In this paper, we propose a power allocation algorithm to guarantee quality of experience (QoE) while considering the impact of imperfect channel state information (CSI) in multi-connectivity multiple-input multiple-output (MIMO) systems. Multi-connectivity is a promising solution for wireless technologies to fulfill ever-increasing demands for QoE via massive MIMO. However, the performance of MIMO systems mostly depends on the accuracy of the channel estimation algorithm that provides CSI. Under imperfect CSI scenarios, increasing transmit power to satisfy QoE for one user exacerbates interference with others, thus leading to a tradeoff between power consumption and user satisfaction. To guarantee QoE while minimizing transmission power under imperfect CSI, the effect of imperfect CSI is quantified, and a closed form signal-to-interference-plus-noise ratio (SINR) is derived. Then, a two-stage algorithm categorizes UEs into two groups: those that only need single connectivity and those that need dual connectivity (i.e., adaptive connectivity). After classification, transmission power is allocated to guarantee QoE via a power control strategy that minimizes the transmission power. By comparing performance with a single connectivity–based algorithm and fixed multi-connectivity–based algorithms, we show that our proposed algorithm not only satisfies all the UEs in the system but also consumes less transmission power than the benchmarks.

The following topics are dealt with: wireless channels; optimisation; resource allocation; cellular radio; learning (artificial intelligence); MIMO communication; telecommunication network reliability; telecommunication traffic; 5G mobile communication; radiofrequency interference.

Handover failure and ping-pongs are the common thorny issues in modern mobile networks. While handover failures caused by radio link failure (RLF) significantly reduces the reliability of network operation, ping-pongs drastically waste signaling resources. In the upcoming fifth-generation (5G) networks especially, a complex deployment of small cells can exacerbate the two problems, even though the network can be integrated with a self-organizing network (SON), which is an automation-based solution. Due to the coupling of RLFs and ping-pongs as explained in the literature, it is difficult to analyze handovers and minimize both RLFs and ping-pongs simultaneously. In this paper, we model a handover procedure with geometric elements (Apollonian circles and the straight line), and analyze handover performance. The analysis provides an optimal handover setting for minimizing both RLFs and ping-pongs together, whereas previous works only considered trade-offs between them. We show that our analysis accurately estimates the optimal setting by comparing it with an NS-3 simulation. From the analysis, different environments can require different optimal values: fading (as well as interference) limit the optimal values; user speed has a scaling impact; and time-to-trigger has a shifting effect.