We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed, since the last received update was generated. In this paper, we model the source-destination link using the queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.

We consider a system in which random status updates arrive at a source node, and should be transmitted through a wireless network to the intended destination node. The status updates are samples of a random process, transmitted as packets, containing the time stamp to identify the moment the sample was generated. The time it takes to successfully transmit a packet to the destination is modeled as an exponentially distributed service time. The status update age at the receiver is the time elapsed since the last received update was generated. In this paper, we analyze the age in the case that the source node has the capability to manage the arriving samples and decide which packets will be transmitted to the destination. In addition to the average age, we investigate the average of a new metric, called peak age, which provides information about the maximum value of the Age, achieved immediately before receiving an update.

Freshness of status update packets is essential for enabling services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of single-server multi-source queueing models under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the case with exponentially distributed service time, i.e., for a multi-source M/M/1 queueing model. Moreover, we derive three approximate expressions for the average AoI for a multi-source M/G/1 queueing model having a general service time distribution. Simulation results are provided to validate the derived exact average AoI expression, to assess the tightness of the proposed approximations, and to demonstrate the AoI behavior for different system parameters.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria, including weighted sum rate maximization, weighted sum mean square error minimization, minimum signal-to-interference-plus-noise ratio (SINR) maximization and sum power minimization under a minimum SINR constraint. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically nontractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. Even though each subproblem is optimally solved, there is no guarantee that the global optimum has been found due to the nonconvexity of the problem. However, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient.

Cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base stations (BS) is considered for users located within a soft handover (SHO) region. The downlink resource allocation problem with different BS power constraints is studied for the orthogonal frequency division multiplexing system with adaptive MIMO transmission. Joint design of the linear transmit and receive beamformers in a MIMO multiuser transmission subject to per BS power constraints is considered. A solution for the weighted sum rate maximization problem is proposed. The proposed algorithm is shown to provide a very efficient solution despite of the fact that the global optimality cannot be guaranteed due to the non-convexity of the optimization problem. Moreover, efficient resource allocation method based on zero forcing transmission is provided. The impact of the size of a SHO region, the overhead from the increased resource utilization, and different inter-cell interference distributions due to the SHO are evaluated by system level simulations. Although the overhead from the SHO processing can be significant, it can be mitigated by using space division multiple access for users having an identical SHO active set composition. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing.

Information freshness is crucial for time-critical IoT applications, e.g., monitoring and control. We consider an IoT status update system with users, energy harvesting sensors, and a cache-enabled edge node. The users receive time-sensitive information about physical quantities, each measured by a sensor. Users demand for the information from the edge node whose cache stores the most recently received measurements from each sensor. To serve a request, the edge node either commands the sensor to send an update or retrieves the aged measurement from the cache. We aim at finding the best actions of the edge node to minimize the average AoI of the served measurements at the users, termed on-demand AoI. We model this problem as a Markov decision process and develop reinforcement learning (RL) algorithms: model-based value iteration and model-free Q-learning. We also propose a Q-learning method for the realistic case where the edge node is informed about the sensors’ battery levels only via the status updates. The case under transmission limitations is also addressed. Furthermore, properties of an optimal policy are characterized. Simulation results show that an optimal policy is a threshold-based policy and that the proposed RL methods significantly reduce the average cost compared to several baselines.