Fundamental Limits of Intelligent Reflecting Surface Aided Multiuser Broadcast Channel

Intelligent reflecting surface (IRS) has recently received significant attention in wireless networks owing to its ability to smartly control the wireless propagation through passive reflection. Although prior works have employed the IRS to enhance the system performance under various setups, the fundamental capacity limits of an IRS aided multi-antenna multi-user system have not yet been characterized. Motivated by this, we investigate an IRS aided multiple-input single-output (MISO) broadcast channel by considering the capacity-achieving dirty paper coding (DPC) scheme and dynamic beamforming configurations. We first propose a bisection based framework to characterize its capacity region by optimally solving the sum-rate maximization problem under a set of rate constraints, which is also applicable to characterize the achievable rate region with the zero-forcing (ZF) scheme. Interestingly, it is rigorously proved that dynamic beamforming is able to enlarge the achievable rate region of ZF if the IRS phase-shifts cannot achieve fully orthogonal channels, whereas the attained gains become marginal due to the reduction of the channel correlations induced by smartly adjusting the IRS phase-shifts. The result implies that employing the IRS is able to reduce the demand for implementing dynamic beamforming. Finally, we analytically prove that the sum-rate achieved by the IRS aided ZF is capable of approaching that of the IRS aided DPC with a sufficiently large IRS in practice. Simulation results shed light on the impact of the IRS on transceiver designs and validate our theoretical findings, which provide useful guidelines to practical systems by indicating that replacing sophisticated schemes with easy-implementation schemes would only result in slight performance loss

Saliency-Augmented Memory Completion for Continual Learning

Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitable given bounded memory and unbounded tasks, how to forget is a problem continual learning must address. Therefore, beyond simply avoiding catastrophic forgetting, an under-explored issue is how to reasonably forget while ensuring the merits of human memory, including 1. storage efficiency, 2. generalizability, and 3. some interpretability. To achieve these simultaneously, our paper proposes a new saliency-augmented memory completion framework for continual learning, inspired by recent discoveries in memory completion separation in cognitive neuroscience. Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding. When learning new tasks, previous data from memory are inpainted by an adaptive data generation module, which is inspired by how humans complete episodic memory. The module's parameters are shared across all tasks and it can be jointly trained with a continual learning classifier as bilevel optimization. Extensive experiments on several continual learning and image classification benchmarks demonstrate the proposed method's effectiveness and efficiency.Comment: Published at SIAM SDM 2023. 15 pages, 6 figures. Code: https://github.com/BaiTheBest/SAM

Cross-Community Knowledge Building with Idea Thread Mapper

Research on computer-supported collaborative learning faces the challenge of extending student collaboration to higher social levels and enabling cross-boundary interaction. This study investigated collaborative knowledge building among four Grade 5 classroom communities that studied human body systems with the support of Idea Thread Mapper (ITM). While students in each classroom collaborated in their local (home) discourse space to investigate various human body functions, they generated reflective syntheses— “super notes”—to share knowledge progress and challenges in a cross-community meta-space. As a cross-community collaboration, students from the four classrooms further used the Super Talk feature of ITM to investigate a common problem: how do people grow? Data sources included classroom observations and videos, online discourse within each community, students’ super notes and records of Super Talk discussion shared across the classrooms, and student interviews. The results showed that the fifth-graders were able to generate high quality super notes to reflect on their inquiry progress for cross-classroom sharing. Detailed analysis of the cross-classroom Super Talk documented students’ multifaceted understanding constructed to understand how people grow, which built on the diverse ideas from each classroom and further contributed to enriching student discourse within each individual classroom. The findings are discussed focusing on how to approach cross-community collaboration as an expansive and dynamic context for high-level inquiry and continual knowledge building with technology support

Network-Level Integrated Sensing and Communication: Interference Management and BS Coordination Using Stochastic Geometry

In this work, we study integrated sensing and communication (ISAC) networks with the aim of effectively balancing sensing and communication (S&C) performance at the network level. Focusing on monostatic sensing, the tool of stochastic geometry is exploited to capture the S&C performance, which facilitates us to illuminate key cooperative dependencies in the ISAC network and optimize key network-level parameters. Based on the derived tractable expression of area spectral efficiency (ASE), we formulate the optimization problem to maximize the network performance from the view point of two joint S&C metrics. Towards this end, we further jointly optimize the cooperative BS cluster sizes for S&C and the serving/probing numbers of users/targets to achieve a flexible tradeoff between S&C at the network level. It is verified that interference nulling can effectively improve the average data rate and radar information rate. Surprisingly, the optimal communication tradeoff for the case of the ASE maximization tends to employ all spacial resources towards multiplexing and diversity gain, without interference nulling. By contrast, for the sensing objectives, resource allocation tends to eliminate certain interference especially when the antenna resources are sufficient, because the inter-cell interference becomes a more dominant factor affecting sensing performance. Furthermore, we prove that the ratio of the optimal number of users and the number of transmit antennas is a constant value when the communication performance is optimal. Simulation results demonstrate that the proposed cooperative ISAC scheme achieves a substantial gain in S&C performance at the network level.Comment: 13 pages, 12 figures. This work has been submitted to the IEEE for possible publicatio

3D Multi-Target Localization Via Intelligent Reflecting Surface: Protocol and Analysis

