Autoencoder-Bank Based Design for Adaptive Channel-Blind Robust Transmission

Abstract The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there is an explicit channel model for training that matches the actual channel model in the online transmission. Since the actual channel varies over time, this imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. More precisely, we divide the interval of random channel coefficients into n sub-intervals. Subsequently, in the offline training phase, we employ an AE bank consisting of n pairs of encoder and decoder and perform training over the sub-intervals. Then, in the online transmission phase, based on the actual channel conditions, the optimal pair of encoder and decoder is selected for data transmission in terms of satisfying an average block error rate (BLER) constraint imposed on the system. To monitor actual channel conditions for adopting the adaptive scheme, we assume a realistic scenario where the instantaneous channel gain is not known to Tx/Rx and it is blindly estimated at the RX, i.e., without using any pilot symbols. Our simulation results confirms the superiority of the proposed adaptive scheme over a non-adaptive scenario in terms of average power consumption. For instance, when the target average BLER is equal to 10−4 , our proposed algorithm with n = 5 can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme

AbstractThe idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to $$10^{-4}$$ 10 - 4 , our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.

In order to solve the problems of high average power consumption, low average throughput, high average energy consumption per unit of data, and short network life cycle in traditional multisource wireless cooperation methods, this paper proposes a multisource wireless cooperative network design method based on multiple goals. We analyze the characteristics of heterogeneous deployment of multisource wireless cooperative networks and the energy consumption of nodes and control the energy consumption of network data transmission through distributed opportunistic transmission scheduling methods according to the analysis results. We use the optimal strategy of minimizing expected energy consumption, transform the problem of data transmission energy consumption, establish a mathematical model, and obtain the optimal solution for minimizing expected energy consumption. According to the optimal stop rule, the minimum expected energy consumption threshold is obtained, and then the optimal solution is obtained on the constraint set of the multiobjective optimization problem through the multiobjective optimization method, so as to achieve the goal of minimizing the expected energy of the network. Experimental results show that this method prolongs the network life cycle, reduces the average power consumption of network data transmission, and improves the average network throughput.

This paper investigates the problem of data transmission for the joint radar and communication systems (JRCSs). The performance of the JRCS is characterized by data throughput related to the radar echo data (RED) and communication data rate (CDR). Two spectral coexistence schemes are proposed based on the degree of spectrum sharing for radar and communication, i.e., the isolated subfrequency band (ISFB) and mix-used frequency band (MUFB) schemes. Firstly, the signals of radar and communication are operated on the isolated subcarriers, enabling the received signals to be processed independently and bringing the advantage of interference avoidance. Secondly, the signals of radar and communication can be jointly operated on the same subcarriers for the MUFB scheme, which enhances the spectrum efficiency. Unlike the ISFB scheme, the CDR of the MUFB scheme is maximized along with the interference from the radar signal, and meanwhile, the allocated radar power on each subcarrier is derived by maximizing the radar mutual information. Numerical results show that the MUFB scheme significantly improves the performance of data transmission over the ISFB scheme, and a significant performance gain in the data transmission can be achieved, compared to the average power allocation case.

To study this issue, a new structure for UWB communication systems based on compressed sensing (CS) is proposed. This proposals focus on solving two main problems. First, building a system based on the CS to reduce the sampling rate at the receiver. Second, analyzing the impact of inter multi-pulses interference (IMI) to the ability to transmit high data rate of UWB communication systems. Experimental results show that, the IMI will be changed greatly depending on the different types of channel model. In the LOS channel model, the effects of the IMI negligible thus data transmission rate can be achieved up to several hundred Mbps. whereas for the NLOS channel models, the effects of the IMI large so data transmission rate can only reach a few Mbps.

Sensors enabled Internet of things (IoT) has become an integral part of the modern, digital and connected ecosystem. Narrowband IoT (NB-IoT) technology is one of its economical versions preferable when low power and resource limited sensors based applications are considered. One of the major characteristics of NB-IoT technology is its offer of reliable coverage enhancement (CE) which is achieved by repeating the transmission of signals. This repeated transmission of the same signal challenges power saving in low complexity NB-IoT devices. Additionally, the NB-IoT devices are expected to suffer from congestion due to simultaneous random access procedures (RAPs) from an enormous number of devices. Multiple RAP reattempts would further reduce the power saving in NB-IoT devices. We propose a novel power efficient RAP (PE-RAP) for reducing power consumption of NB-IoT devices in a highly congested environment. The existing RAP do not differentiate the failures due to poor channel conditions or due to collision. After the RAP failure either due to collision or poor channel, the devices can apply power ramping or can transit to a higher CE level with higher repetition configuration. In the proposed PE-RAP, the NB-IoT devices can re-ascertain the channel conditions after an RAP attempt failure such that the impediments due to poor channel are reduced. The power increments and repetition enhancements are applied only when necessary. We probabilistically obtain the chances of RAP reattempts. Subsequently, we evaluate the average power consumption by devices in different CE levels for different repetition configurations. We validate our analysis by simulation studies.

Orthogonal frequency division multiplexing (OFDM) is a multicarrier transmission system that can achieve high data rate over wireless channels. At the same time, multiple input multiple output OFDM (MIMO-OFDM) in wireless communication systems has been exposed to offer significant improvement over wireless technology by providing transmit diversity. It has become a promising technique for high-performance 5G broadband wireless communications. However, the main problem associated with MIMO-OFDM is that its signal exhibits high peak-to-average power ratio (PAPR), which causes nonlinear distortion and consequently performance degradation. Besides, PAPR carries weaknesses such as an increase in power consumption of high power amplifier (HPA) and analog to digital converter (ADC). Thus, 5G base stations will push up power requirements because energy consumption grows with the number of transceiver elements. So, mobile operators must find the right compromise that, on the one hand, guarantees a certain level of performance to a data flow, and, on the other hand, the energy cost generated for the deployment of the network. For this, as part of the management of power consumption, we propose MIMO constant envelope OFDM (MIMO-CE-OFDM) technique. In this work, we used MIMO-CE-OFDM to mitigate the nonlinear effect of HPA and ADC. To perform practical simulations, we have used COST 2100 MIMO channel model. In this paper, a MIMO-CE-OFDM system has been presented and analyzed under COST 2100 channel model conditions. Simulation results are given to illustrate the performance of [Formula: see text] MIMO-CE-OFDM in the presence of both HPA and ADC nonlinearity. This work shows that the effect of nonlinearity is shown to be negligible on MIMO-CE-OFDM signal.

The conventional shift register consists of master and slave (MS) latches with each latch receiving the data from the previous stage. Therefore, the same data are stored in two latches separately. It leads to consuming more electrical power and occupying more layout area, which is not satisfactory to most circuit designers. To solve this issue, a novel cross-latch shift register (CLSR) scheme is proposed. It significantly reduced the number of transistors needed for a 256-bit shifter register by 48.33% as compared with the conventional MS latch design. To further verify its functions, this CLSR was implemented by using TSMC 40 nm CMOS process standard technology. The simulation results reveal that the proposed CLSR reduced the average power consumption by 36%, cut the leakage power by 60.53%, and eliminated layout area by 34.76% at a supply voltage of 0.9 V with an operating frequency of 250 MHz, as compared with the MS latch.