Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches have great potentials to automate and optimize network configuration. Learning-based approaches face the challenges of learning a highly complex function for each base station and balancing the fundamental exploration-exploitation tradeoff while minimizing the exploration cost. Fortunately, in cellular networks, base stations (BSs) often have similarities even though they are not identical. To leverage such similarities, we propose kernel-based multi-BS contextual bandit algorithm based on multi-task learning. In the algorithm, we leverage the similarity among different BSs defined by conditional kernel embedding. We present theoretical analysis of the proposed algorithm in terms of regret and multi-task-learning efficiency. We evaluate the effectiveness of our algorithm based on a simulator built by real traces.
Various aspects of network parameter configuration have been studied in the literature, such as pilot power configuration, spectrum, handoff threshold, etc. Traditional approaches derive analytical relationship between network configuration and its performance based on communication theory, such as @cite_13 @cite_5 @cite_20 @cite_15 . Such approaches are often prohibitively complex, involve various approximations, and require a significant amount of input information (such as the number of users, the location of each user, etc.).
@cite_5 @cite_15 @cite_13 @cite_20
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Cellular network configuration is critical for network performance. Current practice is labor-intensive, error-prone, and far from optimal. To automate efficient cellular network configuration, in this work, we propose an online-learning-based joint-optimization approach that addresses a few specific challenges: limited data availability, convoluted sample data, highly complex optimization due to interactions among neighboring cells, and the need to adapt to network dynamics. In our approach, to learn an appropriate utility function for a cell, we develop a neural-network-based model that addresses the convoluted sample data issue and achieves good accuracy based on data aggregation. Based on the utility function learned, we formulate a global network configuration optimization problem. To solve this high-dimensional non-concave maximization problem, we design a Gibbs-sampling-based algorithm that converges to an optimal solution when a technical parameter is small enough. Furthermore, we design an online scheme that updates the learned utility function and solves the corresponding maximization problem efficiently to adapt to network dynamics. To illustrate the idea, we use the case study of pilot power configuration. Numerical results illustrate the effectiveness of the proposed approach. This article outlines the fundamental modeling issues of opportunistic spectrum access in cognitive radio networks. In particular, we identify the effects of connection-based channel usage on the QoS performance of spectrum management techniques. During the transmission period of a secondary user's connection, the phenomenon of multiple spectrum handoffs due to interruptions of primary users arises quite often. In addition to multiple interruptions, the connection-based channel usage behaviors are also affected by spectrum sensing time, switching between different channels, generally distributed service time, and channel contention between multiple secondary users. An analytical framework based on the preemptive resumption priority M G 1 queueing theory is introduced to characterize the effects of the above factors simultaneously. The proposed generalized analytical framework can incorporate various system parameters into the design of very broad spectrum management techniques, including spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. The applications of this analytical framework on spectrum decision as well as spectrum mobility are discussed, and some open issues using this framework are suggested in this article. Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control as well as the maximum power limitations for UEs. More specifically, for interference mitigation and robust uplink communication, each UE is required to control its transmit power such that the average received signal power at its serving base station (BS) is equal to a certain threshold ρ o . Due to the limited transmit power, the UEs employ a truncated channel inversion power control policy with a cutoff threshold of ρ o . We show that there exists a transfer point in the uplink system performance that depends on the following tuple: BS intensity λ, maximum transmit power of UEs P u , and ρ o . That is, when P u is a tight operational constraint with respect to (w.r.t.) λ and ρ o , the uplink outage probability and spectral efficiency highly depend on the values of λ and ρ o . In this case, there exists an optimal cutoff threshold ρ o *, which depends on the system parameters, that minimizes the outage probability. On the other hand, when P u is not a binding operational constraint w.r.t. λ and ρ o , the uplink outage probability and spectral efficiency become independent of λ and ρ o . We obtain approximate yet accurate simple expressions for outage probability and spectral efficiency, which reduce to closed forms in some special cases. How to keep the probability of hand-off drops within a prespecified limit is a very important quality-of-service (QoS) issue in cellular networks because mobile users should be able to maintain ongoing sessions even during their hand-off from one cell to another. We design and evaluate predictive and adaptive schemes for bandwidth reservation for the hand-offs of ongoing sessions and the admission control of new connections. We first develop a method to estimate user mobility based on an aggregate history of hand-offs observed in each cell. This method is then used to probabilistically predict mobiles' directions and hand-off times in a cell. For each cell, the bandwidth to be reserved for hand-offs is calculated by estimating the total sum of tractional bandwidths of the expected hand-offs within a mobility-estimation time window. Three different admission-control schemes for new connection requests using this bandwidth reservation are proposed. We also consider variations that utilize the path location information available from the car navigation system or global positioning system. Finally, we evaluate the performance of the proposed schemes extensively to show that they meet our design goal and outperform the static reservation scheme under various scenarios.