We propose a new class of error correction codes for low-delay streaming communication. We consider an online setup where a source packet arrives at the encoder every M channel uses, and needs to be decoded with a maximum delay of T packets. We consider a sliding-window erasure channel -C(N, B, W ) -which introduces either up to N erasures in arbitrary positions, or B erasures in a single burst, in any window of length W . When M = 1, the case where source-arrival and channel-transmission rates are equal, we propose a class of codes -MiDAS codes -that achieve a near optimal rate. Our construction is based on a layered approach. We first construct an optimal code for the C(N = 1, B, W ) channel, and then concatenate an additional layer of parity-check symbols to deal with N > 1. When M > 1, the case where source-arrival and channel-transmission rates are unequal, we characterize the capacity when N = 1 and W ≥ M (T + 1), and for N > 1, we propose a construction based on a layered approach. Numerical simulations over Gilbert-Elliott and Fritchman channel models indicate significant gains in the residual loss probability over baseline schemes. We also discuss the connection between the error correction properties of the MiDAS codes and their underlying column distance and column span.

Abstract-Cloud radio-access network (CRAN) is a wireless cellular network architecture in which the base-stations are connected to a cloud-computing based central processor with digital backhaul links. In this setting, the base-stations can be thought of as relays between the central processor and the user terminals. This paper considers the downlink of a cloud radioaccess network with finite-capacity backhaul links. We model the overall network as a relay broadcast channel, and propose a hybrid compression and message-sharing strategy for the downlink transmission. This paper further proposes numerical techniques for optimizing the quantization noise level in the compression part of the hybrid precoding scheme. We compare the system-level performance of the proposed scheme with the pure compression and the pure message sharing schemes to show the benefit of the hybrid strategy for the downlink CRAN.

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The steadily increasing amount of atmospheric carbon dioxide (CO2) is affecting the global climate system and threatening the long-term sustainability of Earth's ecosystem. In order to better understand the sources and sinks of CO2, NASA operates the Orbiting Carbon Observatory-2 & 3 satellites to monitor CO2 from space. These satellites make passive radiance measurements of the sunlight reflected off the Earth's surface in different spectral bands, which are then inverted to obtain estimates of the atmospheric CO2 concentration. In this work, we first analyze the current operational retrieval procedure, which uses prior knowledge in the form of probability distributions on the relevant atmospheric state variables to regularize the underlying ill-posed inverse problem, and demonstrate that the resulting uncertainties might be poorly calibrated both at individual locations and over a spatial region. To alleviate these issues, we propose a new method that uses known physical constraints on the state variables and direct inversion of the target functionals of the CO2 profile to construct well-calibrated frequentist confidence intervals based on convex programming. Furthermore, we study the influence of individual nuisance state variables on the length of the confidence intervals and identify certain key variables that can greatly reduce the final uncertainty given additional deterministic or probabilistic constraints, and develop a principled framework to incorporate such information into our method.

Uplink-downlink duality refers to the fact that the Gaussian broadcast channel has the same capacity region as the dual Gaussian multiple-access channel under the same sumpower constraint. This paper investigates a similar duality relationship between the uplink and downlink of a cloud radio access network (C-RAN), where a central processor (CP) cooperatively serves multiple mobile users through multiple remote radio heads (RRHs) connected to the CP with finite-capacity fronthaul links. The uplink of such a C-RAN model corresponds to a multipleaccess relay channel; the downlink corresponds to a broadcast relay channel. This paper considers compression-based relay strategies in both uplink and downlink C-RAN, where the quantization noise levels are functions of the fronthaul link capacities. If the fronthaul capacities are infinite, the conventional uplinkdownlink duality applies. The main result of this paper is that even when the fronthaul capacities are finite, duality continues to hold for the case where independent compression is applied across each RRH in the sense that when the transmission and compression designs are jointly optimized, the achievable rate regions of the uplink and downlink remain identical under the same sum-power and individual fronthaul capacity constraints. As an application of the duality result, the power minimization problem in downlink C-RAN can be efficiently solved based on its uplink counterpart.