Meng-Lin Ku

Advances in Energy Harvesting Communications: Past, Present, and Future Challenges

Recent emphasis on green communications has generated great interest in the investigations of energy harvesting communications and networking. Energy harvesting from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many attractive benefits to the environment and deployment. However, unlike the conventional stable energy, the intermittent and random nature of the renewable energy makes it challenging in the realization of energy harvesting transmission schemes. Extensive research studies have been carried out in recent years to address this inherent challenge from several aspects: energy sources and models, energy harvesting and usage protocols, energy scheduling and optimization, implementation of energy harvesting in cooperative, cognitive radio, multiuser and cellular networks, etc. However, there has not been a comprehensive survey to lay out the complete picture of recent advances and future directions. To fill such a gap, in this paper, we present an overview of the past and recent developments in these areas and highlight a number of possible future research avenues.

Data-Driven Stochastic Models and Policies for Energy Harvesting Sensor Communications

Energy harvesting from the surroundings is a promising solution to perpetually power-up wireless sensor communications. This paper presents a data-driven approach of finding optimal transmission policies for a solar-powered sensor node that attempts to maximize net bit rates by adapting its transmission parameters, power levels and modulation types, to the changes of channel fading and battery recharge. We formulate this problem as a discounted Markov decision process (MDP) framework, whereby the energy harvesting process is stochastically quantized into several representative solar states with distinct energy arrivals and is totally driven by historical data records at a sensor node. With the observed solar irradiance at each time epoch, a mixed strategy is developed to compute the belief information of the underlying solar states for the choice of transmission parameters. In addition, a theoretical analysis is conducted for a simple on-off policy, in which a predetermined transmission parameter is utilized whenever a sensor node is active. We prove that such an optimal policy has a threshold structure with respect to battery states and evaluate the performance of an energy harvesting node by analyzing the expected net bit rate. The design framework is exemplified with real solar data records, and the results are useful in characterizing the interplay that occurs between energy harvesting and expenditure under various system configurations. Computer simulations show that the proposed policies significantly outperform other schemes with or without the knowledge of short-term energy harvesting and channel fading patterns.

EM-Based Iterative Receivers for OFDM and BICM/OFDM Systems in Doubly Selective Channels

In this paper, we resort to the expectation-maximization (EM) algorithm to tackle the inter-carrier interference (ICI) problem, caused by time-variant multipath channels, for both orthogonal frequency division multiplexing (OFDM) systems and bit-interleaved coded modulation (BICM)/OFDM systems. We first analyze the ICI in frequency domain with a reduced set of parameters, and following this analysis, we derive an EM algorithm for maximum likelihood (ML) data detection. An ML-EM receiver for OFDM systems and a TURBO-EM receiver for BICM/OFDM systems are then developed to reduce computational complexity of the EM algorithm and to exploit temporal diversity, the main idea of which is to integrate the proposed EM algorithm with a groupwise ICI cancellation method. Compared with the ML-EM receiver, the TURBO-EM receiver further employs a soft-output Viterbi algorithm (SOVA) decoder to exchange information with a maximum a posteriori (MAP) EM detector through the turbo principle. Computer simulation demonstrates that the two proposed receivers clearly outperform the conventional one-tap equalizer, and the performance of the TURBO-EM receiver is close to the matched-filter bound even at a normalized maximum Doppler frequency (MDF) up to 0.2.

Toward Optimal Multiuser Antenna Beamforming for Hierarchical Cognitive Radio Systems

In this paper, we present a joint antenna beamforming and power allocation technique to maximize the multiuser sum rate in an underlying microcellular system which reuses the same spectrum of a macrocellular system. One challenge in this kind of hierarchical cognitive radio (HCR) systems is to manage the interference between the macrocell and the microcell. The key contribution of this paper is to develop an optimization technique for antenna beamforming that can maximize the achievable sum rate of the underlying cognitive radio (CR) microcellular system and control the interference between the macrocell and the microcell with a satisfaction level. The proposed technique optimizes the sum rate performance by maximizing its lower bound and transfers the original non-convex problem into a convex optimization problem by introducing auxiliary variables to confine the intra-user interference power among the secondary system. Next, an iterative sum rate maximization (ISM) algorithm is developed to find the beamforming weights and the allocated power for each secondary user to simultaneously maximize system sum rate, coverage, and concurrent multiuser transmission probability in the HCR system. The developed joint design methodology provides valuable insights into the design of an optimal HCR system for various numbers of users as well as cell coverage, and can quantitatively optimize the performance tradeoffs in the hierarchical multiuser CR systems for current and future wireless communication applications.

A complementary codes pilot-based transmitter diversity technique for OFDM systems

This letter presents a complementary codes pilot-based space-time block code/orthogonal frequency division multiplex (STBC/OFDM) system. In this system, a pair of complementary codes transmitted in a pre-defined order with the OFDM data signals is used as the pilot signals in a two-antenna transmitter diversity system, and used to estimate the channels for optimal data detection at the receiver side. A complete receiver architecture has been designed and Monte Carlo simulations have been used to verify the performance of the system in mobile radio fading channels.

