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To improve energy efficiency and reduce the interference to macro users (MUs), this paper investigates the robust resource allocation (RA) problem for maximizing the interference efficiency of users (i.e., total rate/sum interference) in heterogeneous networks. Under perfect channel state information (CSI), the considered RA problem is formulated as a multivariate nonlinear programming problem with constraints on the maximum interference power of MUs, the minimum rate requirement of each femto user, and the maximum transmit power of the femto base station. The original fractional programming problem is converted into a convex optimization problem by using Dinkelbach's method, the logarithmic transformation method, and the successive convex approximation method, where the closed-form solution is obtained with the Lagrangian dual approach. Moreover, with imperfect CSI consideration, the original problem is reformulated as a robust RA problem that is transformed into a deterministic and convex optimization problem by using an inequality transformation approach. In addition, the computational complexity and the cost of robustness are also analyzed. The simulation results verify that the proposed algorithm has better interference efficiency and robustness by comparing with the existing algorithms.

In this paper, based on the characteristics of inertial data collected by smart terminals carried by pedestrians, a dynamic estimation algorithm of stride length using motion mode recognition is proposed. The motion mode includes pedestrian motion status and a smart terminal attitude. As pedestrians move forward, different motion modes will have a corresponding impact on the stride length estimation model, resulting in the stride length estimation model directly affecting the accuracy of the pedestrian dead reckoning system (PDR). Therefore, different motion modes need to make corresponding adjustments to the stride length estimation model to give a specific stride-length adjustment gain. In order to avoid errors caused by the change of the motion mode, a dynamic time warping (DTW) algorithm is proposed to identify the motion mode of the smart terminal, so as to select an appropriate gain to adjust the stride length estimation model, thereby improving the positioning accuracy of the pedestrian dead reckoning system. Experimental results show that the average positioning error of the stride-length estimation model based on DTW motion mode recognition is 1.78 meters in five motion modes. In performance, this algorithm is superior to other traditional stride length estimation models.

In this paper, deep learning is combined to learn the bit posterior probability (soft information) of iterative decoding for a bit-interleaved coded modulation with iterative decoding (BICM-ID) system. The deep neural network (DNN) is adopted to learn and replace multiple modules of the receiver, which can jointly deal with multiple problems and improve the efficiency of the whole system. The output of direct learning iteration reduces the computational cost of iteration to a certain extent. Simulation is carried out under Rayleigh channel and multiple modulation modes and results show that the proposed scheme without iteration is better than traditional BICM systems, and very close to traditional BICM-ID systems which needs iteration between demodulation and decoding.

A dual-band circularly polarized (CP) microstrip antenna for BeiDou Navigation Satellite System (BDS) is presented. A circular patch with notches and a ring slot with notches are used for upper and lower band radiators respectively. Meanwhile, the circular patch acts as a coupling patch to feed the ring slot radiator. A quarter wavelength transformer and branch impedance adapter are introduced for impedance matching. The measured and simulated results show good CP radiation at 1618 MHz and 2452 MHz. The gains of the proposed antenna are 7.91 dBi and 5.24 dBi at the upper band and the lower band respectively.

The design and improvement of feature descriptor is always a prevalent research point. A strong design of feature descriptor can be applied to many industrial fields. However, in many successful designs, there are still have some shortcomings that need to be improved like the flipping operation in image. Although there are many techniques to deal with this problem recently, they still exist some uncertainty factors because they do not compare with different images which have the same but flipped object in their experiments. In this paper, we design an unsupervised learning structure algorithm based on SIFT to achieve flipping invariant and change object to get as many correct matches as possible. Then we will use this algorithm to match in UKBench dataset and indicate that this approach not only solves the problem of flipping operation but also ensures high matching accuracy. Compared with previous other algorithms, our algorithm is extremely simple to implement and does not destroy the structure of SIFT.

Electrocardiogram (ECG) is the physical interpretation of the electrical conductivity of the heart resulting from the muscular activities. It provides an effective way to observe heart diseases during medical treatments and studies. During the acquisition and transmission of ECG signal in the medical environment, the signal is often corrupted by various types of noises. The transmission of ECG regularly introduces noise due to terrible channel conditions. Moreover, there are different types of noises inherent in the data collection process. In order to reduce the impact of noise, in this paper, we proposed an algorithm based on the EMD and the piecewise polynomial fitting to remove the baseline wander for ECG signals. Here, the effect of this algorithm on wandered ECG signals is discussed elaborately with graphical interpretations. It is also analyzed that why this algorithm works better to remove baseline wander by using EMD and combine the piecewise polynomial fitting to denoise the signal processed by the EMD.

