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  1. Streaming codes take a string of source symbols as input and output a string of coded symbols in real time, which effectively eliminate the queueing delay and are regarded as a promising scheme for low latency communications. Aiming at quantifying the fundamental latency performance of random linear streaming codes (RLSCs) over i.i.d. symbol erasure channels, this work derives the exact error probability under, simultaneously, the finite memory length and finite decoding deadline constraints. The result is then used to examine the tradeoff among memory length (complexity), decoding deadline (delay), and error probability (reliability) of RLSCs for the first time inmore »the literature. Two critical observations are made: (i) Too much memory can adversely impact the performance under a finite decoding deadline constraint, a surprising finding not captured by the traditional wisdom that large memory length monotonically improves the performance in the asymptotic regime; (ii) The end-to-end delay of the RLSC is roughly 50% of that of the MDS block code when under identical code rate and error probability requirements. This implies that switching from block codes to RLSCs not only eliminates the queueing delay (thus 50%) but also has little negative impact on the error probability.« less
  2. Motivated by the applications for low-delay communication networks, the finite-length analysis, or channel dispersion identification, of the multi-user channel is very important. Recent studies also incorporate the effects of feedback in point-to-point and common-message broadcast channels (BCs). However, with private messages and feedback, finite-length results for BCs are much more scarce. Though it is known that feedback can strictly enlarge the capacity, the ultimate feedback capacity regions remain unknown for even some classical channels including Gaussian BCs. In this work, we study the two-user broadcast packet erasure channel (PEC) with causal feedback, which is one of the cleanest feedback capacitymore »results and the capacity region can be achieved by elegant linear network coding (LNC). We first derive a new finite-length outer bound for any LNCs and then accompanying inner bound by analyzing a three-phase LNC. For the outer-bound, we adopt a linear-space-based framework, which can successfully find the LNC capacity. However, naively applying this method in finite-length regime will result in a loose outer bound. Thus a new bounding technique based on carefully labelling each time slot according to the type of LNC transmitted is proposed. Simulation results show that the sum-rate gap between our inner and outer bounds is within 0.02 bits/channel use. Asymptotic analysis also shows that our bounds bracket the channel dispersion of LNC feedback capacity for broadcast PEC to within a factor of Q-l (E/2)/Q-l (E).« less
  3. One main objective of ultra-low-latency communications is to minimize the data staleness at the receivers, recently characterized by a metric called Age-of-Information (AoI). While the question of when to send the next update packet has been the central subject of AoI minimization, each update packet also incurs the cost of transmission that needs to be jointly considered in a practical design. With the exponential growth of interconnected devices and the increasing risk of excessive resource consumption in mind, this work derives an optimal joint cost-and-AoI minimization solution for multiple coexisting source-destination (S-D) pairs. The results admit a new AoI-market-price-based interpretationmore »and are applicable to the setting of (a) general heterogeneous AoI penalty functions and Markov delay distributions for each S-D pair, and (b) a general network cost function of aggregate throughput of all S-D pairs. Extensive simulation is used to demonstrate the superior performance of the proposed scheme.« less
  4. The ever-increasing needs of supporting real-time applications have spurred a considerable number of studies on minimizing Age-of-Information (AoI), a new metric characterizing the data freshness of the system. This work revisits and significantly strengthens the seminal results of Sun et al. on the following fronts: (i) The optimal waiting policy is generalized from the 1-way delay to the 2-way delay setting; (ii) A new way of computing the optimal policy with quadratic convergence rate, an order-of-magnitude improvement over the state-of-the-art bisection methods; and (iii) A new low-complexity adaptive online algorithm that provably converges to the optimal policy without knowing themore »exact delay distribution, a sharp departure from the existing AoI algorithms. Contribution (iii) is especially important in practice since the delay distribution can sometimes be hard to know in advance and may change over time. Simulation results in various settings are consistent with the theoretic findings.« less
  5. 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 bymore »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.« less
  6. The ubiquitous usage of communication networks in modern sensing and control applications has kindled new interests on the timing-based coordination between sensors and controllers, i.e., how to use the “waiting time” to improve the system performance. Contrary to the common belief that a zero-wait policy is always optimal, Sun et al. showed that a controller can strictly improve the data freshness, the so-called Age-of-Information (AoI), by postponing transmission in order to lengthen the duration of staying in a good state. The optimal waiting policy for the sensor side was later characterized in the context of remote estimation. Instead of focusingmore »on the sensor and controller sides separately, this work develops the optimal joint sensor/controller waiting policy in a Wiener-process system. The results can be viewed as strict generalization of the above two important results in the sense that not only do we consider joint sensor/controller designs (as opposed to sensor-only or controller only schemes), but we also assume random delay in both the forward and feedback directions (as opposed to random delay in only one direction). In addition to provable optimality, extensive simulation is used to verify the performance of the proposed scheme in various settings.« less