Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach

Abstract

The constant increase in wireless handheld devices and the prospect of billions of connected machines has compelled the research community to investigate different technologies which are able to deliver high data rates, lower latency and better reliability and quality of experience to mobile users. One of the problems, usually overlooked by the research community, is that more connected devices require proportionally more signalling overhead. Particularly, acquiring users' channel state information is necessary in order for the base station to assign frequency resources. Estimating this channel information with full resolution in frequency and in time is generally impossible, and thus, methods have to be implemented in order to reduce the overhead. In this paper, we propose a channel quality estimation method based on the concept of Gaussian process regression to predict users' channel states for varying user mobility profiles. Furthermore, we present a dual-control technique to determine which is the most appropriate prediction time for each user in order to keep the packet loss rate below a pre-defined threshold. The proposed method makes use of active learning and the exploration-exploitation paradigm, which allow the controller to choose autonomously the next sampling point in time so that the exploration of the control space is limited while still reaching an optimal performance. Extensive simulation results, carried out in an LTE-A simulator, show that the proposed channel prediction method is able to provide consistent gain, in terms of packet loss rate, for users with low and average mobility, while its efficacy is reduced for high-velocity users. The proposed dual-control technique is then applied, and its impact on the users' packet loss is analysed in a multicell network with proportional fair and maximum throughput scheduling mechanisms. Remarkably, it is shown that the presented approach allows for a reduction of the overall channel quality signalling by over 90 % while keeping the packet loss below 5 % with maximum throughput schedulers, as well as signalling reduction of 60 % with proportional fair scheduling.

Robust predictive filtering schemes for sub‐band CQI feedback compression in 3GPP LTE systems

“…However, this saving comes at the expense of a marginal degradation in packet loss, feedback reduction and sector throughput performance. A summary of the identified key performance indicators for the proposed schemes compared with both the 3GPP eNB configured sub‐band scheme and the work in [16], which represent the latest work in this field, is shown in Table 8. The feedback reduction and throughput degradation is computed with reference to the 3GPP eNB configured sub‐band feedback scheme, whereas the complexity reduction of the sign clipped LMS‐CQI prediction filtering scheme is computed with reference to the LMS‐CQI prediction filtering scheme.…”

“…Similarly, in [14, 15] the authors propose several CQI prediction schemes based on Wiener filter, cubic spline extrapolation and short‐term average. In [16], Chiumento et al propose an adaptive channel quality estimation method based on Gaussian process (GP) regression at the base station. This GP‐based CQI prediction is exploited in a dual‐control technique which makes use of active learning to determine an optimal prediction time for each user, achieving a 77% signalling overhead reduction when compared with the 3GPP standard eNB‐configured sub‐band feedback compression technique with a packet loss rate (PLR) of 10%.…”

Anticipatory radio resource management for mobile video streaming with linear programming

“…The anticipatory, or proactive, allocation of wireless channel resources is typically used to compensate for delayed channel state information for a small number of upcoming transmission times [7], [8]. Operating close to the coherence time, these schemes predict small-scale fading in the millisecond regime.…”

Enabling 5G mobile wireless technologies

“…Moreover, estimation and feedback of channel state and other system information must be rethought to achieve scalable and efficient solutions in future wireless networks. These are the topics considered in three papers of our special issue [25][26][27]. While the work [25] develops a scalable channel state information (CSI) estimation with controllable resolution and complexity for future dense networks, the paper [26] studies the CSI estimation problem for a very wideband wireless channel using the sampling rate less than the Nyquist rate.…”

“…These are the topics considered in three papers of our special issue [25][26][27]. While the work [25] develops a scalable channel state information (CSI) estimation with controllable resolution and complexity for future dense networks, the paper [26] studies the CSI estimation problem for a very wideband wireless channel using the sampling rate less than the Nyquist rate. In addition, an iterative estimation technique for total noise and interference in multi-carrier wireless systems is developed to assist the data detection in [27], which is relevant for dense wireless networks with aggressive frequency reuse.…”