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Cell-free massive MIMO (CFmMIMO) is a promising paradigm to provide uniform coverage in future wireless networks. However, a fully connected CFmMIMO system where all the access points (APs) serve every user equipment (UE) makes it challenging to deploy and scale in real-time due to high computational complexity and increased signaling overhead. In this work, we study the problem of downlink power allocation in partially connected CFmMIMO (p-CFmMIMO) systems using maximal ratio transmission (MRT). We utilize the underlying geometry of the problem to propose a graph representation of the CFmMIMO system and develop a graph neural network (GNN) based power allocation strategy to maximize the minimum SINR in the system. We demonstrate that the proposed GNN model has excellent generalizability to deployment size, radio propagation morphologies, and per-AP serving density. Our GNN can address the power allocation problem in the fully connected case, the partially connected case, and even the cellular case with magnitudes lower computational complexity compared to the conventional numerical solvers. Notably, we show that over a wide range of service scenarios, the model achieves a median spectral efficiency that is within 10% of the optimal second-order cone programming (SOCP) solution while requiring 100 times fewer FLOPS.

Time-to-event analysis is a branch of statistics that has increased in popularity during the last decades due to its many application fields, such as predictive maintenance, customer churn prediction and population lifetime estimation. Our main contribution is to offer a deep understanding of the interactions between model assumptions and dataset characteristics, along with the effectiveness of different scoring metrics in assessing predictive accuracy and robustness. To this end, we review and compare the performance of several prediction models for time-to-event analysis. These consist of semi-parametric and parametric statistical models, in addition to machine learning approaches. Our study is carried out on four datasets and evaluated in two different scores (the integrated Brier score and concordance index). Moreover, we show how aggregation methods can improve the prediction accuracy and enhance the robustness of the prediction performance. We conclude the analysis with a simulation experiment in which we evaluate the factors influencing the performance ranking of the methods using both scores.

This paper considers a system in which multiple users send periodically generated status information to a common access point (AP) over a collision channel. To avoid high overhead, there is no time synchronization and no feedback information from the AP to indicate whether a transmission is successful or not. The performance metric that we focus on is the age-of-information (AoI), which represents the freshness of the status information received at the AP. For this model, we propose a sequence based MAC scheme in which each user is pre-assigned a periodic sequence to schedule transmissions. This scheme guarantees each user at least one successful packet transmission within a sequence period, in the absence of time synchronization and feedback information from the AP. To the best of our knowledge, this is the first study investigating AoI performance under a sequence based MAC scheme. We derive the closed-form expressions for average AoI, average peak AoI and average age penalty under the sequence based scheduling. Besides, we derive several critical properties of the sequences to optimize the AoI performance. Comparison results show that our proposed sequence scheme outperforms slotted ALOHA and framed ALOHA in various settings.

Modern information and communication systems have become increasingly challenging to manage. The ubiquitous system logs contain plentiful information and are thus widely exploited as an alternative source for system management. As log files usually encompass large amounts of raw data, manually analyzing them is laborious and error-prone. Consequently, many research endeavors have been devoted to automatic log analysis. However, these works typically expect structured input and struggle with the heterogeneous nature of raw system logs. Log parsing closes this gap by converting the unstructured system logs to structured records. Many parsers were proposed during the last decades to accommodate various log analysis applications. However, due to the ample solution space and lack of systematic evaluation, it is not easy for practitioners to find ready-made solutions that fit their needs. This paper aims to provide a comprehensive survey on log parsing. We begin with an exhaustive taxonomy of existing log parsers. Then we empirically analyze the critical performance and operational features for 17 open-source solutions both quantitatively and qualitatively, and whenever applicable discuss the merits of alternative approaches. We also elaborate on future challenges and discuss the relevant research directions. We envision this survey as a helpful resource for system administrators and domain experts to choose the most desirable open-source solution or implement new ones based on application-specific requirements.

In some visible light communication (VLC) systems, transmitters are light sources which support not only illumination but also information broadcast and positioning. Since transmit signals in these systems inevitably interfere with each other at the receiver side, extra devices may be added to synchronize the transmitters, but this impairs an appealing advantage in VLC systems that existing lighting infrastructure can be simply reused. This paper proposes a novel multiple access scheme to realize decentralized asynchronous VLC systems such that both the transmitters and receivers are asynchronous. For a system with $N$ transmitters, two unique codewords of $L$ chips are allocated to each transmitter where $L$ is a prime number larger than $\text{4}\,N$. Those codewords are designed to help maintain a constant transmitted average power and enhance channel estimation, as well as conveying data. Our scheme enables the receiver to obtain one channel estimate and one data symbol per $L$ chips. Simulation results show that the proposed scheme significantly outperforms other schemes in the literature in terms of bit error rate and system throughput.

Maximum completion time is a key performance metric for future wireless applications that have stringent latency requirements. This letter studies its optimization in uplink multi-subcarrier non-orthogonal multiple access (NOMA) networks. The joint optimization problem of power allocation, user pairing and scheduling is analyzed and proved to be NP-hard. To tackle the problem, the power allocation subproblem for a single NOMA pair is first derived. Next, for the joint user pairing and scheduling subproblem, an optimal algorithm for a special case is constructed and a suboptimal algorithm for the general case is designed. Our proposed scheme is applicable to data aggregation in wireless federated learning networks, and simulation results validate its superior performance.

This paper studies the allocation of shared resources between ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) in the emerging 5G and beyond cellular networks. In this paper, we design a unique queuing mechanism for the joint eMBB/URLLC system. The aim is to flexibly schedule URLLC traffic to enhance the total eMBB throughput and the reliability of URLLC packets (i.e., the probability of not dropping URLLC packets in each mini-slot) while maintaining a satisfactory transmission latency as per the 3GPP requirements. Precisely, by deriving the steady-state probabilities of URLLC queue backlog analytically, we formulate a stochastic optimization problem to maximize the total normalized eMBB throughput and the URLLC utility. Due to the stochastic nature of the objective function, it is expensive to evaluate it for any set of inputs, and thus the Bayesian optimization is applied to obtain the optimal results of such a black-box objective function. Numerical results demonstrate that the proposed queuing mechanism never violates the latency requirement of the URLLC services but improves the reliability. It also enhances the total normalized eMBB throughput as compared to the method without queuing.

Heterogeneous cellular networks are composed of a mixture of macro and small (pico) cells embedded in the macro network. We assume uniform traffic for simplicity, and study the scaling of area spectral efficiencies (ASEs) as pico densities are increased, optimizing over transmit power and cell association bias. We found that median ASE grows linearly with the number of picos, with a slope as low as one, i.e., only one picocell per macro sector is necessary to double median ASE. The pico must be placed intelligently rather than randomly, and needs only transmit at -20 dB power equivalent isotropic radiated power (EIRP) relative to the macro. The effectiveness of the pico at such low power is due in part to the improvement in area coverage of an omnidirectional versus a three-sectored antenna. As pico density increases five percentile, signal-to-noise and interference ratio (SINR) decreases, causing poor scaling of edge rates. However, cell association bias was found to be an effective means of restoring the otherwise poor scaling of fifth percentile rates.