The most commonly used setting in the coded caching literature consists of the following four elements: (i) homogeneous file sizes, (ii) homogeneous cache sizes, (iii) user-independent homogeneous file popularity (i.e., all users share the same file preference), and (iv) worst-case rate analysis. While recent results have relaxed some of these assumptions, deeper understanding of the full heterogeneity setting is still much needed since traditional caching schemes place little assumptions on file/cache sizes and almost always allow each user to have his/her own file preference through individualized file request prediction. Taking a microscopic approach, this paper characterizes the exact capacity of the smallest 2-user/2-file ($N=K=2$) problem but under the most general setting that simultaneously allows for (i) heterogeneous files sizes, (ii) heterogeneous cache sizes, (iii) user-dependent file popularity, and (iv) average-rate analysis. Solving completely the case of$N=K=2$could shed further insights on the performance and complexity of optimal coded caching with full heterogeneity for arbitrary$N$and$K$.

Coded caching is a technique for reducing congestion in communication networks by prefetching content during idle periods and exploiting multicasting opportunities during periods of heavy traffic. Most of the existing research in this area has focused on minimizing the worst case (i.e., peak) rate in a broadcast link with multiple identically distributed user requests. However, modern content delivery networks are investing very heavily in profiling their users and predicting their preferences. The minimal achievable rate of a coded caching scheme with heterogeneous user profiles is still unknown in general. This paper presents the first steps towards solving that problem by analyzing the case of two users with distinct but overlapping demand sets. Specifically, it provides a complete characterization of the uniform-average-rate capacity when the sets overlap in just one file and shows that such capacity can be achieved with selfish and uncoded prefetching. Then, it characterizes the same capacity under selfish and uncoded prefetching when the demand sets overlap in two or more files. The paper also provides explicit prefetching schemes that achieve those capacities. All our results allow for arbitrary (and not necessarily identical) users' cache sizes and number of files in each demand set.

The capacity region of 1-to-K broadcast packet erasure channels with ACK/NACK is well known for some scenarios, e.g., K ≤ 3, etc. However, existing achievability schemes either require knowing the target rate R→in advance, and/or have a complicated description of the achievable rate region that is difficult to prove whether it matches the capacity or not. This work proposes a new network coding scheme with the following features: (i) its achievable rate region is identical to the capacity region for all the scenarios in which the capacity is known; (ii) its achievable rate region is much more tractable and has been used to derive new capacity rate vectors; (iii) it employs sequential encoding that naturally handles dynamic packet arrivals; (iv) it automatically adapts to unknown packet arrival rates R→; (v) it is based on GF(q) with q ≥ K. In addition to analytically characterizing the achievable rate region of the proposed scheme, numerical simulation has been used to verify its queue length and delay performance.

This work falls under the broad setting of coded caching with user-dependent file popularity and average-rate capacity analysis. In general, the exact capacity characterization with user-dependent file popularity remains an open problem. For example, user 1 may be interested in files 1 and 2 with probabilities 0.6 and 0.4, respectively, while user 2 may be interested in only files 2, and 3 with probabilities 1/3 and 2/3, respectively, but not interested in file 1 at all. An optimal scheme needs to carefully balance the conflicting interests under the given probabilistic weights. Motivated by this fundamental but intrinsically difficult problem, this work studies the following simplified setting: Each user k is associated with a file demand set (FDS) Θk; each file in Θkis equally desired by user k with probability 1/|Θk| and files outside Θkis not desired at all. Different users may have different Θk1≠ Θk2, which reflects the user-dependent file popularity. Various capacity results have been derived (mostly for the cases of K = 2 users). One surprising byproduct is a proof showing that selfish coded caching is insufficient to achieve the capacity. That is, in an optimal coded caching scheme, a user sometimes has to cache the files of which he/she has zero interests.

The most commonly used setting in the coded caching literature consists of the following five elements: (i) homogeneous file sizes, (ii) homogeneous cache sizes, (iii) user-independent homogeneous file popularity (i.e., all users share the same file preference), and (iv) worst-case rate analysis. While recent results have relaxed some of these assumptions, deeper understanding of the full heterogeneity setting is still much needed since traditional caching schemes place little assumptions on file/cache sizes and almost always allow each user to have his/her own file preference through individualized file request prediction. Taking a microscopic approach, this paper characterizes the exact capacity of the smallest 2-user/2-file (N = K = 2) problem but under the most general setting that simultaneously allows for (i) heterogeneous files sizes, (ii) heterogeneous cache size, (iii) user-dependent file popularity, and (iv) average-rate analysis. Solving completely the case of N = K = 2, the results would shed further insights on the performance and complexity of optimal coded caching with full heterogeneity for arbitrary N and K.

The capacity region of 1-to-K broadcast packet erasure channels with ACK/NACK is known for some scenarios, e.g., K ≤ 3, etc. However, existing achievability schemes either require knowing the target rate R in advance, and/or have a complicated description of the achievable rate region that is difficult prove whether it matches the capacity or not. This work proposes a new network coding protocol with the following unique set of features: (i) Its achievable rate region is identical to the capacity region for all the scenarios in which the capacity is known; (ii) Its achievable rate region is much more tractable that existing works and has been used to derive new capacity rate vectors; (iii) It employs sequential encoding that naturally handles dynamic packet arrivals; (iv) It automatically adapts to unknown packet arrival rates R⃗; (v) It is based on GF(q) with q > K. Numerically, for K = 4, it admits an average control overhead 2-4% (assuming each packet has 1000 bytes), average encoding memory usage 48.5 packets, and average per-packet delay 94.8 time slots, when operating at 95% of the capacity.

