Communication channel

About: Communication channel is a research topic. Over the lifetime, 137411 publications have been published within this topic receiving 1715077 citations. The topic is also known as: communication channel & communications channel.

The Secrecy Capacity Region of the Gaussian MIMO Multi-receiver Wiretap Channel

Abstract: In this paper, we consider the Gaussian multiple-input multiple-output (MIMO) multi-receiver wiretap channel in which a transmitter wants to have confidential communication with an arbitrary number of users in the presence of an external eavesdropper. We derive the secrecy capacity region of this channel for the most general case. We first show that even for the single-input single-output (SISO) case, existing converse techniques for the Gaussian scalar broadcast channel cannot be extended to this secrecy context, to emphasize the need for a new proof technique. Our new proof technique makes use of the relationships between the minimum-mean-square-error and the mutual information, and equivalently, the relationships between the Fisher information and the differential entropy. Using the intuition gained from the converse proof of the SISO channel, we first prove the secrecy capacity region of the degraded MIMO channel, in which all receivers have the same number of antennas, and the noise covariance matrices can be arranged according to a positive semi-definite order. We then generalize this result to the aligned case, in which all receivers have the same number of antennas, however there is no order among the noise covariance matrices. We accomplish this task by using the channel enhancement technique. Finally, we find the secrecy capacity region of the general MIMO channel by using some limiting arguments on the secrecy capacity region of the aligned MIMO channel. We show that the capacity achieving coding scheme is a variant of dirty-paper coding with Gaussian signals.

Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces

Abstract: Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput. Most existing work assumes the ideal channel estimation, which can be challenging due to the high-dimensional cascaded MIMO channels and passive reflecting elements. Therefore, this paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead. Specifically, we first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. At the channel training stage, only a small proportion of elements will be successively activated to sound the partial channels. Moreover, the complete channel matrix can be reconstructed from the limited measurements based on compressive sensing, whereby the common sparsity of angular domain mmWave MIMO channels among different subcarriers is leveraged for improved accuracy. Besides, a complex-valued denoising convolution neural network (CV-DnCNN) is further proposed for enhanced performance. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art solutions.

Propagation Models and Performance Evaluation for 5G Millimeter-Wave Bands

Abstract: Fifth-generation (5G) wireless networks are expected to operate at both microwave and millimeter-wave (mmWave) frequency bands, including frequencies in the range of 24 to 86 GHz. Radio propagation models are used to help engineers design, deploy, and compare candidate wireless technologies, and have a profound impact on the decisions of almost every aspect of wireless communications. This paper provides a comprehensive overview of the channel models that will likely be used in the design of 5G radio systems. We start with a discussion on the framework of channel models, which consists of classical models of path loss versus distance, large-scale, and small-scale fading models, and multiple-input multiple-output channel models. Then, key differences between mmWave and microwave channel models are presented, and two popular mmWave channel models are discussed: the 3rd Generation Partnership Project model, which is adopted by the International Telecommunication Union, and the NYUSIM model, which was developed from several years of field measurements in New York City. Examples on how to apply the channel models are then given for several diverse applications demonstrating the wide impact of the models and their parameter values, where the performance comparisons of the channel models are done with promising hybrid beamforming approaches, including leveraging coordinated multipoint transmission. These results show that the answers to channel performance metrics, such as spectrum efficiency, coverage, hardware/signal processing requirements, etc., are extremely sensitive to the choice of channel models.

Radio Resource Allocation for OFDMA Systems in High Speed Environments

Abstract: In high speed train (HST) system, real-time multimedia entertainments are very important applications in which a data stream often contains packets with different quality of service requirements For example, video stream encoded with scalability contains the base layer packets with high quality (HQ) bit error rate (BER) requirement and enhancement layers' packets with low quality (LQ) BER requirement When a conventional allocation approach, which only considers one BER constraint for one data stream, is applied to orthogonal frequency division multiple access (OFDMA) systems, the BER constraint will be the strictest one among multiple requirements from different types of packets, which leads to inefficient allocation when each data stream has multiple BER requirements This paper aims to develop novel resource allocation approach by considering multiple BER requirements for different types of packets in one data stream In order to not only simplify the resource allocation, but also to compensate for the channel estimation error caused by Doppler shift in the HST environment, a proper number of contiguous subcarriers are grouped into chunks and spectrum is allocated chunk by chunk Simulation results show that the developed resource allocation scheme outperforms the conventional scheme, particularly when the BER ratio of HQ packets to LQ packets is larger than one Furthermore, in order to reduce the complexity of resource allocation further, an empirical allocation scheme is proposed to allocate better chunks to HQ packets It is shown that the performance of the empirical allocation scheme is quite close to that of the optimal scheme

Performance and Construction of Polar Codes on Symmetric Binary-Input Memoryless Channels

Abstract: Channel polarization is a method of constructing capacity achieving codes for symmetric binary-input discrete memoryless channels (B-DMCs) [1]. In the original paper, the construction complexity is exponential in the blocklength. In this paper, a new construction method for arbitrary symmetric binary memoryless channel (B-MC) with linear complexity in the blocklength is proposed. Furthermore, new upper and lower bounds of the block error probability of polar codes are derived for the BEC and the arbitrary symmetric B-MC, respectively.