<p>In intelligent reflecting surface (IRS)-assisted communications, the ultimate gain is achieved when the phases of the reflected signals are optimally selected to maximize the signal-to-noise ratio (SNR). However, practical hurdles, particularly the imperfect phase estimation and quantization can reduce the potential gain. Therefore, this work aims at evaluating the impact of applying a quantized phase in the presence of phase estimation errors. Towards this goal, we derive the probability density function (PDF) of the estimated quantized phase, then using the sinusoidal addition theorem (SAT), the PDF of the received signal envelope is derived and used to derive closed-form expressions of the symbol error rate (SER) and outage probability (OP). The obtained analytical and simulation results show that the SER and OP jointly depend on the SNR, phase estimation accuracy, number of IRS elements, and number of quantization levels. The imperfect phase and quantization demonstrated several counterintuitive results. In particular, it is shown that increasing the number of IRS elements or the number of quantization levels may degrade the system performance. Moreover, the results reveal that the impact of phase quantization increases as the phase estimation accuracy decreases. The results also show that the performance is susceptible to phase errors with an even number of reflectors and binary quantization levels.</p>
<p>In intelligent reflecting surface (IRS)-assisted communications, the ultimate gain is achieved when the phases of the reflected signals are optimally selected to maximize the signal-to-noise ratio (SNR). However, practical hurdles, particularly the imperfect phase estimation and quantization can reduce the potential gain. Therefore, this work aims at evaluating the impact of applying a quantized phase in the presence of phase estimation errors. Towards this goal, we derive the probability density function (PDF) of the estimated quantized phase, then using the sinusoidal addition theorem (SAT), the PDF of the received signal envelope is derived and used to derive closed-form expressions of the symbol error rate (SER) and outage probability (OP). The obtained analytical and simulation results show that the SER and OP jointly depend on the SNR, phase estimation accuracy, number of IRS elements, and number of quantization levels. The imperfect phase and quantization demonstrated several counterintuitive results. In particular, it is shown that increasing the number of IRS elements or the number of quantization levels may degrade the system performance. Moreover, the results reveal that the impact of phase quantization increases as the phase estimation accuracy decreases. The results also show that the performance is susceptible to phase errors with an even number of reflectors and binary quantization levels.</p>
<p> This paper introduces a new analytical framework to evaluate the capacity of intelligent reconfigurable surface (IRS)-aided wireless networks in the presence of a direct link (DL). The obtained analysis is used to characterize the signal-to-noise ratio (SNR) at the user equipment (UE) while using adaptive power and rate transmission. In particular, we consider the channel inversion with a fixed rate, optimum power and rate adaptation, and the truncated channel inversion with a fixed rate. The obtained expressions are derived in a unified closed-form. All the single-hop channel gains are modeled as independent and identically distributed Nakagami-m fading channels. Consequently, the channels’ gains at the receiver become independent and non-identically distributed. The moment generating function (MGF) is used to derive an accurate approximation of the probability density and cumulative distribution functions of the instantaneous SNR, which are used to evaluate the channel capacity at low and high SNRs to quantify the achievable multiplexing gain. The obtained analytical and simulation results indicated that a strong DL may significantly enhance the channel capacity gain obtained using the IRS. In particular scenarios, the capacity improved by about 30% for a large number of IRS elements when the DL Nakagami fading parameter m is increased from 2 to 6. </p>
<p> This paper introduces a new analytical framework to evaluate the capacity of intelligent reconfigurable surface (IRS)-aided wireless networks in the presence of a direct link (DL). The obtained analysis is used to characterize the signal-to-noise ratio (SNR) at the user equipment (UE) while using adaptive power and rate transmission. In particular, we consider the channel inversion with a fixed rate, optimum power and rate adaptation, and the truncated channel inversion with a fixed rate. The obtained expressions are derived in a unified closed-form. All the single-hop channel gains are modeled as independent and identically distributed Nakagami-m fading channels. Consequently, the channels’ gains at the receiver become independent and non-identically distributed. The moment generating function (MGF) is used to derive an accurate approximation of the probability density and cumulative distribution functions of the instantaneous SNR, which are used to evaluate the channel capacity at low and high SNRs to quantify the achievable multiplexing gain. The obtained analytical and simulation results indicated that a strong DL may significantly enhance the channel capacity gain obtained using the IRS. In particular scenarios, the capacity improved by about 30% for a large number of IRS elements when the DL Nakagami fading parameter m is increased from 2 to 6. </p>
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