Dr Marjan Abbasi Mosleh

Reconfigurable Intelligent Surface (RIS) technology is designed to improve channel propagation and, in turn, the performance of wireless communication systems. The extent of the improvement heavily relies on the phase shift optimization, a.k.a. passive beamforming design, at the RIS. This paper proposes novel approaches for simplifying this process in large RIS-aided multiple-input multiple-output (MIMO), a.k.a. MIMO-RIS, systems. More specifically, we derive two novel simplified closed-form approximations (CFAs) of the MIMO-RIS capacity expression in the large number of elements/antennas asymptotic regime and identify various scenarios for which they tightly approximate the classic MIMO-RIS capacity expression. We then design two novel simplified RIS phase optimization algorithms for lossless and practical metasurfaces with reduced computational complexity. We also demonstrate that, in certain practical scenarios, very simple guidelines can be used for setting the phases of MIMO-RIS systems. Numerical evaluations validate the accuracy of our CFAs and simplified RIS phase-shift optimization algorithms. They also show that our simplified approaches can deliver either similar or even better capacity performance than existing methods, especially for practical metasurfaces, in various scenarios.

In the next generation of communication networks (i.e. 6G), metamaterial-based antenna designs, such as Reconfigurable Intelligent Surface (RIS), will be critical for improving wireless communication systems. This paper investigates the ergodic capacity of RIS-aided multiple input multiple outputs (MIMO) systems in the presence of a direct link between the transmitter and receiver. We obtain an exact expression for the ergodic capacity of the cooperative MIMO-RIS systems (along with the corresponding probability density function of the cooperative MIMO-RIS channel) assuming that the receiver is capable of treating the RIS and direct link contributions in the received signal separately. Furthermore, we demonstrate that in the absence of this capability, the resulting formula is a tight upper bound that becomes increasingly tighter with greater numbers of RIS elements. In addition, we pose a simplified capacity expression for large numbers of RIS elements, which provides further insights into the behaviour of the cooperative MIMO-RIS capacity. To gain more insights, we also include a high SNR approximation. Our simulation results confirm the correctness of our expressions and illustrate how the SNR and the number of RIS elements impact the ergodic capacity.

Meta-material-based antenna designs, such as large intelligent surface (LIS), are expected to be a game changer in future wireless cellular systems, since they provide a simple yet effective mean of drastically improving the wireless propagation environment. This paper investigates the ergodic capacity of LIS-aided multiple input multiple output (MIMO), a.k.a. MIMO-LIS, systems. To this end, the derivation of the probability density function (pdf) of the cascaded channel, i.e. the transmitter-to-LIS-to-receiver channel, is studied. Moreover, both high signal-to-noise ratio (SNR) asymptotic expression and closed-form approximations of this ergodic capacity are provided. Monte-Carlo simulations graphically validate the correctness and accuracy of our various expressions, for different antenna configurations. Furthermore, our performance analysis shows that the MIMO-LIS system outperforms both MIMO-AF and MIMO systems (by more than 60% and 15% respectively, at a 30 dB SNR) from an ergodic capacity point of view, which confirms that LIS can be beneficial for improving the propagation environment.

Energy efficiency (EE) is one of the main design criterion for the current and next generation of communication systems. Whereas, reflective intelligent surface (RIS) is foreseen to be a key enabler of the next generation of communication systems by facilitating the propagation of radio frequency signals, and in turn, possibly improving its spectral efficiency (SE) and/or EE. This paper investigates both the EE and SE of a multi-hop multi-antenna RIS-aided communication system through its fundamental trade-off. To this end, we first provide a generic and accurate closed-form approximation (CFA) of the SE (ergodic capacity) for multi-hop multi-antenna RIS-aided systems and verify its accuracy through simulations for various numbers of antennas/phase shifters and hops. Based on this expression, we then derive a novel and accurate CFA of the fundamental EE-SE trade-off for multi-hop multi-antenna RIS-aided systems. We subsequently use our CFA to analyse the variations of the EE as a function of the number of antennas/phase shifters and hops when considering a realistic power consumption model. It turns out that increasing the number of hops is more energy efficient than increasing the number of antennas/phase shifters and that multi-hop communication with RIS is not necessarily always more energy efficient than classic multi-antenna communication, as it is for instance the case in a simple device-to-device communication scenario.

Metamaterial-based antenna designs, such as Reconfigurable Intelligent Surface (RIS), are expected to play a significant role in next generation communication networks (i.e. 6G) because of their ability to improve wireless communication environments. This letter investigates the ergodic capacity of RIS-aided multiple input multiple output (MIMO), a.k.a. MIMO-RIS, systems over Rayleigh-Rician fading channels. We consider that the transmitter-RIS and receiver-RIS links experience Rayleigh and Rician fading, respectively. An exact analytical expression of the ergodic capacity is derived based on closed-form expressions of the probability density function (pdf) of the cascaded channel. Moreover, a high SNR expression and a large RIS approximation are provided to unveil further system insights. Simulations result validate the correctness of our expressions and show the impact of the Rician fading and the number of RIS elements on the capacity.