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Found 20 papers, 0 papers with code
Hierarchical MTC User Activity Detection and Channel Estimation with Unknown Spatial Covariance
To solve the JUICE problem, we first leverage the concept of strong priors and propose a hierarchical-sparsity-inducing spike-and-slab prior to model the structured sparse activity pattern.
Status Updating under Partial Battery Knowledge in Energy Harvesting IoT Networks
We study status updating under inexact knowledge about the battery levels of the energy harvesting sensors in an IoT network, where users make on-demand requests to a cache-enabled edge node to send updates about various random processes monitored by the sensors.
On the Age-Optimality of Relax-then-Truncate Approach under Partial Battery Knowledge in Energy Harvesting IoT Networks
We consider an energy harvesting (EH) IoT network, where users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an EH sensor.
Joint Estimation of Clustered User Activity and Correlated Channels with Unknown Covariance in mMTC
This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a \emph{clustered} user activity pattern.
Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous Networks
Cellular networks that are traditionally designed for human-type communication (HTC) have the potential to provide cost effective connectivity to machine-type communication (MTC).
Query-Age-Optimal Scheduling under Sampling and Transmission Constraints
This letter provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel.
Multi-Source AoI-Constrained Resource Minimization under HARQ: Heterogeneous Sampling Processes
Finally, we consider unknown environment and devise a learning-based transmission policy by relaxing the CMDP problem into an MDP problem using the DPP method and then adopting the deep Q-learning algorithm.
Status Updating with an Energy Harvesting Sensor under Partial Battery Knowledge
We consider status updating under inexact knowledge of the battery level of an energy harvesting (EH) sensor that sends status updates about a random process to users via a cache-enabled edge node.
Channel Charting Aided Pilot Reuse for Massive MIMO Systems with Spatially Correlated Channels
Massive multiple-input multiple-output (mMIMO) technology is a way to increase the spectral efficiency of machine-type communications (MTC).
Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-based Scheduling
The CMDP problem is solved by transforming it into an MDP problem using the Lagrangian relaxation method.
On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors
We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor.
User Activity Detection and Channel Estimation of Spatially Correlated Channels via AMP in Massive MTC
This paper addresses the problem of joint user identification and channel estimation (JUICE) for grant-free access in massive machine-type communications (mMTC).
Minimizing AoI in Resource-Constrained Multi-Source Relaying Systems with Stochastic Arrivals
We propose an algorithm that obtains a stationary deterministic near-optimal policy, establishing a benchmark for the system.
Exploiting Spatial Correlation for Pilot Reuse in Single-Cell mMTC
This work addresses the design of channel features from correlated fading channels to assist the pilot assignment in multi-sector mMTC systems under pilot reuse of orthogonal sequences.
Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC
First, for the case without prior information, we formulate the JUICE as an iterative reweighted $\ell_{2, 1}$-norm minimization problem.
Iterative Reweighted Algorithms for Joint User Identification and Channel Estimation in Spatially Correlated Massive MTC
Joint user identification and channel estimation (JUICE) is a main challenge in grant-free massive machine-type communications (mMTC).
General Total Variation Regularized Sparse Bayesian Learning for Robust Block-Sparse Signal Recovery
Block-sparse signal recovery without knowledge of block sizes and boundaries, such as those encountered in multi-antenna mmWave channel models, is a hard problem for compressed sensing (CS) algorithms.
AoI Minimization in Status Update Control with Energy Harvesting Sensors
Users send requests to the edge node where a cache contains the most recently received measurements from each sensor.
Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks
In such a quantized compressed sensing (QCS) context, we address remote acquisition of a sparse source through vector quantized noisy compressive measurements.
Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning
We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors.