With the emerging environment-aware applications, ubiquitous sensing is expected to play a key role in future networks. In this paper, we study a 3-dimensional (3D) multi-target localization system where multiple intelligent reflecting surfaces (IRSs) are applied to create virtual line-of-sight (LoS) links that bypass the base station (BS) and targets. To fully unveil the fundamental limit of IRS for sensing, we first study a single-target-single-IRS case and propose a novel \textit{two-stage localization protocol} by controlling the on/off state of IRS. To be specific, in the IRS-off stage, we derive the Cram\'{e}r-Rao bound (CRB) of the azimuth/elevation direction-of-arrival (DoA) of the BS-target link and design a DoA estimator based on the MUSIC algorithm. In the IRS-on stage, the CRB of the azimuth/elevation DoA of the IRS-target link is derived and a simple DoA estimator based on the on-grid IRS beam scanning method is proposed. Particularly, the impact of echo signals reflected by IRS from different paths on sensing performance is analyzed. Moreover, we prove that the single-beam of the IRS is not capable of sensing, but it can be achieved with \textit{multi-beam}. Based on the two obtained DoAs, the 3D single-target location is constructed. We then extend to the multi-target-multi-IRS case and propose an \textit{IRS-adaptive sensing protocol} by controlling the on/off state of multiple IRSs, and a multi-target localization algorithm is developed. Simulation results demonstrate the effectiveness of our scheme and show that sub-meter-level positioning accuracy can be achieved.Comment: This paper has been submitted to IEEE journal for possible publicatio

Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing

The Digital twin edge network (DITEN) aims to integrate mobile edge computing (MEC) and digital twin (DT) to provide real-time system configuration and flexible resource allocation for the sixth-generation network. This paper investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid computing system that can achieve mutual benefits for DT and MEC in the DITEN. For the first time, this paper presents the opportunity to realize the network-wide convergence of DT and MEC. In the considered system, specifically, over-the-air computation (AirComp) is employed to monitor the status of the DT system, while MEC is performed with the assistance of DT to provide low-latency computing services. Besides, the IRS is utilized to enhance signal transmission and mitigate interference among heterogeneous nodes. We propose a framework for designing the hybrid computing system, aiming to maximize the sum computation rate under communication and computation resources constraints. To tackle the non-convex optimization problem, alternative optimization and successive convex approximation techniques are leveraged to decouple variables and then transform the problem into a more tractable form. Simulation results verify the effectiveness of the proposed algorithm and demonstrate the IRS can significantly improve the system performance with appropriate phase shift configurations. Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT. This paper proposes the network-wide integration of DT and MEC, then demonstrates the necessity of DT for achieving an optimal performance in DITEN systems through analysis and numerical results

Infrastructuring for Knowledge Building: Advancing a framework for sustained innovation

Despite the wide implementations and extensive research base that has developed on knowledge building communities, continued efforts are required to address the challenges of implementing innovations in diverse contexts as well as sustaining them over time. In this paper, we draw on the idea of infrastructuring as an emergent, multilevel approach that can shed new light on ways to do this. After defining the notion of infrastructuring and showing its unique potential to sustain knowledge building, we examine three cases of infrastructuring within the context of efforts to grow knowledge building innovations in existing educational ecologies. This paper offers some new insights into how infrastructuring can be conceptualized to expand and sustain knowledge building innovations. © 2023 Progedit. All rights reserved

Polygamy and Purifying Selection in Birds

Good genes theories of sexual selection predict that polygamy will be associated with more efficient removal of deleterious alleles (purifying selection), due to the alignment of sexual selection with natural selection. On the other hand, runaway selection theories expect no such alignment of natural and sexual selection, and may instead predict less efficient purifying selection in polygamous species due to higher reproductive variance. In an analysis of polymorphism data extracted from 150-bird genome assemblies, we show that polygamous species carry significantly fewer nonsynonymous polymorphisms, relative to synonymous polymorphisms, than monogamous bird species (p = .0005). We also show that this effect is independent of effective population size, consistent with the alignment of natural selection with sexual selection and “good genes” theories of sexual selection. Further analyses found no impact of polygamy on genetic diversity, while polygamy in females (polyandry) had a marginally significant impact (p = .045). We also recapitulate previous findings that smaller body mass and greater geographic range size are associated with more efficient purifying selection, more intense GC-biased gene conversion, and greater genetic diversity

Polygamy and Purifying Selection in Birds

Good genes theories of sexual selection predict that polygamy will be associated with more efficient removal of deleterious alleles (purifying selection), due to the alignment of sexual selection with natural selection. On the other hand, runaway selection theories expect no such alignment of natural and sexual selection, and may instead predict less efficient purifying selection in polygamous species due to higher reproductive variance. In an analysis of polymorphism data extracted from 150-bird genome assemblies, we show that polygamous species carry significantly fewer nonsynonymous polymorphisms, relative to synonymous polymorphisms, than monogamous bird species (p = .0005). We also show that this effect is independent of effective population size, consistent with the alignment of natural selection with sexual selection and “good genes” theories of sexual selection. Further analyses found no impact of polygamy on genetic diversity, while polygamy in females (polyandry) had a marginally significant impact (p = .045). We also recapitulate previous findings that smaller body mass and greater geographic range size are associated with more efficient purifying selection, more intense GC-biased gene conversion, and greater genetic diversity.<br/