We consider the extra degree of freedom offered by the rotation of the reconfigurable intelligent surface (RIS) plane and investigate its potential in improving the performance of RIS-assisted wireless communication systems. By considering radiation pattern modeling at all involved nodes, we first derive the composite channel gain and present a closed-form upper bound for the system ergodic capacity over cascade Rician fading channels. Then, we reconstruct the composite channel gain by taking the rotations at the RIS plane, transmit antenna, and receive antenna into account, and extract the optimal rotation angles after investigating their impacts on the capacity. Moreover, we present a location-dependent expression of the ergodic capacity and investigate the RIS deployment strategy, i.e. the joint rotation adjustment and location selection. Finally, simulation results verify the accuracy of the theoretical analyses and deployment strategy. Although the RIS location has a big impact on the performance, our results showcase that the RIS rotation plays a more important role. In other words, we can obtain a considerable improvement by properly rotating the RIS rather than moving it over a wide area. For instance, we can achieve more than 200% performance improvement through rotating the RIS by 42.14°, while an 150% improvement is obtained by shifting the RIS over 400 meters.

In backscatter communication (BackCom) systems, the system performance is susceptible to the double fading channels, which is different from traditional communication systems. In this paper, we investigate the capacity performance of BackCom systems via multiple tags, where the tag corresponding to the maximum signal-to-noise ratio (SNR) is selected to combat the double fading. Although the exact ergodic capacity does not admit a closed-form expression, we derive its approximation by exploiting the extreme value theory, which shows that the capacity scales as loglog ${N}$ with ${N}$ denoting the number of tags. Simulation results confirm the tightness of the approximate ergodic capacity, and the correctness of the capacity scaling law.

The massive MIMO channel is characterized by non-stationarity and fast variation, thereby the channel state information obtained by traditional methods will be outdated and the system performance will be degraded. In this paper, we propose a channel prediction algorithm in massive MIMO environments. First, considering the channel characteristics, we propose a first-order Taylor expansion-based predictive channel modeling method. Then, a channel prediction algorithm consisting of the estimation stage and prediction stage is proposed and the interval of effective prediction (IEP) is derived. The performance of the proposed algorithm is testified by numerical simulations. It is shown that, within the IEP, a reliable channel prediction can be obtained with low computational complexity.

In this paper, we investigate and analyze a hybrid ambient backscatter (AmBack) scheme with harvest-then-transmit protocols. In this scheme, the whole transmission is divided into three phases (time slots): energy harvesting (EH), AmBack, and data transmission (DT). Depending on the type of the reflection coefficient (RC) for AmBack, two hybrid schemes are considered: variable RC (VRC) and fixed RC (FRC). In the VRC scheme, the RC changes with the channel state information, and the harvested energy in the first phase is totally used for DT. On the other hand, in the FRC scheme, the RC is fixed to be one, and the harvested energy in the first phase is in part used to cover the tag circuit operation in the second phase and in part for DT in the third phase. The resulting optimization problem is convex for the VRC scheme, but non-convex for the FRC scheme. However, by utilizing the maximum principle in convex optimization, we are able to derive closed-form expressions for optimal solutions for both schemes. In order to get more insights, we also analyze the upper bound performance of both schemes. Simulation results demonstrate that the AmBack/EH-AmBack scheme can always achieve the optimal performance.

This paper investigates the potential advantages of applying reconfigurable intelligent surface (RIS) in the wireless power transfer (WPT) system and designs an efficient RIS-aided WPT scheme. First, we propose an independent beamforming algorithm. Compared to the existing joint beamforming, the proposed scheme achieves the same performance but is practically much more convenient. Second, we propose an absorb-then-reflect power transfer scheme so that the RIS-aided WPT system can be self-sustainable without external energy supply. Third, we analyze the WPT efficiency of the proposed scheme and derive the minimum required scale of the RIS elements to guarantee a performance gain over the traditional WPT system. Next, we consider the multi-user scenario, and propose power allocation schemes to maximize the energy utility. Finally, the effectiveness of the proposed RIS-aided WPT scheme is verified by simulation results.

Accurate channel estimation is important for massive multiple-input multiple-output (mMIMO) to ensure good performance. Considering mMIMO application in 5G, frequency division duplexing (FDD) can provide higher data rate and wider coverage than the time division duplexing (TDD) mode. However, since the uplink/downlink channel is not straightforwardly reciprocal, FDD downlink channel estimation requires heavier training and computation than TDD mode due to the massive number of antennas. In addition, fast channel variation renders the real time estimation even more difficult. In this paper, we propose a downlink channel prediction scheme for FDD mMIMO, which requires only the TDD overhead. Specifically, the downlink channel matrix is represented by three components: steering matrix (frequency dependent), fading coefficients (time varying), and time delays (semi-static). By the proposed scheme, these three components can be obtained through uplink training. In addition, fast tracking and prediction is leveraged to obtain the real time channel state information. Simulation results show that accurate channel prediction is obtained via low cost and complexity by the proposed scheme.