In machine-to-machine (M2M) communication, a massive number of machine devices may transmit simultaneously in response to an event occurring in the system. Supporting massive device transmission while maintaining low congestion and low access delay is a challenging problem. This paper proposes a new transmission control scheme based on slotted ALOHA, with a practical consideration of partial information available at the data aggregator about the system. The proposed approximate maximum likelihood estimation ALOHA (AMLE-ALOHA) scheme incorporates an approximate ML estimation of the (unknown) number of active machines in the system. We apply the drift analysis to show the stability of the proposed control scheme. Simulation results demonstrate that the proposed AMLE- ALOHA outperforms an existing scheme in terms of the access delay and reaction time under bursty traffic with the same partial information, and compares favorably to the optimal control scheme with oracle knowledge of the number of active machines in the system.

This paper studies the information-theoretic secrecy rates of wireless two-way relay systems where two users wish to exchange information through a single relay with an eavesdropper observing all communications. We formulate and compare the achievable secrecy rates of the system that employs one of the three common relay protocols: conventional decode-and-forward (DF), DF with network coding (NC), and compute-and-forward (CF) based on physical-layer network coding (PNC). We show that CF based on PNC achieves the highest secrecy rate at high signal-to- noise ratio (SNR), while, interestingly, the other two protocols have mixed performance depending on the power allocation scheme and network topology. Our study offers insights into designing wireless two-way relay protocols from a secrecy perspective.

Two types of hyperspectral imager designed for underwater monitor was presented in this article. Active hyperspectral imager with light source compensated the lack of near infared ray in the sea water. The pushbroom hyperspectral imager was built in V-FIN (Vehicle for Instrumentation) and push broom scanning by a boat, which was good for studying some area monitored with full spectral image. The spectral range of both hyperspectral imagers is between 400 and 900nm. The living health of coral reefs and sea grass were the task for ocean ecology observation. These preliminary results was to verify the possibility of pushbroom hyperspectral image grabbing under water and the health monitor of coral reef and sea grass by specta reflectance given from both hyperspectral imager.

Many applications involving machine-to-machine (M2M) communications are characterized by the large amount of data to transport. To support these M2M applications, we argue in this article that instead of focusing on serving individual machines with better quality, one should focus on solutions that can better serve the data itself. To substantiate, we consider the application of data gathering from a set of machines that communicate directly to an aggregator. Since the aggregator has limited radio resources, the problem arises as to how the resource can be effectively utilized for supporting such an application. We investigate “data-centric” resource allocation that aims to maximize information entropy of data collected through selecting the subset of machines to transmit, determining the amount of resources to allocate, and scheduling the sequence of transmissions. We present two instantiations of the problems when machines can perform distributed source coding or dependent source coding based on the data overheard from neighboring machines and then propose algorithms for solving the joint optimization problems. Evaluation results show that compared to conventional “machine-centric” resource allocation that aims to maximize the aggregate data rates or number of supported machines, “data-centric” resource allocation exhibits significant performance gain in terms of the quality of data that can be collected for the given amount of radio resources.

We consider the correlated data gathering problem in machine-to-machine communications. The machines implement distributed source coding and transmit their gathered data to the data aggregator. The data aggregator has limited radio resources and thus only a subset of machines are selected for transmission. Missing data from nonselected machines are reconstructed at the aggregator by exploiting data correlation. We first propose a data distortion measure based on information loss to characterize the reconstruction, and derive its relationship with the traditional mean squared error distortion analytically. Then, we formulate the machine selection problem with the objective of minimizing the overall data distortion given some resource constraints. We decouple the problem into subproblems and solve them by the proposed algorithm based on the cross entropy method. Numerical results demonstrate improved data fidelity by implementing distributed source coding, and better network coverage and energy efficiency for the proposed machine selection scheme.

Many applications involving machine-to-machine (M2M) communications are characterized by the large amount of data to transport. To address the “big data” problem introduced by these M2M applications, we argue in this paper that instead of focusing on serving individual machines with better quality, one should focus on solutions that can better serve the data itself. To substantiate this concept, we consider the scenario of data gathering in a wide area by machines that are connected to a central aggregator through direct wireless links. The aggregator has limited radio resources to allocate to machines for uplink transmission of collected data, and hence the problem arises as to how the resources can be effectively utilized for supporting such an M2M application. In contrast to conventional approaches on maximizing the number of machines that can access the radio resources, we investigate an approach that takes into consideration “useful” information content that individual machines can provide for prioritization of resource allocation. Numerical results based on the proposed algorithms show that although the number of machines that can be supported is not maximized, the data so collected at the aggregator does exhibit significant quality gain for the target M2M scenario, thus motivating further investigation along this direction.

Recently, there has been an increasing interest in the deployment and management of femto base stations (BSs) to optimize the overall system performance in macro-femto heterogeneous networks. While deployment of femto BSs is typically not as planned as that of pico BSs, given a number of femto BSs to be distributed to candidate customer sites, questions regarding the optimal deployment locations and transmission configurations still need to be answered. In this paper, we formulate a joint optimization problem involving deployment location, cell selection, and power control to maximize the number of users that can be supported for a given number of femto BSs to be deployed in the macro cell. Since the formulated problem belongs to mixed-integer non-linear programming (MINLP), we propose an anytime algorithm that can yield a desirable solution within proper time limit. Specifically, based on the concept of coalition structure generation, the algorithm decouples the problem into the cluster formation sub-problem and power control sub-problem to find the optimal cluster head (femto BS location), cluster membership (cell selection), and transmission power in an iterative fashion. Evaluation results presented in this paper show that the proposed algorithm can effectively solve the problem with better complexity-optimality tradeoffs compared to baseline approaches.