text: 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.
  @cite_5: The paper validates the feasibility of automating the setting of common pilot power in a WCDMA radio network. The pilot automation improves operability of the network and it is implemented with a control software aiming for load and coverage balancing. The control applies measurements of base station total transmission power of neighboring cells and terminal reports of received pilot signal level to determine the pilot qualification. The pilot power of a cell is periodically updated with simple heuristic rules in order to improve the load and coverage balance. The approach was validated using a dynamic WCDMA system simulator with a deployment of macro and micro cells on a city region whose measured propagation characteristics were incorporated into the model. The results showed that the proposed control method balanced load and coverage and improved the air interface performance measured as a function of packet throughput.
  @cite_15: Base station (BS) sleeping is an effective way to improve the energy-efficiency of cellular networks. However, it may bring extra user-perceived delay. We conduct a theoretical study into the impact of BS sleeping on both energy-efficiency and user-perceived delay. We consider hysteresis sleep and three typical wake-up schemes, namely single sleep, multiple sleep, and @math -limited schemes. We model the system as an @math vacation queue, which captures the setup time, the mode-changing cost, as well as the counting or detection cost during the sleep mode. Closed-form expressions for the average power and the Laplace–Stieltjes transform of delay distribution are obtained. The impacts of system parameters on these expressions are analyzed. We then formulate an optimization problem to design delay-constrained energy-optimal BS sleeping policies. We show that the optimal solutions possess a special structure, thereby allowing us to obtain them explicitly or numerically by simple bisection search. In addition, the relationship between the optimal power consumption and the mean delay constraint is analyzed, so as to answer the fundamental question: how much energy can be saved by trading off a certain amount of delay? It is shown that this optimal relationship is linear only when the delay constraint is lower than a threshold. Numerical studies are also conducted, where the impact of detection or counting cost during the sleep mode is explored, and the delay distribution under the optimal policy is obtained.
  @cite_13: Pilot power management is an important issue for coverage planning in UMTS systems. We consider the problem of minimizing the pilot power subject to the constraint of full service coverage. For this planning problem, which is NP-hard in complexity, effective methods being able to deal with large-scale networks of heterogeneous cell coverage patterns are highly desirable. We propose an integer linear optimization formulation, and a decomposition method that exploits the problem structure using a Dantzig-Wolfe reformulation. We report numerical results for networks of various sizes. The proposed method efficiently finds near-optimal solutions that yield substantial savings in power consumption when compared to baseline approaches.
  @cite_20: Radio coverage optimization is a key element in improving the overall performance of femtocell cellular networks. Owing to the increased size and complexity of femtocell networks, there is an imperative need to develop decentralized coverage optimization algorithms, which is a highly challenging task as these algorithms have to work without global information and coordinated central control of network nodes. In this paper, we consider a deployment scenario of a group of @math femtocells in an enterprise environment and propose a decentralized algorithm for joint femtocell coverage area optimization. The algorithm updates the femtocell's pilot Tx power only and serves to balance the user load amongst the collocated femtocells as well as minimizing the coverage holes and pilot Tx power. Compared to the fixed pilot Tx power allocation, the algorithm introduces an improvement of approximately 18 in terms of supported user traffic, in addition to a significant reduction of pilot leakage to neighboring cells.
summary: 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.).