In Reconfigurable intelligent surface (RIS)-assisted systems the acquisition of channel state information (CSI) and the optimization of the reflecting coefficients constitute a pair of salient design issues. In this paper, a novel channel training protocol is proposed, which is capable of achieving a flexible performance vs. signalling and pilot overhead as well as implementation complexity trade-off.More specifically, first of all, we conceive a holistic channel estimation protocol, which integrates the existing channel estimation techniques and passive beamforming design. Secondly, we propose a new channel training framework. In contrast to the conventional channel estimation arrangements, our new framework divides the training phase into several periods, where the superimposed end-to-end channel is estimated instead of separately estimating the direct BS-user channel and cascaded reflected BS-RISuser channels. As a result, the reflecting coefficients of the RIS are optimized by comparing the objective function values over multiple training periods. Moreover, the theoretical performance of our channel training protocol is analyzed and compared to that under the optimal reflecting coefficients. In addition,

Intelligent reflecting surfaces (IRS) have emerged as a promising technology of managing the radio signal propagation by relying on a large number of low-cost passive reflecting elements. In this letter, the optimal pilot power allocation required for accurate channel estimation of IRS-assisted communication systems is investigated. In contrast to conventional channel estimators, where pilot signals are usually designed to be constant-enveloped, we reconsider the pilot design to improve the passive beamforming performance thus resulting in an improved achievable rate. At first sight the result of our analysis appears counter-intuitive, suggesting that at a given total power, more power should be allocated to estimate low-gain channels, since the channel phase impairments are more severe than those of highgain channels. Our simulation results show that when the number of IRS elements is 4, the rate improvement of our proposed channel estimation scheme over the conventional counterpart may be as high as 25%.

In the presence of irregular transmission/reception point (TRP) topologies and non-uniform user distribution, the user-to-node association optimization is a rather challenging process in real user-centric networks, especially for the joint transmission aided coordinated multipoint (CoMP) technique. The grade of challenge further escalates, when taking the dynamic user scheduling process into account in order to enhance the system capacity attained. To tackle the above-mentioned problem, we holistically optimize the system by conceiving joint user scheduling and user-to-node association. Then, for the sake of striking a significantly better balance between the network capacity and coverage quality, we propose a generalized reinforcement learning assisted framework intrinsically amalgamated both with neuralfitted Q-iteration as well as with ensemble learning and transfer learning techniques. Consequently, a powerful policy can be found for dynamically adjusting the set of TRPs participating in the joint transmission, thus allowing the CoMP-region to breathe, depending on both the temporal and geographical distribution of the tele-traffic load across the network. To facilitate the prompt learning of the global policy supporting flexible scalability, the overall network optimization process is decoupled into multiple local optimization phases associated with a number of TRP clusters relying on iterative information exchange among them. Our simulation results show that the proposed scheme is capable of producing a policy achieving a network-edge throughput gain of up to 140% and a network capacity gain of up to 190% under the challenging scenario of having a non-uniform geographical UE distribution and bursty traffic.

A promising solution for massive Multiple-Input Multiple-Output (m-MIMO) systems is Hybrid digital-analogue (HDA) beamforming as it offers a balanced trade-off between energy efficiency (EE) and spectral efficiency (SE). The lack of practical demonstrations in the open literature is primarily because those most existing works require a large number of phase shifters (PSs), radio frequency (RF) switches, power dividers (PDs), and power combiners which contribute to high hardware complexity and energy consumption (due to insertion loss). In this paper, we introduce a practical dynamic subarray m-MIMO structure that is based on reconfigurable power dividers (RPDs). The extensive system simulation results, considering hardware imperfection extracted from a practical RPD implementation, indicate that the proposed RPD-based dynamic HDA m-MIMO outperforms the fixed subarray counterpart.