Search

Wireless symbiotic communication is an emerging green technology for Internet of Things (IoT), where batteryless devices harvest energy from ambient radio frequency (RF) signals and use backscatter technology for communications. One practical but often neglected constraint for the symbiotic communications is circuit sensitivity, below which the passive devices cannot harvest energy. In this paper, we focus on the capacity analysis under the constraint of sensitivity. Specifically, we first build up the mathematical model for the wireless symbiotic communication system, then derive its channel capacity considering the sensitivity constraint, and compare the capacity performance with the existing symbiotic communication systems without sensitivity constraint and also the traditional point-to-point systems. Simulations are then provided to corroborate our proposed studies.

Sparse channel arises in a number of applications in wireless communications such as channel estimation and signal processing. There is growing evidence that physical wireless channels exhibit a sparse structure, and channel sparsity has been even considered as a nature of channels in many recent research works. However, there still lacks a good measure of channel sparsity, and mostly the assumptions that channel is sparse or non-sparse are based on intuitive analysis without measurement validation, which leads to some contradictions. In this article, based on channel measurement data, it is pointed out that a widely-used assumption, that wireless channels can be considered to be sparse, has pitfalls. Without loss of generality, the measurements are conducted in an urban scenario with different degrees of channel multipath richness. The channel degrees of freedom, diversity measure, and the Ricean K factor are used to evaluate channel sparsity, and they are found to have fairly high accuracy of measuring degrees of channel sparsity. It is also observed from measurements that the degree of channel sparsity is not steady and a sparse channel may change to non-sparse within a short time/distance observation window. Moreover, sparse and non-sparse based channel estimators are evaluated based on the measurements and the performances are analyzed. The results show that a sparse channel estimator cannot guarantee stable estimation accuracy even in channels with a high degree of sparsity, and considerable performance degradation will occur if a channel changes to non-sparse, which actually often happens in realistic communication scenarios and should be carefully considered in performance analysis. Some sparse channel related technical issues are also discussed in the article.

Nowadays, the rapid development of artificial intelligence (AI) provides a fresh perspective in designing future wireless communication systems. Innumerable attempts exploiting AI methods have been carried out, which results in the state-of-the-art performance in many different areas of wireless communications. In this article, we present the most recent and insightful developments that demonstrate the potentials of AI techniques in different physical layer (PHY) components and applications including channel characterization, channel coding, intelligent signal identification, channel estimation, new PHY for random access in massive machine-type communication (mMTC), massive multiple-input multiple-output (MIMO) power control and PHY resource management. Open challenges and potential future directions are identified and discussed along this research line.

Fast time-varying channel parameters and large penetration losses for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper, we introduce, for the first time, backscatter technology into HSR wireless communications, which can address these two challenges and yet have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relaying or beamforming. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and demonstrate that it outperforms the existing direct wireless transmission (DWT) scheme. We derive the upper and lower bounds of channel capacity for BAWT and prove that it exceeds that of DWT on certain conditions. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset and channel estimator, and signal detector. We show that BAWT, rather than DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally, simulation results are provided to corroborate the proposed solutions.

We investigate a wireless powered sensor network, in which multiple sensors generate data and send their data to a base station (BS) periodically. Each sensor first harvests energy from the BS via wireless power transfer and then uses its available energy to transmit to the BS its data. We target minimal average age of information, by optimizing the energy harvesting time and the bandwidth allocation during the sensors’ transmissions. The research problem is hard to solve, as some notations in the problem do not have a closed-form expression. To optimally solve the problem, we first show that there is a one-to-one mapping from the energy harvesting time to the bandwidth allocation. We also develop a method to obtain the bandwidth allocation vector corresponding to each value of the energy harvesting time. Then we get the optimal energy harvesting time by investigating and comparing different sub-regions of energy harvesting time. Numerical results show optimality of our solution and its performance gain over a benchmark scheme based on the traditional threshold-based method.

Energy source and circuit cost are two critical challenges for the future development of the Internet of Things (IoT). Backscatter communications offer a potential solution to conveniently obtain power and reduce cost for sensors in IoT, and researchers are paying close attention to the technology. Backscatter technology originated from the Second World War and has been widely applied in the logistics domain. Recently, both the academic and industrial worlds are proposing a series of new types of backscatter technologies for communications and IoT. In this paper, we review the history of both IoT and backscatter, describe the new types of backscatter, demonstrate their applications, and discuss the open challenges.

