| By flexibly reconfiguring the electromagnetic propagation environment through adjustment of the phase shifts of its reflecting elements,reconfigurable intelligent surface(RIS)is envisioned to be a promising technique for enhancing the spectrum and energy efficiency of 6G-and-beyond communications systems.To reap the benefits promised by RIS,accurate channel state information(CSI)is required.However,due to the following two facts that an RIS equipped with passive elements is typically not capable of executing complex signal processing tasks,and the cascaded channel contains a large number of channel coefficients,current channel estimation techniques for RIS-aided systems faces many challenges including: large pilot training overhead,high computational complexity,relatively low estimation accuracy,and the mismatch between the practical hardware and theoretical models.Against the above background,this thesis conducts a very intensive study on channel estimation for RIS-aided multi-user(MU)systems based on the structured channel models.A number of innovative results are obtained as follows:First,an efficient three-stage channel estimation method is developed for an RISaided MU multiple-input single-output(MISO)systems,in which both the base station(BS)and the RIS are equipped with a uniform planar array(UPA).Specifically,in Stage I,the CSI of a typical user is estimated.To address the power leakage issue for the common angles-of-arrival(Ao As)estimation in this stage,a low-complexity onedimensional search method is developed.In Stage II,with the estimated information from Stage I and the correlation among multiple cascaded channel matrices,a re-parameterized common RIS-BS channel is constructed to estimate other users’ CSI.In Stage III,only the rapidly varying channel gains need to re-estimated.Additionally,the RIS phase shift training matrices are designed so as to improve the estimation performance.Simulation results validate that the proposed method outperforms other existing algorithms in terms of pilot overhead,and approaches the genie-aided upper bound in the high signal-to-noise(SNR)region.Secondly,an effective two-phase channel estimation method is proposed for an RISaided multiple-input multiple-output(MIMO)systems with a multi-antenna UPA-type BS,a multi-element UPA-type RIS and multiple multi-antenna UPA-type users.The angles-of-departure(Ao Ds)at the users and the common Ao As at the BS are estimated in Phase I and Phase II,respectively.Then,the estimation of a multi-antenna channel with J paths is decomposed into the estimation of J single-path channels.Therefore,the cascaded Ao Ds at the RIS and the channel gains can be estimated using methods similar to those developed for the single-antenna case in this thesis.With the estimated cascaded channels,a block coordinate descent-majorization minimization is adopted to investigate the downlink weighted sum rate(WSR)of all users in the RIS-aided MU MIMO systems.Simulation results show that the proposed method achieves higher WSR than the benchmark algorithms,and performs nearly the same as its upper bound in the high SNR case.Finally,a novel two-stage channel estimation method is developed with reduced pilot overhead and error propagation for a RIS-aided MU MISO systems.Specifically,in Stage I,by carefully designing the RIS phase shift training matrix and introducing matching matrices,all users jointly transmit the pilot signals for the estimation of the correlation factors between different paths of the common RIS-BS channel,which achieves significant MU diversity gain.Then,the inherent ambiguity of the structured cascaded channel is utilized to construct an ambiguous common RIS-BS channel composed of the estimated correlation factors.In Stage II,with the constructed ambiguous common RIS-BS channel,each user independently sends reduced pilots for estimating their specific user-RIS channel so as to obtain the entire cascaded channel.Simulation results demonstrate that the channel estimation accuracy of the proposed algorithm is improved with the increase of the number of users,and outperforms that of other existing channel estimation algorithms even with low average pilot overhead. |
