In applications of remote sensing, estimation, and control, timely communication is critical but not always ensured by high-rate communication. This work proposes decentralized age-efficient transmission policies for random access channels with $M$ transmitters. We propose the notion of age-gain of a packet to quantify how much the packet will reduce the instantaneous age of information at the receiver side upon successful delivery. We then utilize this notion to propose a transmission policy in which transmitters act in a decentralized manner based on the age-gain of their available packets. In particular, each transmitter sends its latest packet only if its corresponding age-gain is beyond a certain threshold which could be computed adaptively using the collision feedback or found as a fixed value analytically in advance. Both methods improve age of information significantly compared to the state of the art. In the limit of large $M$ , we prove that when the arrival rate is small (below $\frac {1}{eM}$ ), slotted ALOHA-type algorithms are order optimal. As the arrival rate increases beyond $\frac {1}{eM}$ , while age increases under slotted ALOHA, it decreases significantly under the proposed age-based policies. For arrival rates $\theta $ , $\theta =\frac {1}{o(M)}$ , the proposed algorithms provide a multiplicative gain of at least two compared to the minimum age under slotted ALOHA (minimum over all arrival rates). We conclude that it is beneficial to increase the sampling rate (and hence the arrival rate) and transmit packets selectively based on their age-gain. This is surprising and contrary to common practice where the arrival rate is optimized to attain the minimum AoI. We further extend our results to other random access technologies such as Carrier-sense multiple access (CSMA).

Age of Information (AoI) is studied in two-user broad-cast networks with feedback, and lower and upper bounds are derived on the expected weighted sum AoI of the users. In particular, a class of simple coding actions is considered and within this class, randomized and deterministic policies are devised. Explicit conditions are found for symmetric dependent channels under which coded randomized policies strictly outperform the corresponding uncoded policies. Similar behaviour is shown numerically for deterministic policies.

Coding for random access communication is a key challenge in Internet of Things applications. In this paper, the well-known scheme of Coded Slotted Aloha (CSA) is considered and its performance is analyzed in the non-asymptotic regime where the frame length and the number of users are finite. A density evolution framework is provided to describe the dynamics of decoding, and fundamental limits are found on the maximum channel load (i.e., the number of active users per time slot) that allows reliable communication (successful decoding). Finally, scaling laws are established, describing the non-asymptotic relation between the probability of error, the number of users, and the channel load.

This paper investigates the incentives of mobile network operators (MNOs) for acquiring additional spectrum to offer mobile virtual network operators (MVNOs) and thereby inviting competition for a common pool of end users (EUs). We consider interactions between two service providers, a MNO and an MVNO, when the EUs 1) must choose one of them 2) have the option to defect to an outside option should the SP duo offer unsatisfactory access fees or qualities of service. We formulate a multi-stage hybrid of cooperative bargaining and non-cooperative games in which the two SPs jointly determine their spectrum acquisitions, allocations and mutual money flows through the bargaining game, and subsequently individually determine the access fees for the EUs through the non-cooperative game. We identify when the overall equilibrium solutions exist, when it is unique and characterize the equilibrium solutions when they exist. The characterizations are easy to compute, and are in closed form or involve optimizations in only one decision variable. The hybrid framework allows us to determine whether and by how much the different entities benefit due to the cooperation in spectrum acquisition decision.

This paper investigates the incentives of mobile network operators (MNOs) for acquiring additional spectrum to offer mobile virtual network operators (MVNOs) and thereby inviting competition for a common pool of end users (EUs). We consider a base case and two generalizations: (\romannumeral1) one MNO and one MVNO, (\romannumeral2) one MNO, one MVNO and an outside option, and (\romannumeral3) two MNOs and one MVNO. In each of these cases, we model the interactions of the service providers (SPs) using a sequential game, identify when the Subgame Perfect Nash Equilibrium (SPNE) exists, when it is unique and characterize the SPNE when it exists. The characterizations are easy to compute, and are in closed form or involve optimizations in only one decision variable. We identify metrics to quantify the interplay between cooperation and competition, and evaluate those as also the SPNEs to show that cooperation between MNO and MVNO can enhance the payoffs of both, while increased competition due to the presence of additional MNOs is beneficial to EUs but reduces the payoffs of the SPs.

In communication and computation cooperative networks (3CNs), timely computation is crucial but not always guaranteed. There is a strong demand for a computational task to be completed within a given deadline. The time taken involves processing time, transmission time, and the impact of the deadline. However, a measure of such timeliness in 3CNs is lacking. To address this gap, we propose the novel concept of Age of Computing (AoC) to quantify computation freshness in 3CNs. Built on task timestamps, AoC serves as a practical metric for dynamic and complex real-world 3CNs. We evaluate AoC under two types of deadlines: (i) soft deadline, tasks can be fed back to the source if delayed beyond the deadline, but with additional latency; (ii) hard deadline, tasks delayed beyond the deadline are discarded. We investigate AoC in two distinct networks. In point-to-point, time-continuous networks, tasks are processed sequentially using a first-come, first-served discipline. We derive a general expression for the time-average AoC under both deadlines. Utilizing this expression, we obtain a closed-form solution for M/M/1-M/M/1 systems under soft deadlines and propose an accurate approximation for hard deadlines. These results are further extended to G/G/1-G/G/1 systems. Additionally, we introduce the concept of computation throughput, derive its general expression and an approximation, and explore the trade-off between freshness and throughput. In the multi-source, time-discrete networks, tasks are scheduled for offloading to a computational node. For this scenario, we develop AoC-based Max-Weight policies for real-time scheduling under both deadlines, leveraging a Lyapunov function to minimize its drift.