To support wireless Internet of things (IoT) devices, this paper presents a new solution which combines wireless power transfer and mobile edge computing. Specifically, we consider one mobile device, which first harvests energy from radio frequency signals sent by a base station and then offloads all or part of its data to be processed to the base station. The process of energy harvesting and offloading span over multiple fading blocks. The target is to maximize the average amount of processed data in unit time. To achieve this target, we optimize the stopping rule for energy harvesting (i.e., when to stop energy harvesting and start offloading) and the number of fading blocks for data offloading. To solve the formulated problem optimally, we decompose it into two levels. In the lower level, the stopping rule for energy harvesting is optimized given a fixed number of fading blocks for offloading. The associated lower-level problem is solved optimally based on a series of special properties of the problem. In the upper level, the number of fading blocks for offloading is optimized. Efficiency of our work with fully offloading mode and partially offloading mode is shown by using simulation.

This letter investigates a multiple-relay decode-and-forward (DF) network with simultaneous wireless information and power transfer (SWIPT). The relays use the power splitting (PS) technique to receive radio frequency (RF) energy and information simultaneously from the source. To characterize nonlinearity of energy harvesters at the relays, two existing models are adopted, which are the logistic function model and the constant-linear-constant (CLC) model. We target end-to-end throughput maximization by optimizing the power and bandwidth assignment for every source-relay-destination link and the PS ratio at every relay node. The formulated problems are demonstrated to be nonconvex. Through a series of analysis and transformations, we find global optimal solutions for the formulated problems. Numerical results verify the effectiveness of our proposed methods.

Wireless vehicular communications and sensing technologies are key to enabling more advanced intelligent transportation systems (ITSs) with improved safety and efficiency. Within the realm of wireless communication, millimeter-wave (mmwave) technology has recently received much attention, providing rich spectrum resources to support the timely transmission of large amounts of data. This is especially important for vehicular applications because the number of sensors on modern vehicles is rapidly increasing and thus generating large amounts of data. To fully exploit this potential, understanding mm-wave vehicle-to-vehicle (V2V) propagation channels is crucial. In this article, we review the state of the art in mm-wave V2V channel measurements and modeling, describe recent directional V2V channel measurements performed in the 60-GHz band, and discuss future challenges to be addressed in mm-wave V2V channel measurements and modeling.

Publisher Copyright: IEEE Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation,Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks.

In this paper, we suggest a new framework for wireless communications on railways aided by transmissive metasurfaces (TMs) and investigate the channel and phase shift estimation when orthogonal time frequency space (OTFS) is used. Specifically, we first establish the OTFS transmitter, end-to-end channel, and OTFS receiver models, and then define the input-output signal relation in vector form. Based on signal relation, we develop a channel and phase shift estimator that initially adopts the linear minimum mean squared error method to obtain the end-to-end channel and iteratively optimize the phase shift using the majorization-minimization (MM) method. Finally, numerical results are provided to show both convergence and performance of our MM-based estimation algorithm.

Fast time-varying channel parameters and large penetration loss for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper we introduce, for the first time, backscatter technology into wireless communication systems on HSRs which can address the two challenges and meanwhile have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relay or beamforming technology. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and compare its performance with existing direct wireless transmission (DWT) scheme. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset (CFO) and channel estimator, and signal detector. We show that BAWT, instead of DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally simulation results are provided to corroborate the proposed studies.

Internet of things (IoT) is future direction of network development. The goal of IoT is to smartly connect everything. However, there exist many challenges to realize the goal, such as power sources, access technologies and architectures. This chapter first states the origin of IoT, describes its development history, discusses existing and arising technologies of IoT, accesses algorithms and backscatter technology and suggests the challenges of implementing IoT for 5G and 6G.

The novel reconfigurable intelligent surface (RIS) is an emerging technology which facilitates high spectrum and energy efficiencies in Beyond 5G and 6G wireless communication applications. Against this backdrop, this paper investigates two-way communications via reconfigurable intelligent surfaces (RISs) where two users communicate through a common RIS. We assume that uplink and downlink communication channels between two users and the RIS can be reciprocal. We first obtain the optimal phase adjustment at the RIS. We then derive the exact outage probability and the average throughput in closed-forms for single-element RIS. To evaluate multiple-element RIS, we first introduce a gamma approximation to model a product of Rayleigh random variables, and then derive approximations for the outage probability and the average throughput. For large average signal-to-interference-plus-noise ratio (SINR) $\rho$, asymptotic analXsis also shows that the outage decreases at the rate $(\log(\rho)/\rho)$ where L is the number of elements, whereas the throughput increases with the rate $\log(\rho)$.

