We consider an optimal cache-placement-anddelivery-policy using Network-level Orthogonal Multipoint Multicasting (OMPMC) for wireless networks. The placement of files in caches of Base Station (BS) is based on a probabilistic model, with controlled cache placement probabilities. File delivery is based on multipoint multicast and network-based orthogonal transmission; all BSs in the network caching a file transmit it synchronously in dedicated radio resources. If the average signal-to-noise ratio associated to a file at a requesting user is less than a threshold, the request is in outage. We derive a closed-form expression for the outage probability for a network modeled as a Poisson Point Process. An optimal caching policy is solved from an optimization problem, and compared to a threshold-based policy, suboptimal partial solutions, and single-point cache delivery. Simulation results show that exploiting OMPMC with optimal cache and bandwidth allocation significantly improves the overall outage probability as compared to single point delivery.
We consider optimal cache placement and delivery for Orthogonal Multipoint Multicasting (OMPMC) cellular systems. In OMPMC, all Base Stations (BSs) that cache a file transmit identical signals in a dedicated frequency resource. The simultaneous transmissions create artificial multipath propagation, which creates Inter-Block Interference (IBI) and Inter-Carrier Interference (ICI) in Orthogonal Frequency Division Multiplexing systems where the Cyclic Prefix (CP) is shorter than the maximum propagation delay. The placement of files at BS caches is based on a probabilistic model. A file request is in outage if the average signal-to-interference-and-noise ratio associated with a request is less than a threshold. We formulate the cache policy and bandwidth allocation as a joint optimization problem aiming to minimize the total outage probability, and considering the effect of IBI and ICI. Despite that the outage probability does not have a closed form expression, we are able to devise an algorithm to find the optimal solution based on predictorcorrector approach. Simulations results are used to demonstrate the capability of the proposed algorithm to find the optimum cache policy. Simulation results show that the effect of ICI/IBI has to be considered in designing OMPMC caching policy.
We consider a reinforcement learning (RL) based joint cache placement and delivery (CPD) policy for cellular networks with limited caching capacity at both Base Stations (BSs) and User Equipments (UEs). The dynamics of file preferences of users is modeled by a Markov process. User requests are based on current preferences, and on the content of the user's cache. We assume probabilistic models for the cache placement at both the UEs and the BSs. When the network receives a request for an un-cached file, it fetches the file from the core network via a back-haul link. File delivery is based on network-level orthogonal multipoint multicasting transmissions. For this, all BSs caching a specific file transmit collaboratively in a dedicated resource. File reception depends on the state of the wireless channels. We design the CPD policy while taking into account the user Quality of Service and the back-haul load, and using an Actor-Critic RL framework with two neural networks. Simulation results are used to show the merits of the devised CPD policy.
Many sequential decision-making problems need optimization of different objectives which possibly conflict with each other. The conventional way to deal with a multi-task problem is to establish a scalar objective function based on a linear combination of different objectives. However, for the case of having conflicting objectives with different scales, this method needs a trial-and-error approach to properly find proper weights for the combination. As such, in most cases, this approach cannot guarantee an optimal Pareto solution. In this paper, we develop a single-agent scale-independent multi-objective reinforcement learning on the basis of the Advantage Actor-Critic (A2C) algorithm. A convergence analysis is then done for the devised multi-objective algorithm providing a convergence-in-mean guarantee. We then perform some experiments over a multi-task problem to evaluate the performance of the proposed algorithm. Simulation results show the superiority of developed multi-objective A2C approach against the single-objective algorithm.
We consider an optimal cache-placement-anddelivery-policy where traffic is offloaded from Single-Point Unicast (SPUC) service by using network-level Orthogonal Multipoint Multicast (OMPMC) scheme. The files are classified into two sets. The most popular files are cached at the BSs using a probabilistic approach and are served by OMPMC. The remaining files are fetched from the core network on demand and served by SPUC. Optimal compound scheme is analyzed, based on resource allocation between OMPMC and multi-antenna SPUC schemes. If a user is not able to successfully receive the requested file due to its experienced signal-to-interference-plusnoise ratio, its request is in outage. A closed-form expression is derived for the total outage probability based on stochastic geometry for the compound scheme. An optimization problem is formulated to design the caching policy for the compound scheme. The optimal solution to this problem is obtained by finding optimal cache placement, bandwidth allocation, and file classification. Simulation results show that the compound scheme outperforms other caching schemes in terms of the total outage probability.
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