Yu-Han Yang

Green Wireless Communications: A Time-Reversal Paradigm

Green wireless communications have received considerable attention recently in hope of finding novel solutions to improve energy efficiency for the ubiquity of wireless applications. In this paper, we argue and show that the time-reversal (TR) signal transmission is an ideal paradigm for green wireless communications because of its inherent nature to fully harvest energy from the surrounding environment by exploiting the multi-path propagation to re-collect all the signal energy that would have otherwise been lost in most existing communication paradigms. A green wireless technology must ensure low energy consumption and low radio pollution to others than the intended user. In this paper, we show through theoretical analysis, numerical simulations and experiment measurements that the TR wireless communications, compared to the conventional direct transmission using a Rake receiver, reveals significant transmission power reduction, achieves high interference alleviation ratio, and exhibits large multi-path diversity gain. As such it is an ideal paradigm for the development of green wireless systems. The theoretical analysis and numerical simulations show an order of magnitude improvement in terms of transmit power reduction and interference alleviation. Experimental measurements in a typical indoor environment also demonstrate that the transmit power with TR based transmission can be as low as 20% of that without TR, and the average radio interference (thus radio pollution) even in a nearby area can be up to 6dB lower. A strong time correlation is found to be maintained in the multi-path channel even when the environment is varying, which indicates high bandwidth efficiency can be achieved in TR radio communications.

Time-Reversal Wireless Paradigm for Green Internet of Things: An Overview

In this paper, we present an overview of the time-reversal (TR) wireless paradigm for green Internet of Things (IoT). It is shown that the TR technique is a promising technique that focuses signal waves in both time and space domains. The unique asymmetric architecture significantly reduces the cost of the terminal devices, the total number of which is expected to be very large for IoT. The focusing effect of the TR technique can harvest the energy of all the multi-paths at the receiver, which improves the energy efficiency of the wireless transmission and thus the battery life of terminal devices in IoT. Facilitated by the high-resolution spatial focusing, the TR division multiple access scheme leverages the uniqueness of the multi-path profiles in the rich-scattering environment and maps them into location-specific signatures, so that spatial multiplexing can be achieved for multiple users operating on the same spectrum. In addition, the TR system can easily support heterogeneous terminal devices by providing various quality-of-service (QoS) options through adjusting the waveform and rate backoff factor. Finally, the unique location-specific signature in TR system can provide additional physical-layer security and thus can enhance the privacy and security of customers in IoT. All the advantages show that the TR technique is a promising paradigm for IoT.

Time-Reversal Division Multiple Access over Multi-Path Channels

The multi-path effect makes high speed broadband communications a very challenging task due to the severe inter-symbol interference (ISI). By concentrating energy in both the spatial and temporal domains, time-reversal (TR) transmission technique provides a great potential of low-complexity energy-efficient communications. In this paper, a novel concept of time-reversal division multiple access (TRDMA) is proposed as a wireless channel access method based on its high-resolution spatial focusing effect. It is proposed to use TR structure in multi-user downlink systems over multi-path channels, where signals of different users are separated solely by TRDMA. Both the single-transmit-antenna scheme and its enhanced version with multiple transmit antennas are developed and evaluated in this paper. The system performance is investigated in terms of its effective signal-to-interference-plus-noise ratio (SINR), the achievable sum rate and the achievable rates with outage. And some further discussions regarding its advantage over conventional rake receivers and the impact of spatial correlations between users are given at the end of this paper. It is shown in both analytical and simulation results that desirable properties and satisfying performances can be achieved in the proposed TRDMA multi-user downlink system, which makes TRDMA a promising candidate for future energy-efficient low-complexity broadband wireless communications.

Time-Reversal Wideband Communications

With the advance of semiconductor technologies, the performance of analog-to-digital converter (ADC) has been improved a lot during the past decade in terms of both sampling rate and resolution. The cost has gone down dramatically that makes the wideband communication much affordable. Under such circumstances, a natural question to ask is: Is there a low complexity high energy-efficient solution to high throughput wideband communications? In this letter, we explore this question by studying time-reversal communications. We find that compared with that of OFDM system, the computational complexity of a time-reversal system at the transmitter side is lower since it requires similar additions but no multiplications. Furthermore, the computational complexity of a time-reversal system at the receiver side is negligible since only onetap detection is performed, which means that the overall computational complexity of time-reversal system is much lower than that of OFDM system. Moreover, we find that when the bandwidth is large enough, time-reversal system can achieve much higher achievable rate than OFDM system. Therefore, time-reversal technique is a desired solution to low complexity high throughput wideband communications when more bandwidth is available.

A cheat-proof game theoretic demand response scheme for smart grids

While demand response has achieved promising results on making the power grid more efficient and reliable, the additional dynamics and flexibility brought by demand response also increase the uncertainty and complexity of the centralized load forecast. In this paper, we propose a game theoretic demand response scheme that can transform the traditional centralized load prediction structure into a distributed load prediction system by the participation of customers. Moreover, since customers are generally rational and thus naturally selfish, they may cheat if cheating can improve their payoff. Therefore, enforcing truth-telling is crucial. We prove analytically and demonstrate with simulations that the proposed game theoretic scheme is cheat-proof, i.e., all customers are motivated to report and consume their true optimal demands and any deviation will lead to a utility loss. We also prove theoretically that the proposed demand response scheme can lead to the solution that maximizes social welfare and is proportionally fair in terms of utility function. Moreover, we propose a simple dynamic pricing algorithm for the power substation to control the total demand of all customers to meet the target demand curve. Finally, simulations are shown to demonstrate the efficiency and effectiveness of the proposed game theoretic algorithm.