In this paper, power allocation problem is investigated for an LTE-U (long-term evolution unlicensed) system, in which a cellular system will transmit data on the unlicensed spectrum occupied by Wi-Fi system. One base station and multiple mobile users are considered for both uplink scenario and downlink scenario. To access the base station in uplink scenario or to transmit information to multiple mobile users in downlink scenario, orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are studied respectively. The transmit power of the base station or multiple mobile users over multiple channels in the unlicensed spectrum are optimized to maximize the system throughput while imposing a probabilistic constraint on the interference to Wi-Fi receiver only with a number of samples of interference channel’s gain. Although being in non-closed-form, the formulated probabilistic constraint is transformed to be two types of closed-form constraints. Then the originally formulated optimization problems fall into convex optimization problems. In addition, it is proved that the OMA mode can achieve the same performance with the NOMA mode for uplink scenario, and one simple solution is developed under OMA mode for downlink scenario. Numerical results are demonstrated to show the performance of our proposed methods.

This paper investigates the tradeoff between information rate and harvested energy in a simultaneous wireless information and power transfer system under power splitting (PS) scheme and time switching (TS) scheme. A single-input-multiple-output transmitter–receiver pair is considered. For the channel from the transmitter to the receiver, only the mean and covariance matrix are known, but the instantaneous channel information or the channel gain distribution is unknown. We define robust outage capacity that applies for all possible channel distributions with the given mean and covariance matrix, and study the tradeoff between the robust outage capacity and harvested power level. By solving optimization problems, we find the PS ratio setting for PS scheme and TS ratio setting for TS scheme to achieve the outer boundary of robust outage capacity-energy region (which includes all possible pairs of achievable robust outage capacity and harvested power level). The PS and TS ratio setting are fixed over fading blocks, and thus can avoid communication overhead and computation overhead over each fading block, and facilitate a simple implementation in a practical system. We also prove that the robust outage capacity-energy regions in PS scheme and TS scheme are both convex. Further, we prove that the PS scheme outperforms the TS scheme in terms of the rate-energy tradeoff. Numerical results are also presented to verify the correctness of our theoretical results.

This paper proposes a blind channel estimator based on expectation maximization algorithm and historical information-based basis expansion model for uplink wireless communication systems on high speed railways. The information of basis matrices is obtained from the uplink data of the past trains at the base station (BS). With the known basis matrices at the BS, our suggested estimator can estimate the basis coefficients and recover the channel parameters without requiring training symbols. The modified Cramer-Rao bound is derived for the estimated basis coefficients and the computational complexity of the proposed estimator is analyzed. Numerical results are then provided to corroborate our studies. It is shown that the proposed estimator outperforms existing data-aided estimators, including least square and linear minimum mean square error.

In this the network utility maximization problem is investigated for wireless networks when only channel mean and variance are known. Due to the randomness of wireless channel, link outage will happen and a traffic flow’s rate will gradually decrease along its routing path. When only channel mean and variance are known, channel distribution is uncertain and it is harder to control the rate loss caused by link outage. We take into account this feature of rate loss and target at maximizing the network utility of multiple traffic flows’ rates at their destinations. An optimization problem is formulated, which is non-convex. Global optimal solution is achieved with the aid of bilevel optimization method and monotonic optimization method. Numerical results are presented to verify the effectiveness of our proposed method.

Ambient backscatter, an emerging communication mechanism where battery-free devices communicate with each other via backscattering ambient radio frequency (RF) signals, has achieved much attention recently because of its desirable application prospects in the Internet of Things. In this paper, we formulate a practical transmission model for an ambient backscatter system, where a tag wishes to send some low-rate messages to a reader with the help of an ambient RF signal source, and then provide fundamental studies of noncoherent symbol detection when all channel state information of the system is unknown. For the first time, a maximum likelihood detector is derived based on the joint probability density function of received signal vectors. In order to ease availability of prior knowledge of the ambient RF signal and reduce computational complexity of the algorithm, we design a joint-energy detector and derive its corresponding detection threshold. The analytical bit error rate (BER) and BER-based outage probability are also obtained in a closed form, which helps with designing system parameters. An estimation method to obtain detection-required parameters and comparison of computational complexity of the detectors are presented as complementary discussions. Simulation results are provided to corroborate theoretical studies.

