Coexistence of Heterogeneous Services in the Uplink with Discrete Signaling and Treating Interference as Noise

The problem of enabling the coexistence of heterogeneous services, e.g., different ultra-reliable low-latency communications (URLLC) services and/or enhanced mobile broadband (eMBB) services, in the uplink is studied. Each service has its own error probability and blocklength constraints and the longer transmission block suffers from heterogeneous interference. Due to the latency concern, the decoding of URLLC messages cannot leverage successive interference cancellation (SIC) and should always be performed before the decoding of eMBB messages. This can significantly degrade the achievable rates of URLLC users when the interference from other users is strong. To overcome this issue, we propose a new transmission scheme based on discrete signaling and treating interference as noise decoding, i.e., without SIC. Guided by the deterministic model, we provide a systematic way to construct discrete signaling for handling heterogeneous interference effectively. We demonstrate theoretically and numerically that the proposed scheme can perform close to the benchmark scheme based on capacity-achieving Gaussian signaling with the assumption of perfect SIC.Comment: 7 pages, accepted for presentation at IEEE Global Communications Conference (GLOBECOM) 202

Downlink Transmission under Heterogeneous Blocklength Constraints: Discrete Signaling with Single-User Decoding

In this paper, we consider the downlink broadcast channel under heterogenous blocklength constraints, where each user experiences different interference statistics across its received symbols. Different from the homogeneous blocklength case, the strong users with short blocklength transmitted symbol blocks usually cannot wait to receive the entire transmission frame and perform successive interference cancellation (SIC) owing to their stringent latency requirements. Even if SIC is feasible, it may not be perfect under finite blocklength constraints. To cope with the heterogeneity in latency and reliability requirements, we propose a practical downlink transmission scheme with discrete signaling and single-user decoding, i.e., without SIC. In addition, we derive the finite blocklength achievable rate and use it for guiding the design of channel coding and modulations. Both achievable rate and error probability simulation show that the proposed scheme can operate close to the benchmark scheme which assumes capacity-achieving signaling and perfect SIC.Comment: 7 pages, 1 figure, accepted for presentation at IEEE ICC 2023. arXiv admin note: substantial text overlap with arXiv:2212.0173

Very-KIND, a KIND domain–containing RasGEF, controls dendrite growth by linking Ras small GTPases and MAP2

The regulation of cytoskeletal components in the dendritic shaft core is critical for dendrite elongation and branching. Here, we report that a brain-specific Ras guanine nucleotide exchange factor (RasGEF) carrying two kinase non-catalytic C-lobe domains (KINDs), very-KIND (v-KIND), regulates microtubule-associated protein 2 (MAP2). v-KIND is expressed in developing mouse brain, predominantly in the cerebellar granule cells. v-KIND not only activates Ras small GTPases via the C-terminal RasGEF domain, but also specifically binds to MAP2 via the second KIND domain (KIND2), leading to threonine phosphorylation of MAP2. v-KIND overexpression suppresses dendritic extension and branching of hippocampal neurons and cerebellar granule cells, whereas knockdown of endogenous v-KIND expression promotes dendrite growth. These findings suggest that v-KIND mediates a signaling pathway that links Ras and MAP2 to control dendrite growth

A Class of Semiparametric Models with Homogeneous Structure for Panel Data Analysis

Stimulated by the analysis of a dataset from China about Covid-19, we propose a class of semiparametric models for panel data analysis. The proposed models account for both homogeneity and heterogeneity among the individuals of a panel data. They strike a nice balance between parsimony and risk of misspecification. Although stimulated by the analysis of a particular dataset, the proposed models apply to very broad range of panel data analysis, they are powerful in exploring nonlinear dynamic patterns of impacts of covariates or transformed covariates. An estimation procedure is presented, and its asymptotic properties are established. Intensive simulation studies are also conducted to demonstrate how well the estimation procedure works and the risk of ignoring homogeneity or heterogeneity among individuals in panel data analysis. Finally, we apply the proposed models and estimation procedure to the Covid-19 data from China, and reveal some interesting dynamic patterns of the impacts of some important factors

A green intelligent routing algorithm supporting flexible QoS for many-to-many multicast

The tremendous energy consumption attributed to the Information and Communication Technology (ICT) field has become a persistent concern during the last few years, attracting significant academic and industrial efforts. Networks have begun to be improved towards being “green”. Considering Quality of Service (QoS) and power consumption for green Internet, a Green Intelligent flexible QoS many-to-many Multicast routing algorithm (GIQM) is presented in this paper. In the proposed algorithm, a Rendezvous Point Confirming Stage (RPCS) is first carried out to obtain a rendezvous point and the candidate Many-to-many Multicast Sharing Tree (M2ST); then an Optimal Solution Identifying Stage (OSIS) is performed to generate a modified M2ST rooted at the rendezvous point, and an optimal M2ST is obtained by comparing the original M2ST and the modified M2ST. The network topology of Cernet2, GéANT and Internet2 were considered for the simulation of GIQM. The results from a series of experiments demonstrate the good performance and outstanding power-saving potential of the proposed GIQM with QoS satisfied