Anti-Eavesdropping Space-Time Network Coding for Cooperative Communications

Physical (PHY) layer security has recently become a hot issue in wireless communication. In this paper, an approach to a generalized anti-eavesdropping space-time network coding (GAE-STNC) for cooperative communications is proposed to achieve the physical layer security and overcome the problem of imperfect synchronization while still guaranteeing full diversity. Based on the assumption of channel reciprocity, the basic idea is to exploit the channel state information (CSI) between the legitimate transmitters and the receiver, which is used to generate the secret key. With this secret key, the transmitters introduce pseudo random interferences by adding an anti-eavesdropping matrix to the initial system. Since the eavesdroppers channel is typically independent of the legitimate channel, the channel between the legitimate transmitters-receiver pair, the signal received by the eavesdropper is interfered. While the receiver can effectively decode the signal by utilizing the global CSI, the eavesdroppers can not decode the received signal correctly. Then, two specific schemes derived from the GAE-STNC are proposed. Numerical analysis and simulation results are presented to illustrate the proposed GAE-STNC schemes.

Time-Reversal Division Multiple Access in Multi-Path Channels

Multi-path effect makes high speed broadband communications a very challenging task due to the severe inter-symbol interference (ISI). By concentrating energy in both spatial and temporal domains, time-reversal (TR) transmission technique provides a great potential of low-complexity energy-efficient communications. In this paper, we develop a new concept of time-reversal division multiple access (TRDMA) as a wireless channel access method based on its high-resolution spatial focusing effect. We propose to use TR structure in a multi-user downlink system over large delay spread channels, where the signals of different users are separated solely by TRDMA. Both the single-transmit-antenna scheme and its enhanced version with multiple-transmit-antenna are developed and evaluated in this paper. We investigate the system performance in terms of the effective SINR and the achievable sum rate of the multi-user system. It is shown in both analytical and simulation results that satisfying performances can be achieved in the proposed TRDMA multi-user downlink system.

Distributed State Estimation With Dimension Reduction Preprocessing

System state estimation relies heavily on the measurements. With the advance of sensing technology, the ability to measure is no longer a bottleneck in many systems, and more and more researchers now focus on the rich-information setting, i.e., big data. However, although information never hurts, it does not help unconditionally. How to make the most of it depends on whether we can process the data efficiently. In some systems, the inherent constraint such as the bandwidth and cost makes it necessary to reduce the dimension of the measurement before further processing. The problem that the raw measurements are first preprocessed to reduce size and then used for estimation is addressed in this paper. It is shown that there is a lower bound on the size of the preprocessed data such that if the size is beyond the bound, there exists a closed-form estimator design that the linear minimum mean-square estimation can be obtained. Moreover, we propose an algorithm that is guaranteed to converge to a stationary point to design an estimator in the conditions that the lower bound cannot be reached. Besides convergence, the proposed algorithm guarantees bounded performance loss compared with the global optimal solution under some additional conditions. Finally, simulation results in three different applications are shown to demonstrate the effectiveness of the proposed algorithm.

Waveform Design for Sum Rate Optimization in Time-Reversal Multiuser Downlink Systems

Utilizing channel reciprocity, the traditional time-reversal technique boosts the signal-to-noise ratio at the receiver with very low transmitter complexity. However, the large delay spread gives rise to severe inter-symbol interference (ISI) when the data rate is high, and the achievable transmission rate is further degraded in the multiuser downlink due to the inter-user interference (IUI). In this work, we study the weighted sum rate optimization problem by means of waveform design in the time-reversal multiuser downlink where the receiver processing is based on a single sample. Power allocation has a significant impact on the waveform design problem. We propose a new power allocation algorithm named Iterative SINR Waterfilling, which is able to achieve comparable sum rate performance to that of globally optimal power allocation. We further propose another approach called Iterative Power Waterfilling for multiple data streams. Iterative SINR Waterfilling provides better performance than Iterative Power Waterfilling in the scenario of high interference, while Iterative Power Waterfilling can work under multiple data streams. Simulation results show the superior performance of the proposed algorithms in comparison with other waveform designs such as zero-forcing and conventional time-reversal waveform.

Incentive compatible demand response games for distributed load prediction in smart grids

While demand response has achieved promising results on making the power grid more efficient and reliable, the additional dynamics and flexibility brought by demand response also increase the uncertainty and complexity of the centralized load forecast. In this paper, we propose a game-theoretic demand response scheme that can transform the traditional centralized load prediction structure into a distributed load prediction system by the participation of customers. Moreover, since customers are generally rational and thus naturally selfish, they may cheat if cheating can improve their payoff. Therefore, enforcing truthtelling is crucial. We prove analytically and demonstrate with simulations that the proposed game-theoretic scheme is incentive compatible, i.e., all customers are motivated to report and consume their true optimal demands and any deviation will lead to a utility loss. We also prove theoretically that the proposed demand response scheme can lead to the solution that maximizes social welfare and is proportionally fair in terms of utility function. Moreover, we propose a simple dynamic pricing algorithm for the power substation to control the total demand of all customers to meet the target demand curve. Finally, simulations are shown to demonstrate the efficiency and effectiveness of the proposed game-theoretic algorithm.