Ambient backscatter is a new technology that uses ambient signals to enable communication between battery-free tags (or sensors) and readers. Previous studies on ambient backscatter focused on scenarios with only one tag. In this paper, we investigate the ambient backscatter communication system with multiple tags and analyze the performance of tag selection. Specifically, we formulate a tag selection scheme and design the corresponding detection method. Our results indicate that obtaining the closed-form bit-error rate (BER) in such cases is challenging; hence, we provide a solution for deriving an approximate BER. The simulation results show that the approximated BER curves are in agreement with the exact curves.

Ambient backscatter technology has attracted much attention recently, because it enables battery-free devices, such as tags or sensors, to communicate through wireless energy harvesting and radio backscattering. Existing studies about ambient backscatter assume that the tags have only two states: backscatter or non-backscatter. Actually, some references have shown that the tags can readily realize three states: positive and negative phase backscatter, and non-backscatter. In this paper, we propose a new coding scheme for these tags with three states to improve the throughput of the ambient backscatter communication system. We then design a maximum a posteriori (MAP) detector for the reader to extract binary information from ternary coded signals. We also analyze the detection performance in terms of closed-form bit error rate (BER) expressions. It is found that the proposed coding scheme can improve the throughput of an ambient backscatter system, and there exists an error floor for the BER curve. Finally, simulation results are provided to corroborate our theoretical studies.

Wireless communication has been a highly active research field. Channel estimation technology plays a vital role in wireless communication systems. Channel estimates are required by wireless nodes to perform essential tasks such as precoding, beamforming, and data detection. A wireless network would have good performance with well-designed channel estimates. In this chapter, we first review the channel model for wireless communication systems and then describe two traditional channel estimation methods, and finally introduce two newly designed channel estimators based on deep learning and one expectation-maximization-based channel estimator.

In this paper, we introduce one full-duplex (FD) relaying transmission scheme for high speed railway and compare its ergodic capacity and outage performance with half-duplex (HD) relaying scheme. Both decode-and-forward (DF) amplify-and-forward (AF) relay modes are considered. Moreover, the carriage penetration loss (CPL) and the self-interference (SI) cancellation ratio are investigated. We derive the closed-form expressions for the outage probability and ergodic capacity of both HD and FD relay transmission schemes. It is shown that when CPL is larger than a certain level, the FD relay can achieve better performance in terms of capacity and keep the outage probability in a low level. It is also found that three factors: AF or DF modes, CPL and SI cancellation ratio, can have impact on capacity performance, and that CPL is one decisive factor. Our results can provide theoretical supports for development and deployment of future wireless communication systems on high speed railways.

Existing studies on cognitive two-way relay networks assume that the primary users (PUs) transmit data with two power levels. In practice, it is possible that the PUs may adopt multiple power levels to maximize their throughputs or to compensate for wireless link fading. In such a scenario, there exist two key problems: one is the detection accuracy for the secondary users (SUs), and the other is the throughput tradeoff between PUs and SUs. To address the detection accuracy challenge, we propose a cooperative soft-combination-based energy detector and demonstrate its benefit over other hard-combination-based detectors. To solve the throughput tradeoff problem, we derive the throughput expressions for both PUs and SUs and analyze the impact of our detection accuracy on the throughputs. It is shown that the SU’s throughput varies with the number of the PU’s transmit power levels and achieves a maximum value when the number is small. We also find that increasing the detection capability of the SU reduces its own throughput, while increases the PU’s throughput.

In this paper, we propose a cognitive transmission scheme for Amplify-and-Forward (AF) two-way relay networks (TWRNs) and investigate its joint sensing and transmission performance. Specifically, we derive the overall false alarm probability, the overall detection probability, the outage probability of the cognitive TWRN over Rayleigh fading channels. Furthermore, based on these probabilities, the spectrum hole utilization efficiency of the cognitive TWRN is defined and evaluated. It is shown that smaller individual or overall false alarm probability can result in less outage probability and thus larger spectrum hole utilization efficiency for cognitive TWRN, and however produce more interference to the primary users. Interestingly, it is found that given data rate, more transmission power for the cognitive TWRN does not necessarily obtain higher spectrum hole utilization efficiency. Moreover, our results show that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time slots between the spectrum sensing and data transmission phases. Finally, simulation results are provided to corroborate our proposed studies. Copyright © 2016 John Wiley & Sons, Ltd.

