Optimizing Average-Maximum TTR Trade-off for Cognitive Radio Rendezvous

In cognitive radio (CR) networks, "TTR", a.k.a. time-to-rendezvous, is one of the most important metrics for evaluating the performance of a channel hopping (CH) rendezvous protocol, and it characterizes the rendezvous delay when two CRs perform channel hopping. There exists a trade-off of optimizing the average or maximum TTR in the CH rendezvous protocol design. On one hand, the random CH protocol leads to the best "average" TTR without ensuring a finite "maximum" TTR (two CRs may never rendezvous in the worst case), or a high rendezvous diversity (multiple rendezvous channels). On the other hand, many sequence-based CH protocols ensure a finite maximum TTR (upper bound of TTR) and a high rendezvous diversity, while they inevitably yield a larger average TTR. In this paper, we strike a balance in the average-maximum TTR trade-off for CR rendezvous by leveraging the advantages of both random and sequence-based CH protocols. Inspired by the neighbor discovery problem, we establish a design framework of creating a wake-up schedule whereby every CR follows the sequence-based (or random) CH protocol in the awake (or asleep) mode. Analytical and simulation results show that the hybrid CH protocols under this framework are able to achieve a greatly improved average TTR as well as a low upper-bound of TTR, without sacrificing the rendezvous diversity.Comment: Accepted by IEEE International Conference on Communications (ICC 2015, http://icc2015.ieee-icc.org/

Critically evaluate how and why suppliers circumvent complaince codes. To what extend are social audit frameworks effective in managing sourcing risks.

Sustainability and outsourcing management are both complex areas, but the combination of both is where business, value creation and competitiveness meet reality. It is difficult to manage increasing globalised supply chains. The purpose of this management project is to critically examine the supply chain risk management system of many branded companies or retailers, particularly, social auditing (SA) or called social assessment, as to its effectiveness and appropriateness in managing sourcing risks. From the literature review and some consultations, the characteristic of SA is one of the best instruments to measure a company's integrity and genuine activities. However, it has drawbacks of auditor quality, risks of biased information and most important, the manipulative data. The project proceeds from a case study referring to understand why and how factory managers circumvent the codes, and how to improve the SA framework in order to enhance the sustainability of sourcing practices. The project found out four common manipulation techniques in exercise, namely bribery, falsified record, worker coaching and threatening and complicit coalition. Furthermore, the fundamental problem is the contradiction between the buyer principles and their ethical standards, which needs thorough scrutiny before implementing the social audit assessment

Mining frequent sequences using itemset-based extension

In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is composed oftwo separated steps. In the first step, all frequent itemsets are discovered and we can get great benefit from existing efficient itemset mining algorithms. In the second step, all frequent sequcnces with at least two frequent itemsets are detected by combining depth-first search and item set-based extension candidate generation together. A vertical bitmap data representation is adopted for rapidly support counting reason. Several pruning strategies are used to reduce the search space and minimize cost of computation. An extensive set ofexperiments demonstrate the effectiveness and the linear scalability of proposed algorithm

Annual Report of the Commission of the Department of Public Utilities for the Year Ending November 30, 1937

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an ``all-mmWave'' cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the ``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time-varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of-art baselines, in terms of average queue length and average packet dropping rate

From Authority-Respect to Grassroots-Dissent: Degree-Weighted Social Learning and Convergence Speed

Opinions are influenced by neighbors, with varying degrees of emphasis based on their connections. Some may value more connected neighbors' views due to authority respect, while others might lean towards grassroots perspectives. The emergence of ChatGPT could signify a new ``opinion leader'' whose views people put a lot of weight on. This study introduces a degree-weighted DeGroot learning model to examine the effects of such belief updates on learning outcomes, especially the speed of belief convergence. We find that greater respect for authority doesn't guarantee faster convergence. The influence of authority respect is non-monotonic. The convergence speed, influenced by increased authority-respect or grassroots dissent, hinges on the unity of elite and grassroots factions. This research sheds light on the growing skepticism towards public figures and the ensuing dissonance in public debate

Antiperovskite Li3OCl Superionic Conductor Films for Solid-State Li-Ion Batteries.

Antiperovskite Li3OCl superionic conductor films are prepared via pulsed laser deposition using a composite target. A significantly enhanced ionic conductivity of 2.0 × 10-4 S cm-1 at room temperature is achieved, and this value is more than two orders of magnitude higher than that of its bulk counterpart. The applicability of Li3OCl as a solid electrolyte for Li-ion batteries is demonstrated

Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications

Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potentials of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework