The growing complexity of networks and the variety of future scenarios with diverse and often stringent performance requirements call for a higher level of automation. Intent-based management emerges as a solution to attain high level of automation, enabling human operators to solely communicate with the network through high-level intents. The intents consist of the targets in the form of expectations (i.e., latency expectation) from a service and based on the expectations the required network configurations should be done accordingly. It is almost inevitable that when a network action is taken to fulfill one intent, it can cause negative impacts on the performance of another intent, which results in a conflict. In this paper, we address the challenge of conflict resolution in intent-based networking and propose an online learning approach based on the hierarchical multi-armed bandit framework for autonomous network management. The hierarchical structure enables efficient exploration and exploitation of network configurations while adapting to dynamic network conditions. Our proposed hierarchical multi-armed bandit conflict resolution (MABCR) approach optimizes resource allocation within a partially known system with limited bandwidth. In comparison to other approaches, we show that our algorithm is an effective approach regarding resource allocation and satisfaction of intent expectations.

We jointly optimize Discontinuous Reception (DRX) cycle length and LTE scheduling to minimize mobile devices' energy usage for video delivery, utilising the now well-established potential to predict future channel conditions in cellular networks. Employing in-network caching, we set a strict buffer constraint which provides zero buffer underflow to improve Quality of Experience. Our study provides insight into the energy saving potential sophisticated DRX schemes hold, compared with the currently used static method. To this end, two novel DRX approaches are proposed and studied. The results show that more sophisticated DRX schemes (with variable DRX cycle length) can potentially save 69 percent energy for mobile devices, encouraging further research in the field.

In this paper, optimal resource allocation policies are characterized for wireless cognitive networks under the spectrum leasing model. We propose cooperative schemes in which secondary users share the time-slot with primary users in return for cooperation. Cooperation is feasible only if the primary system's performance is improved over the non-cooperative case. First, we investigate a scheduling problem where secondary users are interested in immediate rewards. Here, we consider both infinite and finite backlog cases. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long-term basis, in return for cooperation. Finally, we present a power allocation problem where the goal is to maximize the expected net benefit defined as utility minus cost of energy. Our proposed scheduling policies are shown to outperform non-cooperative scheduling policies, in terms of expected utility and net benefit, for a given set of feasible constraints. Based on Lyapunov optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

It is well known that Max-Weight type scheduling algorithms are throughput optimal since they achieve the maximum throughput while maintaining the network stability. However, the majority of existing works employing Max-Weight algorithm require the complete channel state information (CSI) at the scheduler without taking into account the associated overhead. In this work, we design a Scheduling and Selective Feedback algorithm (SSF) taking into account the overhead due to acquisition of CSI. SSF algorithm collects CSI from only those users with sufficiently good channel quality so that it always schedules the user with the highest queue backlog and channel rate product at every slot. We characterize the achievable rate region of SSF algorithm by showing that SSF supports 1 + ϵ fraction of the rate region when CSI from all users are collected. We also show that the value of ϵ depends on the expected number of users which do not send back their CSI to the base station. For homogenous and heterogeneous channel conditions, we determine the minimum number of users that must be present in the network so that the rate region is expanded, i.e., ϵ > 0. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by collecting CSI from only less than 50% of all users in a CDMA based cellular network utilizing high data rate (HDR) protocol.

It is well known that opportunistic scheduling algorithms are throughput optimal under dynamic channel and network conditions. However, these algorithms achieve a hypothetical rate region which does not take into account the overhead associated with channel probing and feedback required to obtain the full channel state information at every slot. In this work, we design a joint scheduling and channel probing algorithm by considering the overhead of obtaining the channel state information. We adopt a correlated and non-stationary channel model, which is more realistic than those used in the literature. We use concepts from learning and information theory to accurately track channel variations to minimize the number of channels probed at every slot, while scheduling users to maximize the achievable rate region of the network. Simulation results show that with the proposed algorithm, the network can carry higher user traffic.

It is well known that Max-Weight scheduling provides queue stability whenever this is possible. However, Max-Weight scheduling requires the complete channel state information (CSI) to make the best transmission decision at every time slot. The common assumption in this line of research assumes that the network controller has full CSI at every decision time without taking into account the overhead associated with channel probing. In practice, however, acquiring CSI is not cost-free and requires certain amount of resources. In this work, we design a Scheduling and Dynamic Feedback algorithm, named SDF, by considering the overhead of obtaining the channel state information. We first establish a bound on the achievable rate region of SDF algorithm by proving that SDF supports 1+ ϵ fraction of of the full rate region (the rate region when all users are probed) where ϵ only depends on the expected number of users which are not probed. Then, for homogenous channel, we show that when the number of users in the network is greater than 3, ϵ >;0, i.e., we guarantee to expand the rate region. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by probing only less than 50% of all channels in a CDMA based cellular network utilizing high data rate protocol under normal channel conditions.

Optimal transmission scheduling in wireless cognitive networks is considered under the spectrum leasing model. We propose a cooperative scheme in which secondary nodes share the time slot with primary nodes in return for cooperation. Cooperation is feasible only if the system's performance is improved over the non-cooperative case. First, we investigate a scenario where secondary users are interested in immediate rewards. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long term basis, in return for cooperation. In both scenarios, our proposed schemes are shown to outperform non-cooperative scheduling schemes, in terms of both individual and total expected utility, for a given set of feasible constraints. Based on Lyapunov Optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.