High-speed railways (HSRs) improve the quality of rail services, yield greater customer satisfaction, and help to create socioeconomically balanced societies [1]. This highly efficient transport mode creates significant challenges in terms of investment, technology, industry, and environment. To handle increasing traffic, ensure passenger safety, and provide real-time multimedia information, a new communication system for HSR is required. In the last decade, public networks have been evolving from voice-centric second-generation systems, e.g., Global System for Mobile Communications (GSM) with limited capabilities, to fourth-generation (4G) broad-band systems that offer higher data rates, e.g., long-term evolution (LTE). It is thus relevant for HSR to replace the current GSM-railway (GSM-R) technology with the next-generation railway-dedicated communication system providing improved capacity and capability.

In this study, a problem of efficient spectrum sensing and power allocation is studied. A network of cognitive radio adopting the technique of two-way relay is considered. A relay node, sitting between two secondary users, carries out spectrum sensing and helps the two secondary users to realise bidirectional relay communications. For such a system, an optimisation problem which targets at maximising the average rate between the two secondary users while guaranteeing the detection probability above a predefined threshold, is formulated. Although the problem is shown to be non-convex which means the global optimal solution is hard to be obtained, the authors solve the formulated problem global optimally by using a combination of bilevel optimisation and monotonic programming. Numerical results are provided to present the effectiveness of the authors' proposed algorithm.

Ambient backscatter has been a hot research topic ever since its birth in 2013. One open problem for ambient backscatter communication systems is individual channel estimation, which is the focus of this paper. In this paper, we design a communication protocol for the reader and the tag so as to obtain all the channel parameters. Specifically, we utilize expectation maximization (EM) algorithm, one typical machine learning approach, to design a semi-blind estimator so as to acquire combined channel parameters. We also obtain the uplink channel between the reader and the tag with a maximum likelihood (ML) estimator and estimate the downlink channel with superimposed pilots from the reader. In addition, we derive all the Cramer-Rao bounds (CRB) of the proposed channel estimators. Finally, simulation results are provided to corroborate our theoretical studies.

The millimeter-wave (mmWave) technology is one possible solution to addressing the explosive requirements for mobile data access on high-speed railways (HSRs). However, utilizing the mmWave technology on HSRs will result in large Doppler shift, which should be estimated and compensated. Traditional Doppler shift estimators including the cyclic prefix (CP) based estimator (CPBE), the two-training-symbol based estimator (TBE) and the best linear unbiased estimator (BLUE) have inferior estimation accuracy especially at low signal-to-noise ratio (SNR). In this paper, we propose the new Doppler shift estimator for mmWave communication systems on HSRs: an equally-divided structure based estimator (ESBE) that divides the effective orthogonal frequency-division multiplexing (OFDM) symbol into multiple equal fragments. Simulation results show that our new estimator outperforms several existing Doppler shift estimators in mmWave communication systems on HSR.

As an emerging system architecture, heterogeneous cloud radio access networks (H-CRANs) can improve system capacity, enlarge coverage, and enhance energy/spectral efficiency. Meanwhile, this newborn architecture also brings many open problems for traditional topics, including synchronization, channel estimation, and data detection. In this article, we present a comprehensive analysis on obtaining CSI in H-CRANs. Specifically, we recognize seven challenges in channel estimation that are caused by a large number of channel parameters, heterogeneity of access nodes in H-CRANs, and the time delays among different nodes. Several research directions for handling these challenges are also proposed, for example, array signal processing and channel compression can eliminate the number of channel estimates, while channel prediction and modification for high-speed railway communications and adaptive downlink array from uplink measurements excel in overcoming the non-reciprocity in channel parameters.

Renewable energy, such as wind and solar energy, may vary significantly over time and locations depending on the weather and the climate conditions. This leads to the supply uncertainty in the electricity (power) market with renewable energy integrated to power grid. In this paper, electricity in the market is classified into two types: stable-supply electricity (SSE) and unstable-supply electricity (USE). We investigate the investment and pricing strategies under the electricity supply uncertainty in wholesale and retail electricity market. In particular, our model combines the wholesale and retail market and capture the dominant players, i.e., consumers, power plant (power operator), and electricity supplier. To derive the market behaviors of these players, we formulate the market decision problems as a multistage Stackelberg game. By solving the game model, we obtain the optimal, with closed-form, wholesale investment and retail pricing strategy for the operator. We also obtain the energy supplier's best price mechanism numerically under certain assumption. We find the price of SSE being about 1.4 times higher than that of USE will benefit energy supplier optimally, under which power plant's optimal strategy of investing is to purchase USE about 4.5 times much more than SSE.

