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  1. We present a novel Packet Type (PT)-based design framework for the finite-length analysis of Device-to-Device (D2D) coded caching. By the exploitation of the asymmetry in the coded delivery phase, two fundamental forms of subpacketization reduction gain for D2D coded caching, i.e., the subfile saving gain and the further splitting saving gain, are identified in the PT framework. The proposed framework features a streamlined design process which uses several key concepts including user grouping, subfile and packet types, multicast group types, transmitter selection, local/global further splitting factor, and PT design as an integer optimization. In particular, based on a predefined user grouping, the subfile and multicast group types can be determined and the cache placement of the users can be correspondingly determined. In this stage, subfiles of certain types can be potentially excluded without being used in the designed caching scheme, which we refer to as subfile saving gain. In the delivery phase, by a careful selection of the transmitters within each type of multicast groups, a smaller number of packets that each subfile needs to be further split into can be achieved, leading to the further splitting saving gain. The joint effect of these two gains results in an overall subpacketization reduction compared to the Ji-Caire-Molisch (JCM) scheme [1]. Using the PT framework, a new class of D2D caching schemes is constructed with order reduction on subpacketization but the same rate when compared to the JCM scheme. 
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  2. We consider the cache-aided multiuser private information retrieval (MuPIR) problem with a focus on the special case of two messages, two users and arbitrary number of databases where the users have distinct demands of the messages. We characterize the optimal memory-load trade-off for the considered MuPIR problem by proposing a novel achievable scheme and a tight converse. The proposed achievable scheme uses the idea of cache-aided interference alignment (CIA) developed in the literature by the same authors. The proposed converse uses a tree-like decoding structure to incorporate both the decodability and privacy requirements of the users. While the optimal characterization of the cache-aided MuPIR problem is challenging in general, this work provides insight into understanding the general structure of the cache-aided MuPIR problem. 
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  3. In this work, we propose a two-stage multi-agent deep deterministic policy gradient (TS-MADDPG) algorithm for communication-free, multi-agent reinforcement learning (MARL) under partial states and observations. In the first stage, we train prototype actor-critic networks using only partial states at actors. In the second stage, we incorporate partial observations resulting from prototype actions as side information at actors to enhance actor-critic training. This side information is useful to infer the unobserved states and hence, can help reduce the performance gap between a network with fully observable states and a partially observable one. Using a case study of building energy control in the power distribution network, we successfully demonstrate that the proposed TS-MADDPG can greatly improve the performance of single-stage MADDPG algorithms that use partial states only. This is the first work that utilizes partial local voltage measurements as observations to improve the MARL performance for a distributed power network. 
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  4. Our extensive real measurements over Amazon EC2 show that the virtual instances often have different computing speeds even if they share the same configurations. This motivates us to study heterogeneous Coded Storage Elastic Computing (CSEC) systems where machines, with different computing speeds, join and leave the network arbitrarily over different computing steps. In CSEC systems, a Maximum Distance Separable (MDS) code is used for coded storage such that the file placement does not have to be re-defined with each elastic event. Computation assignment algorithms are used to minimize the computation time given computation speeds of different machines. While previous studies of heterogeneous CSEC do not include stragglers - the slow machines during the computation, we develop a new framework in heterogeneous CSEC that introduces straggler tolerance. Based on this framework, we design a novel algorithm using our previously proposed approach for heterogeneous CSEC such that the system can handle any subset of stragglers of a specified size while minimizing the computation time. Furthermore, we establish a trade-off in computation time and straggler tolerance. Another major limitation of existing CSEC designs is the lack of practical evaluations using real applications. In this paper, we evaluate the performance of our designs on Amazon EC2 for applications of the power iteration and linear regression. Evaluation results show that the proposed heterogeneous CSEC algorithms outperform the state-of-the-art designs by more than 30%. 
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  5. null (Ed.)