Radiation-Tolerant All-Digital PLL/CDR with Varactorless LC DCO in 65 nm CMOS

This paper presents the first fully integrated radiation-tolerant All-Digital Phase-Locked Loop (PLL) and Clock and Data Recovery (CDR) circuit for wireline communication applications. Several radiation hardening techniques are proposed to achieve state-of-the-art immunity to Single-Event Effects (SEEs) up to 62.52/mg as well as tolerance to the Total Ionizing Dose (TID) exceeding 1.5Grad. The LC Digitally Controlled Oscillator (DCO) is implemented without MOS varactors, avoiding the use of a highly SEE sensitive circuit element. The circuit is designed to operate at reference clock frequencies from 40–320 or at data rates from 40Mbps–320Mbps and displays a jitter performance of 520 with a power dissipation of only 11 and an FOM of −235 .

Analysis of Signal Processing for Gigabit Rate Wireline Communication

In this paper we mainly focus on achieving higher speed data which is about 1Gb/s over short copper loops using G.Fast technology. The use of TDD and higher bandwidth are two key features in G.Fast. Short twisted pair cable models are used to operate at relatively wider bandwidth. At higher frequencies, the problem of FEXT crosstalk becomes dominant. So to overcome these issues we are using different crosstalk cancellation techniques using various cables which provide different data rates at different frequencies and distances. Performance analysis of various crosstalk cancelation techniques has been done in this paper.

Learning to Harness Bandwidth with Multipath Congestion Control and Scheduling

Multipath TCP (MPTCP) has emerged as a facilitator for harnessing and pooling available bandwidth in wireless/wireline communication networks and in data centers. Existing implementations of MPTCP such as, Linked Increase Algorithm (LIA), Opportunistic LIA (OLIA) and BAlanced LInked Adaptation (BALIA) include separate algorithms for congestion control and packet scheduling, with pre-selected control parameters. We propose a Deep Q-Learning (DQL) based framework for joint congestion control and packet scheduling for MPTCP. At the heart of the solution is an intelligent agent for interface, learning and actuation, which learns from experience optimal congestion control and scheduling mechanism using DQL techniques with policy gradients. We provide a rigorous stability analysis of system dynamics which provides important practical design insights. In addition, the proposed DQL-MPTCPalgorithm utilizes the ‘recurrent neural network’ and integrates it with ‘long short-term memory’ for continuously i) learning dynamic behavior of subflows (paths) and ii) responding promptly to their behavior using prioritized experience replay. With extensive emulations, we show that the proposed DQL-based MPTCP algorithm outperforms MPTCP LIA, OLIA and BALIA algorithms. Moreover, the DQL-MPTCP algorithm is robust to time-varying network characteristics and provides dynamic exploration and exploitation of paths. The revised version is to appear in IEEE Trans. in Mobile Computing soon.<br>

Learning to Harness Bandwidth with Multipath Congestion Control and Scheduling

Multipath TCP (MPTCP) has emerged as a facilitator for harnessing and pooling available bandwidth in wireless/wireline communication networks and in data centers. Existing implementations of MPTCP such as, Linked Increase Algorithm (LIA), Opportunistic LIA (OLIA) and BAlanced LInked Adaptation (BALIA) include separate algorithms for congestion control and packet scheduling, with pre-selected control parameters. We propose a Deep Q-Learning (DQL) based framework for joint congestion control and packet scheduling for MPTCP. At the heart of the solution is an intelligent agent for interface, learning and actuation, which learns from experience optimal congestion control and scheduling mechanism using DQL techniques with policy gradients. We provide a rigorous stability analysis of system dynamics which provides important practical design insights. In addition, the proposed DQL-MPTCPalgorithm utilizes the ‘recurrent neural network’ and integrates it with ‘long short-term memory’ for continuously i) learning dynamic behavior of subflows (paths) and ii) responding promptly to their behavior using prioritized experience replay. With extensive emulations, we show that the proposed DQL-based MPTCP algorithm outperforms MPTCP LIA, OLIA and BALIA algorithms. Moreover, the DQL-MPTCP algorithm is robust to time-varying network characteristics and provides dynamic exploration and exploitation of paths. The revised version is to appear in IEEE Trans. in Mobile Computing soon.<br>