Ambient backscatter is a new technology that utilizes environmental wireless signals to enable battery-free devices to communicate with each other. In this paper, we investigate the problem of signal detection for the full duplex wireless system utilizing ambient backscatter. Since one of the key technologies of full duplex wireless system is self-interference cancellation, we first estimate the channel between transmit antenna and receive antenna to eliminate self-interference. Next, a maximum likelihood (ML) detector is proposed to decode the signals from the ambient backscatter device, and the theoretical and practical thresholds are derived in the presence of channel estimation error. In addition, we demonstrate the existence of the optimal number of training symbols that can maximize the system throughput. Finally, simulation results are provided to corroborate our theoretical studies.

Ambient backscatter is an emerging technology that utilizes the surrounding radio frequency (RF) signals such as digital digital television (TV) broadcasting, cellular or Wireless-Fidelity (Wi-Fi) to enable batteryless devices to communicate with each other. One open problem for ambient backscatter is channel capacity. In this paper, we investigate the capacity problem of an ambient backscatter communication system with a passive tag and a reader. Based on the Gaussian channel model, we design a method to determine channel capacity for the ambient backscatter communication system. The channel capacity is the maximal value of the derived mutual information. Interestingly, we find that the capacity for the ambient backscatter communication system is obtained when the input signals are not equiprobable. Finally, numerical results are provided to corroborate our theoretical analysis.

This paper proposes a blind channel estimator based on expectation maximization (EM) algorithm and historical information based basis expansion model (HiBEM) for uplink wireless communication systems on high speed railways (HSRs). The information of basis matrices is obtained from the uplink data of the past trains at the base station (BS). With the known basis matrices at the BS, our suggested estimator can estimate the basis coefficients and recover the channel parameters without requiring training symbols. Numerical results are provided to corroborate that the proposed estimator outperforms existing data-aided estimators, including least square (LS) and linear minimum mean square error (LMMSE).

We study the semi-coherent detection of a novel communication mechanism, ambient backscatter, where detection-required parameters can be obtained from unknown data symbols and a few pilot symbols. To release dependence on prior knowledge of ambient signals, we propose a type of robust design, called energy detector, with the ambient signal being either complex Gaussian (CG) or phase shift keying (PSK). Detection thresholds, analytical BERs, and outage probability are obtained in closed-form. Interestingly, the CG source would cause an error floor while the PSK source does not, which brings nontrivial indication of constellation design as opposed to the popular Gaussian-embedded literatures. Simulations are provided to corroborate the theoretical studies.

Ambient backscatter is a new technology that utilizes existing wireless signals of other communication systems to enable small devices to communicate with each other. One key problem for ambient backscatter is that if it will result in interference on existing communication systems. We investigate this problem in this paper. Specifically, we build up the theoretical system model for the ambient backscatter systems, design a maximum likelihood (ML) detector for the legacy receiver, and also derive the corresponding bit error rate (BER) expressions. Finally, we evaluate the interference of the backscattered signals on existing communication systems via simulations. It is found that channel estimation results, distances and angles between the tag, the radio frequency (RF) source and the legacy receiver will impact the BER performance.

This paper investigates joint maximum likelihood (ML) Doppler shift and channel estimation problem for high speed railway (HSR) wireless communication systems with applying massive uniform linear array (ULA). For characterizing the performance of the considered scenarios, we provide a tractable framework for analyzing the performance of on Doppler shift estimation. Furthermore, the analytical expressions of ML channel estimation are derived, under the joint consideration of Doppler shift. Both analysis match their corresponding Cramer-Rao Bounds (CRBs) well. Finally, simulation results are provided to corroborate our proposed studies.

In this paper, we consider a point-to-point single antenna communication system where the receiver decodes information and harvests energy at the same time. Two practical schemes, namely power splitting scheme and time switching scheme, are investigated, in which the receiver splits or switches signals for information decoding and energy harvesting. We derive the characteristics of the formulated problem which maximizes outage received power under the outage capacity probability and outage received power probability constraints. Moreover, the numerical results prove the correctness of the characteristics of the optimization problem. In particular, it illustrates that the PS scheme outperforms the TS scheme.

