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Register a new userLarge System Achievable Rate Analysis of RIS-Assisted MIMO Wireless Communication with Statistical CSIT
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Added by
Jun Zhang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Reconfigurable intelligent surface (RIS) is an emerging technology to enhance wireless communication in terms of energy cost and system performance by equipping a considerable quantity of nearly passive reflecting elements. This study focuses on a downlink RIS-assisted multiple-input multiple-output (MIMO) wireless communication system that comprises three communication links of Rician channel, including base station (BS) to RIS, RIS to user, and BS to user. The objective is to design an optimal transmit covariance matrix at BS and diagonal phase-shifting matrix at RIS to maximize the achievable ergodic rate by exploiting the statistical channel state information at BS. Therefore, a large-system approximation of the achievable ergodic rate is derived using the replica method in large dimension random matrix theory. This large-system approximation enables the identification of asymptotic-optimal transmit covariance and diagonal phase-shifting matrices using an alternating optimization algorithm. Simulation results show that the large-system results are consistent with the achievable ergodic rate calculated by Monte Carlo averaging. The results verify that the proposed algorithm can significantly enhance the RIS-assisted MIMO system performance.
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Training-based transmission over Rayleigh block-fading multiple-input multiple-output (MIMO) channels is investigated. As a training method a combination of a pilot-assisted scheme and a biased signaling scheme is considered. The achievable rates of successive decoding (SD) receivers based on the linear minimum mean-squared error (LMMSE) channel estimation are analyzed in the large-system limit, by using the replica method under the assumption of replica symmetry. It is shown that negligible pilot information is best in terms of the achievable rates of the SD receivers in the large-system limit. The obtained analytical formulas of the achievable rates can improve the existing lower bound on the capacity of the MIMO channel with no channel state information (CSI), derived by Hassibi and Hochwald, for all signal-to-noise ratios (SNRs). The comparison between the obtained bound and a high SNR approximation of the channel capacity, derived by Zheng and Tse, implies that the high SNR approximation is unreliable unless quite high SNR is considered. Energy efficiency in the low SNR regime is also investigated in terms of the power per information bit required for reliable communication. The required minimum power is shown to be achieved at a positive rate for the SD receiver with no CSI, whereas it is achieved in the zero-rate limit for the case of perfect CSI available at the receiver. Moreover, numerical simulations imply that the presented large-system analysis can provide a good approximation for not so large systems. The results in this paper imply that SD schemes can provide a significant performance gain in the low-to-moderate SNR regimes, compared to conventional receivers based on one-shot channel estimation.
Reconfigurable intelligent surface (RIS) assisted radio is considered as an enabling technology with great potential for the sixth-generation (6G) wireless communications standard. The achievable secrecy rate (ASR) is one of the most fundamental metrics to evaluate the capability of facilitating secure communication for RIS-assisted systems. However, the definition of ASR is based on Shannons information theory, which generally requires long codewords and thus fails to quantify the secrecy of emerging delay-critical services. Motivated by this, in this paper we investigate the problem of maximizing the secrecy rate under a delay-limited quality-of-service (QoS) constraint, termed as the effective secrecy rate (ESR), for an RIS-assisted multiple-input single-output (MISO) wiretap channel subject to a transmit power constraint. We propose an iterative method to find a stationary solution to the formulated non-convex optimization problem using a block coordinate ascent method (BCAM), where both the beamforming vector at the transmitter as well as the phase shifts at the RIS are obtained in closed forms in each iteration. We also present a convergence proof, an efficient implementation, and the associated complexity analysis for the proposed method. Our numerical results demonstrate that the proposed optimization algorithm converges significantly faster that an existing solution. The simulation results also confirm that the secrecy rate performance of the system with stringent delay requirements reduce significantly compared to the system without any delay constraints, and that this reduction can be significantly mitigated by an appropriately placed large-size RIS.
We consider an echo-assisted communication model wherein block-coded messages, when transmitted across several frames, reach the destination as multiple noisy copies. We address adversarial attacks on such models wherein a subset of the noisy copies are vulnerable to manipulation by an adversary. Particularly, we study a non-persistent attack model with the adversary attacking 50% of the frames on the vulnerable copies in an i.i.d. fashion. We show that this adversarial model drives the destination to detect the attack locally within every frame, thereby resulting in degraded performance due to false-positives and miss-detection. Our main objective is to characterize the mutual information of this adversarial echo-assisted channel by incorporating the performance of attack-detection strategies. With the use of an imperfect detector, we show that the compound channel comprising the adversarial echo-assisted channel and the attack detector exhibits memory-property, and as a result, obtaining closed-form expressions on mutual information is intractable. To circumvent this problem, we present a new framework to approximate the mutual information by deriving sufficient conditions on the channel parameters and also the performance of the attack detectors. Finally, we propose two attack-detectors, which are inspired by traditional as well as neural-network ideas, and show that the mutual information offered by these detectors is close to that of the Genie detector for short frame-lengths.
Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme in 5G systems for the simultaneous access of tremendous number of wireless nodes. On the other hand, RF-enabled wireless energy harvesting is a promising technology for self-sustainable wireless nodes. In this paper, we consider a NOMA system where the near user harvests energy from the strong radio signal to power-on the information decoder. A generalized energy harvesting scheme is proposed by combining the conventional time switching and power splitting scheme. The achievable rate region of the proposed scheme is characterized under both constant and dynamic decoding power consumption models. If the decoding power is constant, the achievable rate region can be found by solving two convex optimization subproblems, and the regions for two special cases: time switching and power splitting, are characterized in closed-form. If the decoding power is proportional to data rate, the achievable rate region can be found by exhaustive search algorithm. Numerical results show that the achievable rate region of the proposed generalized scheme is larger than those of time switching scheme and power splitting scheme, and rate-dependent decoder design helps to enlarge the achievable rate region.
Intelligent reflecting surface (IRS) is a promising technology for wireless communications, thanks to its potential capability to engineer the radio environment. However, in practice, such an envisaged benefit is attainable only when the passive IRS is of a sufficiently large size, for which the conventional uniform plane wave (UPW)-based channel model may become inaccurate. In this paper, we pursue a new channel modelling and performance analysis for wireless communications with extremely large-scale IRS (XL-IRS). By taking into account the variations in signals amplitude and projected aperture across different reflecting elements, we derive both lower- and upper-bounds of the received signal-to-noise ratio (SNR) for the general uniform planar array (UPA)-based XL-IRS. Our results reveal that, instead of scaling quadratically with the increased number of reflecting elements M as in the conventional UPW model, the SNR under the more practically applicable non-UPW model increases with M only with a diminishing return and gets saturated eventually. To gain more insights, we further study the special case of uniform linear array (ULA)-based XL-IRS, for which a closed-form SNR expression in terms of the IRS size and transmitter/receiver location is derived. This result shows that the SNR mainly depends on the two geometric angles formed by the transmitter/receiver locations with the IRS, as well as the boundary points of the IRS. Numerical results validate our analysis and demonstrate the importance of proper channel modelling for wireless communications aided by XL-IRS.
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