The concept of a large intelligent surface (LIS) has recently emerged as a promising wireless communication paradigm that can exploit the entire surface of man-made structures for transmitting and receiving information. An LIS is expected to go beyond massive multiple-input multiple-output (MIMO) system, insofar as the desired channel can be modeled as a perfect line-of-sight. To understand the fundamental performance benefits, it is imperative to analyze its achievable data rate, under practical LIS environments and limitations. In this paper, an asymptotic analysis of the uplink data rate in an LIS-based large antenna-array system is presented. In particular, the asymptotic LIS rate is derived in a practical wireless environment where the estimated channel on LIS is subject to estimation errors, interference channels are spatially correlated Rician fading channels, and the LIS experiences hardware impairments. Moreover, the occurrence of the channel hardening effect is analyzed and the performance bound is asymptotically derived for the considered LIS system. The analytical asymptotic results are then shown to be in close agreement with the exact mutual information as the number of antennas and devices increase without bounds. Moreover, the derived ergodic rates show that hardware impairments, noise, and interference from estimation errors and the non-line-of-sight path become negligible as the number of antennas increases. Simulation results show that an LIS can achieve a performance that is comparable to conventional massive MIMO with improved reliability and a significantly reduced area for antenna deployment.

Large intelligent surfaces (LISs) have been recently proposed as an effective wireless communication solution that can leverage antenna arrays deployed on the entirety of man-made structures such as walls. An LIS can provide space-intensive and reliable communication, enabling the desired wireless channel to exhibit a perfect line-of-sight. However, the outage probability of LIS, which is an important performance metric to evaluate the system reliability, remains uncharacterized. In this paper, the distribution of uplink sum-rate is asymptotically analyzed for an LIS system. Given the derived asymptotic distribution, the outage probability is derived for the considered LIS system. Simulation results show that the results of the proposed asymptotic analyses are in close agreement to the exact mutual information in the presence of a large number of antennas and devices.

In this paper, we study the channel state information (CSI) feedback based on the deep autoencoder (AE) considering the feedback errors and feedback delay in the frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) system. We construct the deep AE by modeling the CSI feedback process, which involves feedback transmission errors and delays. The deep AE is trained by setting the delayed version of the downlink channel as the desired output. The proposed scheme reduces the impact of the feedback errors and feedback delay. Simulation results demonstrate that the proposed scheme achieves better performance than other comparable schemes.

Large intelligent surfaces (LISs) constitute a new and promising wireless communication paradigm that relies on the integration of a massive number of antenna elements over the entire surfaces of man-made structures. The LIS concept provides many advantages, such as the capability to provide reliable and space-intensive communications by effectively establishing line-of-sight (LOS) channels. In this paper, the system spectral efficiency (SSE) of an active LIS system is asymptotically analyzed under a practical LIS environment with a well-defined uplink frame structure. In order to verify the impact on the SSE of pilot contamination, the SSE of a multi-LIS system is asymptotically studied and a theoretical bound on its performance is derived. Given this performance bound, an optimal pilot training length for multi-LIS systems subjected to pilot contamination is characterized and, subsequently, the number of devices that need to be serviced by the LIS in order to maximize the performance is derived. Simulation results show that the derived analyses are in close agreement with the exact mutual information in presence of a large number of antennas, and the achievable SSE is limited by the effect of pilot contamination and intra/inter-LIS interference through the LOS path, even if the LIS is equipped with an infinite number of antennas. Additionally, the SSE obtained with the proposed pilot training length and number of scheduled devices is shown to reach the one obtained via a brute-force search for the optimal solution.

Increasing areal density in bit patterned media recording systems increases the 2-D interference from the adjacent islands in the down-track and cross-track directions. Also, nonlinear effects due to the track mis-registration (TMR) and media noise become severe as the areal density increases and degrade the bit error rate (BER) performance. In this paper, we proposed a new detection scheme based on multi-layer perceptrons (MLPs) to alleviate the nonlinear effects. Two MLPs are used in the proposed detection scheme and learn the channel with the nonlinear effects in the training procedure. One MLP is used for TMR estimation and the other MLP is used for data detection. The MLP-based TMR estimator estimates the TMR from the readback signals. Then, the MLP-based data detector equalizes the channel reflecting the estimated TMR and media noise and detects the data bit. Therefore, the detection scheme is robust to the effects of the TMR and media noise. The proposed detection scheme provides the improved BER performances compared to the conventional detection scheme using the 2-D equalizer filter, 1-D generalized partial response target, and Viterbi detector in the channel with the TMR and media noise.

To seamlessly deliver artificial intelligence (AI) services using object detection, both inference latency from a system perspective as well as inference accuracy should be considered important. Although edge computing can be applied to efficiently operate these AI services by significantly reducing inference latency, deriving an optimized computational offloading policy for edge computing is a challenging problem. In this paper, we propose inference latency prediction approaches for determining the optimal offloading policy in edge computing. Since there is no correlation between the image size and inference latency during object detection, approaches to predict inference latency are required for finding the optimal offloading policy. The proposed approaches predict the inference latency between devices and object detection algorithms by using their statistical information on the inference latency. By exploiting the predicted inference latency, a client may efficiently determine whether to execute an object detection task locally or remotely. Through various experiments, the performances of predicted inference latency according to the object detection algorithms are compared and analyzed by considering two communication protocols in terms of the root mean square error. The simulation results show that the predicted inference latency matches the actual inference latency well.