Physical layer (PHY) security is recently regarded as a promising technique to improve the security performance of wireless communication networks. Current developments in PHY security are often based on the assumption of perfect channel state information (CSI). In this paper, both security and reliability performance for the downlink cloud radio access network with optimal remote radio heads (RRHs) node selection are investigated in a practical scenario by considering channel estimation (CE) errors. In particular, a three-phase transmission scheme is proposed and the linear minimum mean-square error (MMSE) estimation method is utilized to obtain the CSI. Based on the CSI estimates and the statistics of CE errors, the outage probability and intercept probability are derived in closed-form expression to evaluate the security and reliability performance, respectively. In addition, two possible cases (with or without intercepting signals from baseband unit) are considered for the eavesdropper. It is found that the suggested optimal RRHs selection scheme outperforms the nonselection scheme, and that the increasing number of RRHs can lower the outage probability as well as the intercept probability. It is also shown that there exists an optimal training number to minimize the sum of the outage probability and intercept probability. Finally, simulation results are provided to corroborate our proposed studies.

This paper reports (i) a set of measurements of the wireless propagation channel at 930 MHz, conducted along the "Zhengzhou-Xian" high-speed railway of China in various railway viaduct scenarios, and (ii) an analysis and modeling of the small-scale and large-scale channel parameters based on those measurements. The environment can be categorized into four cases, covering viaducts with different heights and in different suburban environments. Small values of fade depth, level crossing rates, and average fade duration are observed. Akaike's Information Criterion (AIC)-based evaluation indicates that the Ricean distribution is the best to describe small-scale amplitude fading. An analysis of the envelope autocovariance function shows that the coherence distance is less than 10 cm. The Ricean K-factor is modeled as a piecewise-linear function of distance. Moreover, a breakpoint path loss model is developed and shadow fading is investigated using the same break point as for the distance-dependent K-factor model. The Suzuki distribution is found to offer a good fit for the composite multipath/shadowing channels. We find that the viaduct height H, together with the number of surrounding scatterers, significantly affects the small- and large-scale channel parameters. These results are applicable to both normal-speed and high-speed railways, and will be useful in the modeling of railway viaduct channels and the design of railway wireless communication systems.

In this paper, we mainly study the symbol detection of the ambient backscatter, a novel communication mechanism that utilizes ambient radio frequency signals as both the power source and the symbol carrier to enable communications between devices. We derive the optimal detector with its closed-form bit error rate (BER), and thus give the detection that yields the balanced BER. We next design a suboptimal detector with a relatively simple BER expression that tells more insight of the system parameters. For the practical application, we propose a method to estimate the required parameters of the detectors, which heuristically derives another detection threshold. Simulations are then provided to corroborate the theoretical studies.

Time-varying channel estimation is a well-known challenge for wireless communications on high-speed railways. Existing estimation algorithms include interpolation, basis expansion model (BEM) and autoregressive models. These methods remain unchanged for every scenario on high speed railways (HSRs), such as viaducts, tunnels, cutting and open areas. Thus their estimation performance are not consistent and also not satisfactory in many cases. Noting that every train on one high-speed railway follows the same track and therefore the channel parameters are correlated at each fixed position. Based on this observation, we propose a new BEM that explores the correlation between current channels parameters and past ones. More importantly, we analyze its channel estimation performance and justify our suggestions. Finally, simulation results are provided to corroborate our proposed studies.

In this paper, we consider the sequential detection problem in a new cognitive radio (CR) scenario when the primary user (PU) works with more than one transmit power levels. The targets of the secondary user (SU) is not only to detect the presence of PU but also to recognize PU's transmit power levels. We formulate a valid sequential detection approach via the modified Neyman Pearson (NP) criterion and then derive the closed-form decision region for each PU's transmit power level. Moreover, the average sample number (ASN), a key metric for any sequential detection method, is analytically derived in closed-form to facilitate the performance evaluation. Finally simulation result is presented to verify the correctness of the proposed studies.

In this paper, we explore the underlay cognitive relay networks in the presence of hardware impairment. To avoid interfering with the primary user's communications, the transmission powers of the secondary source and relays are adaptively adjusted by considering the following three practical effects: i) the maximum transmission power constraint at the secondary transmission nodes, ii) the interference power constraint at the primary receiver, and iii) imperfect hardware. By taking the correlations among the received signal-to-interference-plus-noise ratios into account, exact closed form expressions for the outage probabilities are respectively derived for the cases with and without a direct secondary link over Rayleigh fading channels. We further conduct an asymptotic outage probability to evaluate the impact of hardware impairment on diversity order, and show both cases with and without direct secondary link can achieve the full diversity order. Finally, simulation results are presented to verify the correctness of our analytical derivations.

