# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
###### Abstract  
Although fifth-generation (5G) wireless communication can ] support well a high data rate of transmission, issues such as base station (BS) failure and poor BS signals may cause serious interruption problems. This paper studies the user-BS selection strategy with received signal strength indication (RSSI)-based reliability in 5G wireless networks. First, reliability is defined on the basis of the RSSI and failure probability of the BS. The problem is modeled as a selection strategy optimization problem with BS capacity and receiving sensitivity as constraints. Second, the original problem can be transformed into a resource allocation problem with probabilistic constraints. For the situation where user distribution is known, we used dynamic programming to obtain the optimal BS selection strategy. For the situation where user distribution is unknown, starting from user trajectory data, we used the space-time density estimation method based on the Epanechnikov kernel to estimate user density and bring it into dynamic programming to obtain the optimal selection strategy. Simulation results show that our density estimation algorithm is more accurate than the commonly used density estimation algorithm. Compared with the distance-based optimization method, our RSSI-based optimization method also improved the communication signal quality under different scenarios. wireless network; selection-strategy optimization; RSSI; dynamic programming; space-time density estimation +
Footnote †: journal: Applied  
systems, BSs are usually deployed to achieve seamless network coverage. Whether a user can be covered by a BS depends on the distance between them [4]. However, such a user-BS connection strategy based solely on distance is difficult to meet the high standards of 5G wireless networks [6], since distance is only from a geometric point of view and can only reflect limited information. The standard requirements of the 5G networks are quite different from 4G in terms of low latency, bandwidth, reliability, and availability [7]. In order to deal with the problems encountered in actual situations, the user-BS connection strategy mostly starts from some utility indicators such as spectral and energy efficiency, and quality of service (QoS) [8]. However, instead of being inherent properties, these utility indicators are defined by people. In addition to difficulties in obtaining, there may also exist problems such as inconsistent standards. In order to address the above problems, we need to consider a common and real indicator that can easily be obtained in practical scenarios. In addition, the above studies rarely take reliability into account, while 5G wireless networks have high requirements for reliability. Reliability is one of the most important factors of wireless network communication quality; it can not only improve user experience, but also help operators in operation and maintenance. On the basis of the above motivations, we considered modeling the user-BS connection strategy problem as an optimization problem with reliability as the optimization object. The received signal strength indicator (RSSI) is commonly used in the communication localization field [9]. RSSI can easily be obtained from most WiFi receivers such as mobile phones, tablets, and laptops [10; 11]. RSSI meets the above requirements, so we chose to optimize reliability with RSSI. This paper studies the optimization of 5G wireless communication networks based on RSSI by rationally designing a BS-user strategy selection scheme. For the selection strategy, our goal was to maximize the communication signal quality of the overall wireless network when BS capacity and receiving sensitivity constraints are met. Since the constraint function exists in the form of an expectation function that cannot be handled by the traditional random approximation simulated annealing (SA) algorithm, we began from user distribution and considered the two cases where user distribution is known and unknown.