Proactive pushing has emerged as a promising solution to scale the service capacity by utilizing idle spectrum resources when on-demand transmission cannot fully exploit the spectrum resources during off-peak times. How to schedule both pushing and on-demand transmission jointly to meet a higher spectrum efficiency becomes a critical issue. Moreover, efficient and fair spectrum sharing among multiple resource schedulers remains open. In this paper, we introduce virtual network operators (VNOs) as schedulers that pay for consumed bandwidth and jointly schedule pushing and on-demand services. We adopt nonlinear spectrum pricing schemes with a convex and increasing price function enabling each VNO to share spectrum resources appropriately and independently. Considering the revenue from users and the cost for spectrum, we formulate a Markov decision process (MDP) to maximize the profit of VNO. A modified value iteration algorithm is applied to solve the MDP with reduced computational complexity. Furthermore, we show the structure of the optimal policy and provide the upper and lower bounds for the optimal performance. We present a low-complexity heuristic policy that can scale in practice with large state spaces and action spaces.

Massive access has attracted considerable recent attention because it holds the promise of efficiently enabling connections of extensive devices in machine-type communications. A large portion of massive access protocols exploit the reservation-based mechanisms that can satisfy random access requests while avoiding much bandwidth waste due to packet collisions. In this paper, we present a unified framework based on a tandem queue model for analyzing and optimizing reservation-based random access protocols with arbitrary collision resolution methods. More specifically, the two queues characterize the channel reservation and packet transmission respectively. We present the high-dimensional Markov chain of the tandem queue, the transition matrix of which can be derived from arbitrary collision resolution algorithms. The transition matrix then allows us to calculate the average packet delay by solving a linear equation. To provide further insights, a typical reservation based access using tree splitting for collision resolution is analyzed from both theoretical and numerical perspectives.

Real-time monitoring has received considerable attention recently due to its potential in automatic driving, tele-surgery, and factory automation in the 6G era. In remote estimation or reconstruction of stochastic processes, the statistical properties of stochastic processes to be monitored play a central role. Among the stochastic processes interested by real-time applications, the Brownian motion, also known as the Wiener processes is a typical one. In this paper, we are interested in how to monitor Brownian motions efficiently, timely, and reliably. To achieve this goal, we reveal that the real-time estimation error is jointly determined by the quantization error and freshness of data samples. Based on this observation, we present an optimal joint sampling and quantization scheme that efficiently balances the quantization distortion and the age-of-information (AoI). Furthermore, we find that the error accumulation will lead to infinite distortion as monitoring time increases. To overcome this, a multi-layer error correction method is presented for infinite-time monitoring, in which bounded distortion can be achieved with limited data rate. Finally, to conquer the accumulation of transmission errors in unreliable channels, we present an error correction mechanism based on periodic feedback. Diffusion approximation is then adopted to determine the optimal feedback rate and interval.

Proactive pushing can exploit the spectrum underutilized during the off-peak time to push popular content files, thereby significantly improving the spectrum efficiency. Moreover, in a communication system that the virtual network operator (VNO) has to buy spectrum from the base station to conduct file transmission, proactive pushing has been recognized as a promising technology to improve the income of the VNO. However, the appropriate pushing schemes and the achievable income of the VNO are unclear yet. In this paper, joint pushing, pricing, and recommendation (JPPR) schemes are presented for cache-enabled radio access networks. We aim to investigate recommendation-based pushing policy to maximize the average income of the VNO. We establish a Markov chain model, which derives the average income of the VNO. Based on this, we formulate an optimization problem to achieve the maximum average income. We further convert the optimization problem into an equivalent linear programming problem. Moreover, a greedy algorithm is applied to solve the problem with lower computational complexity. Finally, simulation results show the significant income gains that can be achieved by JPPR schemes compared with the system without the JPPR schemes.

As mobile data traffic has explosively grown during the past decades, pushing popular contents to small cells has been proposed to deal with the growing data demands. To improve the cache hit ratio, the recommender system is employed to recommend cached contents when the requests are not hit by the cache. However, how to persuade users to accept recommended files remains an open problem. We conceive a method that the network operators can give a discount on the traffic cost of the recommended contents. However, some users may maliciously request unpopular contents to get the discount, which will reduce the profit of the virtual network operator (VNO). In order to punish the malicious behaviour, we the VNOcan reduce the recommendation probability to these users. Meanwhile, these malicious users will reduce the malicious probability to increase the revenue. To study the interactions between the profit of the VNO and the revenue of the users, we formulate a non-cooperative game to find the Nash equilibrium (NE) of the recommendation probability of the VNO and the malicious probability of the users. Simulation results indicate that the VNO's profit and the user's revenue can be significantly increased with the proposed system compared with the system without joint recommendation and pricing schemes.

Proactive pushing holds the promise of significantly improving spectrum efficiency. However, we are confronted with challenges including increasingly scarce spectrum resources and extra costs of pushing. As a result, the spectrum should be carefully shared between proactive pushing and on-demand transmission to avoid waste of resources. In this paper, we take into account the constraints of spectrum resources. We consider a pricing-based resource scheduling so that we can make a profitably efficient use of the limited spectrum resources. To maximize the income of content providers, we formulate a non-linear optimization problem, which is linearized to be more tractable. Numerical results demonstrate that appropriate pushing can bring significant gain on the income with scarce spectrum resources.

Edge caching has become a promising technology in future wireless networks owing to its remarkable ability to reduce peak data traffic. However, the storage resource can be limited in practice hence only a small amount of files can be cached. How to improve the cache hit ratio in finite-buffer caching based on the prediction of user demands has become an important problem. In this paper, we study caching policies with finite buffer by exploiting the prediction of a user's request time, referred to as request delay information (RDI). Based on RDI, we maximize the average cache hit ratio through a Markov decision process (MDP) approach. Specifically, we formulate an MDP problem and apply a modified value iteration algorithm to find an optimal caching policy. Moreover, we provide an upper bound and a lower bound for the cache hit ratio, as well as an analytical cache hit ratio with small buffers. To address the issue that the state space can be prohibitively large in practice, we present a low-complexity heuristic caching policy that is shown to be asymptotically optimal. Simulation results show that introducing RDI may bring significant cache hit ratio gain when the buffer size is limited.

Caching is a promising technology that holds the potential of substantially improving the bandwidth efficiency and reducing peak data traffic. As a result, the maximization of cache hit ratio in proactive caching has attracted much attention most recently. Unfortunately, the hit ratio can be very limited due to the constrained buffer size. In this paper, we are interested in maximizing the hit ratio for users with limited buffer size by exploiting the prediction of a user's request time for content files, also referred to as the request delay information (RDI). More specifically, the hit ratio is maximized by keeping the popular and storage-efficient content files in a receiver buffer. To achieve this goal, we formulate an infinite horizon Markov decision problem (MDP) that can be efficiently solved by a value iteration algorithm. For more practical applications, we present a heuristic caching policy that can greatly reduce the computational complexity when the buffer size is large and the content arrival rate is high, thereby holding a great potential in practice. Simulation results show that the RDI may bring significant hit ratio gain, especially in small buffer scenarios.