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Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud (MEC) can reduce the completion time of these tasks. However, using the MEC may come at a cost such as related to use of a cloud service or privacy. In this paper, we consider a source that generates time-stamped status updates for delivery to a monitor after processing by the mobile device or MEC. We study how a scheduler must forward these updates to achieve timely updates at the monitor but also limit MEC usage. We measure timeliness at the monitor using the age of information (AoI) metric. We formulate this problem as an infinite horizon Markov decision process (MDP) with an average cost criterion. We prove that an optimal scheduling policy has an age-threshold structure that depends on how long an update has been in service. 

A reconfigurable substrate integrated waveguide (SIW) filtenna operating in the 5G millimeter Wave (mmWave) band is presented, where varactors are integrated into the filtering-antenna structure to change the resonant frequency and coupling between the SIW resonators. The proposed structure allows for the reconfigurability of the antenna radiation frequency band in the range of 24-27 GHz, covering most of the 3GPP n258 band, with a constant bandwidth of 400 MHz and broadside radiation pattern. A prototype of the proposed mmWave filtenna is designed and fabricated, where the measurement results are in good agreement with the simulation. The proposed cost-effective and scalable filtenna is an ideal candidate for deployment in 5G wireless networks, with the ability to reduce adjacent channel interference (ACI) and enable passive spectrum coexistence with weather sensors in the 23.8 GHz band. 

The allocation of the 5G mmWave spectrum in the 26 GHz range, known as 3GPP band n258, has raised wide concern among the remote sensing and weather forecast communities due to the adjacency of this band with a frequency band used by passive sensors in Earth Exploration-Satellite Service (EESS). The concern stems from the potential radio frequency interference (RFI) caused by transmissions in the n258 band into the 23.8 GHz frequency, one of the key frequencies employed by weather satellite passive sensing instruments, such as AMSUA and ATMS, to measure atmospheric water vapor using its emission spectrum. Such RFI can bias satellite observations and compromise weather forecasting. In this paper, we develop a modeling and numerical framework to evaluate the potential effect of the 5G mmWave n258 band’s commercial deployment on numerical weather forecast accuracy. We first estimate and map the spatio-temporal distribution of 5G mmWave base stations at the county-level throughout the contiguous United States (US) using a model for technology adoption prediction. Then, the interference power received by the AMSU-A radiometer is estimated for a single base station based on models for signal transmission, out-of-band radiation, and radio propagation. Then, the aggregate interference power for each satellite observation footprint is calculated. Using the contaminated microwave observations, a series of simulations using a numerical weather prediction (NWP) model are conducted to study the impact of 5G-induced contamination on weather forecasting accuracy. For example, our results show that when the interference power at the radiometer from a single base station is at a level of −175 dBW for a network of base stations with spectral efficiency of 15 bit/s/Hz/BS, the aggregate interference power has limited impact in the year 2025 but can result in an induced noise in brightness temperature (contamination) of up to 17 K in the year 2040. Furthermore, that level of RFI can significantly impact the 12-hour forecast of a severe weather event such as the Super Tuesday Tornado Outbreak with forecasting errors of up to 10 mm in precipitation or a mean absolute error of 12.5%. It is also estimated that when the level of interference power received by the radiometer from a single base station is −200 dBW, then there is no impact on forecasting errors even in 2040. 

A source generates time-stamped update packets that are sent to a server and then forwarded to a monitor. This occurs in the presence of an adversary that can infer information about the source by observing the output process of the server. The server wishes to release updates in a timely way to the monitor but also wishes to minimize the information leaked to the adversary. We analyze the trade-off between the age of information (AoI) and the maximal leakage for systems in which the source generates updates as a Bernoulli process. For a time slotted system in which sending an update requires one slot, we consider three server policies: (1) Memoryless with Bernoulli Thinning (MBT): arriving updates are queued with some probability and head-of-line update is released after a geometric holding time; (2) Deterministic Accumulate-and-Dump (DAD): the most recently generated update (if any) is released after a fixed time; (3) Random Accumulate-and-Dump (RAD): the most recently generated update (if any) is released after a geometric waiting time. We show that for the same maximal leakage rate, the DAD policy achieves lower age compared to the other two policies but is restricted to discrete age-leakage operating points. 

The utilization of newer spectrum bands such as in 5G and 6G networks, has the potential to inadvertently cause interference to passive sensing applications operating in the adjacent portions of spectrum. One such application that has received a lot of attention has been passive weather sensing where leakage from 5G mmWave band transmissions in the 26 GHz spectrum could potentially impact the observations of passive sensors on weather prediction satellites. To mitigate problems such as the above, we present a design framework that can be employed in mmWave networks by using filtennas (or filtering antennas) at the transmitter along with integrated resource allocation to minimize leakage into adjacent channels. Specifically, we propose an Iterative Leakage Aware Water Filling solution to allocate power and bandwidth in a system employing filtennas that guarantees performance requirements while reducing the leakage. In addition, a key contribution of this work is the characterization of the leakage function based on the order of filtennas which is incorporated in our resource allocation framework.