On Energy Harvesting Gain and Diversity Analysis in Cooperative Communications

The use of energy harvesting cooperative relays is a promising solution to battery-limited wireless networks. In this paper, we consider a cooperative system in which one source node transmits data to one destination with the assistance of an energy harvesting decode-and-forward (DF) relay node. Our objective is to minimize the long-term average symbol error rate (SER) performance through a Markov decision process (MDP) framework. By doing so, we find the optimal stochastic power control at the relay that adapts the transmission power to the changes of energy harvesting, battery, channel, and decoding states. We derive a finite-integral expression for the exact average SER of the cooperative system. Further insights are gained by analyzing the asymptotic average SER and its lower and upper bounds at high signal-to-noise ratio (SNR), and the performance is eventually characterized by the occurrence probability of the relays actions at the worst channel states in the MDP. We also show that the optimal cooperative policy at asymptotically high SNR follows a threshold-type structure, i.e., the relay spends the harvested energy only when the signal is successfully decoded and the source is faced with the worst channel condition in its direct link. Using these observations to quantify the diversity gain and the energy harvesting gain, we reveal that full diversity is guaranteed if and only if the probability of harvesting zero energy quantum is zero, which can be achieved by reducing the energy quantum size or increasing the energy harvesting capability. Finally, we present several numerical examples to validate the analytical findings.

A Robust Channel Estimator for High-Mobility STBC-OFDM Systems

In this paper, a robust channel estimator for high- mobility space-time block code-orthogonal frequency division multiplexing (STBC-OFDM) systems is proposed and applied in IEEE 802.16e systems. A high-performance two-stage channel estimation method is adopted. The proposed architecture reduces computational complexity effectively and improves 85.2% of the hardware implementation. The performances of the proposed design have been demonstrated through the simulation of an STBC-OFDM system with two transmit antennas and one receive antenna. At the vehicle speed of 120 and 240 km/hr for quadrature phase shift keying (QPSK) modulation, the proposed design can achieve the bit-error rate (BER) of about 10-4 and 10-3 without using channel coding. Moreover, it has significant performance improvement as compared with interpolation-based channel estimation methods. The proposed channel estimator implemented in 90 nm CMOS technology can support up to 29.03 Mbps (uncoded) downlink data transmission. The design only requires 859.6 K gates and dissipates 43.71 mW at 83.3 MHz operating frequency with 1 V power supply.

Toward Green Power Allocation in Relay-Assisted Multiuser Networks: A Pricing-Based Approach

Green communications have emerged as a demanding concept for improving the network energy efficiency (EE). In this paper, a pricing-based approach is investigated to achieve energy-efficient power allocation in relay-assisted multiuser networks. We introduce a network price to the power consumption as a penalty for the achievable sum rate, and study its impact on the tradeoff between the EE and the spectral efficiency (SE). It is hard to directly solve the problem as it is non-convex, and thus a concave lower bound on the pricing-based utility is applied to transform the problem into a convex one. Through dual decomposition, a q-price algorithm is proposed for iteratively tightening the lower bound and finding the optimal solution. In addition, an optimal price that enables green power allocation is defined and found from the viewpoint of maximizing EE. We further analyze the optimal power allocation strategies of the pricing-based approach in a two-user case under different noise operating regimes, yielding on-off, water-filling, and channel-reversal approaches, etc. Finally, the performance of the proposed approach is evaluated by computer simulations, and we characterize the interaction between the EE and SE for various network parameters when the network is designed from the energy-efficient perspective.

Higher-order statistics based sequential spectrum sensing for cognitive radio

In cognitive radio (CR), spectrum sensing is a key enabling functionality to discover the vacant spectrum which is not occupied by primary systems. With good sensing capability, secondary users can effectively recycle the spectrum resource without disturbing active primary users. Energy detector (ED) is a commonly used and relatively simple spectrum sensing technique. In realistic environments, the CR receiver might operate at low signal-to-noise ratio (SNR) regimes due to the channel fading and noise uncertainty. At low SNR cases, the performance of the EDs degrades dramatically as the signal and noise are mixed together after the operation of energy calculation. In this paper, a high-order statistics (HOS) based sequential test detector is investigated to sense the underutilized spectrum, particularly for low-SNR applications. We resort to HOS, in terms of cumulant statistics, for overwhelming the Gaussian noise effect and improving the spectrum sensing reliability. Based on these cumulants, a binary hypothesis testing problem is formulated and a low-complexity sequential probability ratio test (SPRT) is developed for efficiently and fast detecting the vacant spectrum so as to meet the sensing duration requirements. Our numerical results show that the proposed detector outperforms the conventional EDs at extremely low SNR environments.

A Refined Channel Estimation Method for STBC/OFDM Systems in High-Mobility Wireless Channels

In this paper, we investigate channel estimation for orthogonal frequency division multiplexing (OFDM) systems with space-time block code (STBC) in mobile wireless channels. Our proposed method consists of two-stage processing and is developed on the basis of the classical discrete Fourier transform (DFT)-based channel estimation method. In the initialization stage, we employ a multipath interference cancellation technique to estimate multipath delays and multipath complex gains. In the tracking stage, we develop a refined decision-feedback (DF) DFT-based channel estimation method in which a few pilot tones inserted in OFDM data symbols are applied to form an optimal gradient vector at the first iteration such that the error propagation effect is mitigated. In order to reduce computational complexity, an approximate weighting matrix is adopted to avoid matrix inversion. We demonstrate the proposed method through computer simulation of an STBC/OFDM system with two transmit antennas and a single receive antenna. The results show that our method outperforms the classical DFT-based method, the STBC-based minimum mean square error (MMSE) method, and the Kalman filtering method as well, and that significant signal-to-noise ratio (SNR) performance improvement can be achieved, especially when a high-level modulation scheme, e.g. 16QAM, is adopted in high-mobility environments.