We study the problem of signal detection for the ambient backscatter system (ABS) when data are transmitted with differential modulation. An implementation of the maximum likelihood (ML) detection algorithm is proposed. To reduce the computational complexity, we further design a suboptimal detector and derive its bit error rate (BER) closed-form expression. Moreover, both the upper and the lower bounds of the BER, which can tell more insight of how system parameters affect the detection performance, are obtained. Simulations are then provided to corroborate the studies.

SUMMARY In traditional Internet architectures, current centralized mobility management schemes face scalability issues because of the creation of network bottlenecks and a single mobility agent of failures. To address these issues, we propose a distributed mobility management scheme in locator/identifier separation networks (DMMLIS). We divide the network into many domains according to the area of autonomous systems. Each domain consists of a mapping server and several tunnel routers (xTRs). The mapping server stores global mappings between endpoint identifier (EID) prefixes and autonomous system numbers (ASNs) to lookup the mobile nodes' (MNs') home domain. Meanwhile, the mapping server also contains ASN-to-xTR mappings so that it can forward Map-Register and Map-Request messages to any xTR of the MN's home domain, thus enhancing reliability. In addition, the xTRs in each domain constitute one-hop distributed hash table ring. Moreover, any xTR in the MN's home domain may be the home agent of MNs, and the MNs' EID-to-RLOC (routing locator) mapping is stored in two xTRs using two hash functions. In this way, it not only supports quick lookup but also improves scalability and survivability. To evaluate the survivability and the efficiency of DMMLIS, we analyze the service blocking probability, the system response time, and the total protocol cost. We also present numerical results to demonstrate the performance of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.

Joint estimation of the carrier frequency offset (CFO) and the channel is developed for a two-way relay network (TWRN) that comprises two source terminals and an amplify-and-forward (AF) relay. The terminals use orthogonal frequency division multiplexing (OFDM). New zero-padding (ZP) and cyclic-prefix (CP) transmission protocols, which maintain the carrier orthogonality and ensure low estimation and detection complexity, are proposed. Both protocols lead to the same estimation problem which can be solved by the nulling-based least square (LS) algorithm and perform identically when the block length is large. We present detailed performance analysis by proving the unbiasedness of the LS estimators at high signal-to-noise ratio (SNR) and by deriving the closed-form expression of the mean-square-error (MSE). Simulation results are provided to corroborate our findings.

This paper investigates the Doppler shift estimation problem for high speed railway (HSR) wireless communication systems with massive linear receiving antennas. Firstly, a theoretical channel model is built up with channel coefficient, direction of arrival (DOA) and Doppler shift taken into account. Secondly, a new Doppler shift estimation algorithm is proposed through reconstructing the received signal based on Discrete Fourier Transform (DFT). Moreover, a phase rotation algorithm is presented to improve the estimation accuracy. It is found that the suggested estimation algorithm works well and also obtains DOA estimate. Finally, simulation results are provided to corroborate our proposed studies.

Devices that harvest power from radio-frequency (RF) signals are generally referred to as RF-powered devices. One emerging technology that enables RF-powered devices to communicate with others is ambient backscatter. In this paper, we study the problem of signal detection and analyse the uplink bit error rate (BER) performance for RF-powered devices utilizing ambient backscatter. Specifically, we build up a theoretical model for a communication system which consists of one reader and one tag. The tag employs ambient backscatter to communicate with the reader. Next we design an optimal detector which can minimize the BER and find the closed-form expression for the detection threshold. Noting that the optimal detector cannot result in equal BERs in detect “0” or “1”, therefore we design another detector that can achieve the same error probability in detecting “0” with that in detecting “1”. Moreover, we analyse the BER performance for both detectors. Finally, simulations are provided to corroborate the proposed studies.

Ambient backscatter is a new communication technology that utilizes ambient radio frequency signals to enable battery-free devices to communicate with each other. In this paper, we study the problem of signal detection and bit error rate (BER) performance for this novel communication system that utilizes ambient backscatter and multiple receiving antennas. Specifically, we first build up the theoretical system model and then propose a maximum likelihood detection method. We also obtain the optimal closed-form detection threshold and the corresponding BER expression. It is found that increasing the number of the receiving antennas can bring about larger BER gains at low SNR, while little gains in high SNR. Finally, simulation results are provided to corroborate our theoretical analysis.