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This work considers the problem of sending a 1-bit message over an acyclic network, where the “edge” connecting any two nodes is a memoryless binary-input/symmetric-output (BISO) channel. For any arbitrary acyclic network topology and constituent channel models, a min-cut-based converse of the learning rate, denoted by r^*, is derived. It is then shown that for any r < r^*, one can design a scheme with learning rate r. Capable of approaching the optimal r^*, the proposed scheme is thus the asymptotically fastest for sending one bit over any acyclic BISO-channel network. The construction is based on a new concept of Lossless Amplify-&-Forward, a sharp departure from existing multi-hop communication scheme designs. 

Streaming codes eliminate the queueing delay and are an appealing candidate for low latency communications. This work studies the tradeoff between error probability p_e and decoding deadline ∆ of infinite-memory random linear streaming codes (RLSCs) over i.i.d. symbol erasure channels (SECs). The contributions include (i) Proving pe(∆) ∼ ρ∆^{−1.5}e^{−η∆}. The asymptotic power term ∆^{−1.5} of RLSCs is a strict improvement over the ∆^{−0.5} term of random linear block codes; (ii) Deriving a pair of upper and lower bounds on the asymptotic constant ρ, which are tight (i.e., identical) for one specific class of SECs; (iii) For any c > 1 and any decoding deadline ∆, the c-optimal memory length α^*_c (∆) is defined as the minimal memory length α needed for the resulting pe to be within a factor of c of the best possible p^*_e under any α, an important piece of information for practical implementation. This work studies and derives new properties of α^*_c (∆) based on the newly developed asymptotics. 

The ever-increasing needs of supporting real-time applications have spurred new studies on minimizing Age-of-Information (AoI), a novel metric characterizing the data freshness of the system. This work studies the single-queue information update system and strengthens the seminal results of Sun et al. on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new fixed-point-based method is proposed with quadratic convergence rate, an order-of-magnitude improvement over the state-of-the-art; (ii) When the distributional knowledge is unavailable (which is the norm in practice), two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to upgrade certain components to handle additional practical challenges. Two such upgrades are proposed for the situations: (iii.1) The AoI penalty function is also unknown and must be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. The performance of our schemes is either provably optimal or within 3% of the omniscient optimal offline solutions in all simulation scenarios. 

One canonical example of Age-Of-Information (AoI) minimization is the update-through-queues models. Existing results fall into two categories: The open-loop setting for which the sender is oblivious of the actual packet departure time, versus the closed-loop setting for which the decision is based on instantaneous Acknowledgement (ACK). Neither setting perfectly reflects modern networked systems, which almost always rely on feedback that experiences some delay. Motivated by this observation, this work subjects the ACK traffic to an independent queue so that the closed-loop decision is made based on delayed feedback. Near-optimal schedulers have been devised, which smoothly transition from the instantaneous-ACK to the open loop schemes depending on how long the feedback delay is. The results thus quantify the benefits of delayed feedback for AoI minimization in the update-through-queues systems. 

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.