This paper investigates achievable information rates and error exponents of mismatched decoding when the channel belongs to the class of channels that are close to the decoding metric in terms of relative entropy. For both discrete- and continuous-alphabet channels, we derive approximations of the worst-case achievable information rates and error exponents as a function of the radius of a small relative entropy ball centered at the decoding metric, allowing the characterization of the loss incurred due to imperfect channel estimation. We provide a number of examples including symmetric metrics and modulo-additive noise metrics for discrete systems, and nearest neighbor decoding for continuous-alphabet channels, where we derive the approximation when the channel admits arbitrary statistics and when it is assumed noise-additive with unknown finite second-order moment.

Designing energy-efficient beamformers is a major challenge to the deployment of battery-powered devices transmitting short data packets at millimeter-wave frequencies. In general, directly maximizing energy efficiency will not meet the stringent reliability requirements of certain Internet-of-Things applications. We focus on designing analog beamformers in a device-to-device setting under short packets, a finite encoder set, and imperfect channel knowledge. To guarantee the required error decoding performance, a two-step procedure is proposed such that beamformers are designed to minimize the power consumption subject to reliability constraints under worst-case channel estimation errors, and then the channel encoder maximizing the energy efficiency is selected. The proposed design enjoys a moderate loss under practical low-resolution phase shifters, and exhibits robustness to inaccurate channel estimators, which is crucial to the tradeoff between energy efficiency and error decoding performance.

We study Gaussian i.i.d. codebooks and nearest neighbor decoding over continuous-alphabet channels. We define a class of compound channels that are within a small radius relative entropy ball centered at the nearest neighbor decoding metric. We derive approximations to the worst-case achievable rates and find the penalty terms proportional to the square root of the ball radius.

This paper investigates achievable error exponents of i.i.d. and constant-composition codes for a decoder whose decoding metric is close to the channel probability law in terms of relative entropy. We derive approximations of the worst-case achievable error exponents as functions of the radius of a small relative entropy ball centered at the decoding metric, and characterize the error terms of the underlying approximations.

An optimum energy allocation for massive user networks operating under iterative successive interference cancellation (I-SIC) is derived in this work. We analyze an uplink setting where a large network of cost-effective low-power users employ a practical transmission scheme consisting of the same channel encoder followed by spread spectrum modulation. Variational calculus (VC) is utilized to derive the asymptotic large-user energy distribution that favors I-SIC's performance, in terms of spectral efficiency (SE), when at most two decoding attempts per user are performed. The capacity region under random spreading and SIC operating in the infinite blocklength regime is evaluated and contrasted with the maximized SE using short packets.

A multiple access (MA) optimization technique for massive low-rate direct-sequence spread spectrum communications is analyzed in this work. A dense network of users transmitting at the same rate to a common central node under channel-aware energy allocation is evaluated. At reception, successive interference cancellation (SIC) aided by channel decoding is adopted. Our contribution focuses on wireless scenarios involving a vast number of users for which the provided user-asymptotic model holds. Variational calculus (VC) is employed to derive the energy allocation function that, via user-power imbalance, maximizes the network spectral efficiency (SE) when perfect channel state information at transmission (CSIT) is available and both average and maximum per-user energy constraints are set. Monte Carlo simulations at chip-level of a SIC receiver using a real decoder assess the proposed optimization method.

This paper studies the throughput maximization of a dense multiple access network of low-rate subscribers that share the same practical Forward Error Correction (FEC) code and modulation scheme, and transmit to a central node that implements a Successive Soft Interference Cancellation (soft SIC) strategy in order to mitigate Multiple Access Interference (MAI). In the user-asymptotic case, we make use of Variational Calculus (VC) tools to derive, in terms of the Packet Error Rate (PER) of the shared encoder and the Residual Energy (RE) from imperfect cancellation, the optimum energy profile that maximizes the network spectral efficiency, when a sum power constraint at the SIC input is enforced. Comparative performance analyses using a representative encoder are carried out. Simulation results show the benefit of the adopted soft SIC scheme in front of other SIC strategies, obtaining relevant throughput gains under high traffic loads.