cells in opposite…
Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is…
Non-concave maximization has been the subject of much recent study in the optimization and machine learning communities, specifically in deep learning. Recent papers Ge et al, Lee et al (and references therein) indicate that first order…
Motivated by the promising benefits of dynamic Time Division Duplex (TDD), in this paper, we use a unified framework to investigate both the technical issues of applying dynamic TDD in homogeneous small cell networks (HomSCNs), and the…
Dynamic Time Division Duplex (TDD) has been introduced as a solution to deal with the uplink and downlink traffic asymmetry, mainly observed for dense heterogeneous network deployments. However, the use of this feature requires new…
Although in cellular networks full-duplex and dynamic time-division duplexing promise increased spectrum efficiency, their potential is so far challenged by increased interference. While previous studies have shown that self-interference…
Small cell enchantment is emerging as the key technique for wireless network evolution. One challenging problem for small cell enhancement is how to achieve high data rate with as-low-as-possible control and computation overheads. As a…
Even nowadays, where Deep Learning (DL) has achieved state-of-the-art performance in a wide range of research domains, accelerating training and building robust DL models remains a challenging task. To this end, generations of researchers…
We propose a randomized multiplicative weight update (MWU) algorithm for $\ell_{\infty}$ regression that runs in $\widetilde{O}\left(n^{2+1/22.5} \text{poly}(1/\epsilon)\right)$ time when $\omega = 2+o(1)$, improving upon the previous best…
In the dynamic linear program (LP) problem, we are given an LP undergoing updates and we need to maintain an approximately optimal solution. Recently, significant attention (e.g., [Gupta et al. STOC'17; Arar et al. ICALP'18, Wajc STOC'20])…
Network-assisted device-to-device communication is a promising technology for improving the performance of proximity-based services. This paper demonstrates how the integration of device-to-device communications and dynamic time-division…
In this paper, we introduce dynamic time-frequency-division duplex (D-TFDD), which is a novel duplexing scheme that combines time-division duplex (TDD) and frequency-division duplex (FDD). In D-TFDD, a user receives from the base station…
Device-to-device (D2D) underlaid cellular network, enabled with radio frequency energy harvesting (RFEH), and enhanced interference management schemes is a promising candidate to improve spectral and energy efficiency of next generation…
In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming. However, channel…
Traditionally, wireless cellular systems have been designed to operate in Frequency Division Duplexing (FDD) paired bands that allocates the same amount of spectrum for both downlink (DL) and uplink (UL) communication. Such design is very…
Positive linear programs (LPs) model many graph and operations research problems. One can solve for a $(1+\epsilon)$-approximation for positive LPs, for any selected $\epsilon$, in polylogarithmic depth and near-linear work via variations…
Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands. In this paper, we develop a novel distributed hierarchical deep reinforcement…
We study downlink (DL) channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in a time-division duplex. The users must know their effective channel gains to decode their received DL data signals.…
Full duplex (FD) communications, which increases spectral efficiency through simultaneous transmission and reception on the same frequency band, is a promising technology to meet the demand of next generation wireless networks. In this…