Template matching is widely used in computer vision and pattern recognition for its efficiency. However, conventional matching algorithms fail to match same templates of a same scene when illumination changes, especially when shadows exist. To achieve higher accuracy of template matching under different illuminations, a template matching algorithm is proposed based on gradient-chromatic illumination-invariant image. The matching results on Middlebury 2014 stereo dataset shows that our algorithm is illumination-invariant and achieve higher accuracy of rectangle template matching.

In this paper, in order to solve poor communication problems under the long-distance transmission and secure communication, an energy-efficient maximization based resource allocation (RA) algorithm is proposed to improve system energy efficiency (EE) and information security for secure intelligent reflecting surface-based cellular networks under an active eavesdropper. Firstly, a nonlinear RA model with coupled variables (i.e., beamforming vector and phase shift matrix) is formulated for a multiple-input single-output cellular system with the help of IRS, where the minimum secure rate constraint of user, the maximum transmit power constraint of base station (BS), and continuous phase shift constraint are considered simultaneously. Then, the fractional objective function is transformed into a parameter-subtractive form by using Dinkelbach's approach, meanwhile the coupled beamforming vector and the phase shift parameter are decoupled by using an alternating iteration method. Moreover, the non-convex problem is converted into a semi-definite programming problem solved by using convex optimization tools. Finally, simulation results demonstrate the proposed algorithm has good EE and security.

In this paper, we investigate a downlink outage- constrained robust resource allocation algorithm for achieving the robust transmission in power-domain non-orthogonal multiple access based underlay cognitive radio networks. Our goal is to maximize the sum energy efficiency of secondary users (SUs) under the consideration of imperfect channel state information, while protecting the data rate requirement of each SU and satisfying the maximum transmission power constraint of the secondary base station as well as the interference temperature constraint of each primary user (PU). The proposed optimization problem is non-convex and difficult to solve. To deal with the intractability of the problem coming from the outage probability constraints, in particular, the closed-form expressions of the outage probabilities of SUs and PUs are derived. The original problem is converted into an equivalent convex problem by using successive convex approximation and parameter transformation. A robust subchannel and power allocation algorithm is developed to solve the problem. Simulation results show the effectiveness of the proposed algorithm by comparing with the existing algorithms.

Conventional downlink non-orthogonal multiple access (NOMA) with spatial modulation (SM) limits the number of chosen transmitting antennas. Therefore, a multiuser NOMA-based generalized spatial modulation (GSM) system is proposed for the downlink scenario, where more than one transmitting antenna are selected. Different channel gains are considered in the NOMA-GSM system to reduce the impact of intra-group interference. Moreover, to make good use of power domain and spatial domain resources, a dynamic user grouping is proposed, where different users are selected from different sets according to their effective channel gains. Moreover, a dynamic power allocation scheme is proposed to maximize the achievable sum rate of users, which is the suboptimal solution of the product of two-user signal-to-interference-plus-noise ratio. To extend to three-user systems, another power allocation scheme is proposed. The achievable sum rate maximization problem is transformed into a convex optimization problem by introducing the auxiliary variable. Simulation results show that proposed user grouping and power allocation schemes have advantages compared with existing ones.

To improve transmission efficiency and robustness, we investigate robust energy efficiency (EE)-based maximization power allocation and time optimization problem in orthogonal frequency division multiple access (OFDMA) based heterogeneous networks under channel uncertainties. The non-convex optimization problem is decomposed into three subproblems: subcarrier allocation, transmission time optimization and power allocation. The subcarrier allocation is obtained based on maximizing user's EE under the effective transmission time. The Taylor expansion method is used to obtain the optimal transmission time. Additionally, Bernstein approach and Dinkelbach's method are used to solve the power allocation problem. Simulation results indicate that the proposed algorithm has good robustness in terms of outage probability.

In this paper, the interference-efficient based resource allocation for uplink transmission in an OFDMA-based cognitive radio network is studied. The interference efficiency (IE) is defined as the total data rate of secondary users (SUs) over sum interference power imposed on primary users (PUs). The objective is to maximize the total IE of SUs subject to transmit power constraints of SUs, subcarrier assignment constraint and the interference power threshold constraints of SUs. The original mixed integer fractional programming problem is converted into the convex one which is solved by using the convex optimization technique. Simulation results show the effectiveness of the proposed algorithm in terms of the interference to PUs and transmission efficiency.

Mobile AdHoc Networks (MANET) can be fast implemented, and it is very popular in many specific network requirements, such as UAV (Unmanned Aerial Unit), Disaster Recovery and IoT (Internet of Things) etc. However, MANET is also vulnerable. AODV (Ad hoc On-Demand Distance Vector Routing) protocol is one type of MANET routing protocol and many attacks can be implemented to break the connections on AODV based AdHoc networks. In this article, aim of protecting the MANET security, we modeled the AODV protocol with one type of Automata and analyzed the security vulnerabilities of it; then based on the analyzing results, we proposed an enhancement to AODV protocol to against the Black Hole Attacks. We also implemented the proposed enhancement in NS3 simulator and verified the correctness, usability and efficiency.

For heterogeneous networks (HetNets), power allocation strategy with energy harvesting (EH) will be a promising technique in the green communication system. In this paper, considering the trade-off between nonrenewable energy consumption and robustness, a robust power allocation (RPA) problem is formulated to optimize energy-efficiency for EH-enabled HetNets under imperfect channel state information. Meanwhile, minimum required rate constraints and maximum interference constraints to macrocell users are guaranteed. Based on fractional programming and worst-case principle, the original non-convex optimization problem can be converted into a deterministic convex one which can be solved by using Lagrange dual decomposition theory. Numerical results show the efficiency and robustness of our proposed RPA algorithm comparing with non-robust algorithm.

In this work, a wearable, intelligent testing device based on nRF52832 microcontroller is designed to monitor the changes of life signs parameters of patients with depression and cardiovascular disease and determine disease seriousness and mood stability. The proposed device uses the ADS1292R chip and conductive silica gel for collection and preconditioning the electrocardiogram (ECG) and respiratory signals, which and then filtered by nRF52832. Since nRF52832 is a Bluetooth chip, it transmits the life signs signals to the mobile phone APP through Bluetooth to be displayed on a device screen. The wearable, intelligent detection devices have the advantages of small size, low power consumption, low cost, and convenience to be wearable.

Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Bluetooth Beacon positioning is one of the most widely used technology. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors by using fixed path loss models. In this paper, we proposes a RSSI real-time correction method based on Bluetooth gateway, which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusted by the RSSI fluctuation information stored on the server in real time. The adjusted RSSIs can be used for calculation and obtain a more accurate distance, i.e., smaller positioning error. Finally, the position of blind nodes is estimated by least squares algorithm. The experiment shows that the proposed method improves the positioning accuracy.

The Multiple-Input Multiple-Out (MIMO) technology can improve system capacity by using multiple antenna transceivers with high decode complexity at the receiver. The Integer-Forcing (IF) linear detection algorithm is used to reduce the complexity of implementation in MIMO system, and its performance is close to the ML detection algorithm. However, the decode complexity of the IF detection will increase with the increase of antenna number. In order to further reduce the decode complexity, we propose a new detection algorithm called Group Integer-Forcing detection algorithm in this paper. In this algorithm, the received symbols are divided into groups, then each group will be decoded by the IF detection respectively to recover transmitted codewords in their own group, and those recovered codewords will be gathered to obtain original transmitted information before grouping. The Group Integer-Forcing detection algorithm reduce the decode complexity via grouping, so it has great advantage on complexity compare with the IF detection. It also has better error performance than the ZF detection algorithm according to simulation results, and the error performance gap is more and more obvious with the increased antenna numbers.

Compared with the Multiple-Input Multiple-Output (MIMO) systems coded with Space-Time Block Code (STBC), Spatial Modulation (SM) has high spectral efficiency and low diversity gain. After combined with Space-Time Block Code (STBC), so called STBC-SM is able to obtain higher diversity gain at the same time. In this paper, we propose a new full-diversity STBC-SM scheme for spatial modulation systems with four transmit antennas. The transmitted information is partitioned into two parts for STBC-SM codeword selection and modulation symbol mapping, respectively. The proposed codeword matrices are constructed by simultaneously choosing the transmit antennas of one pair of orthogonal STBC instead of only one. Precoding and symbol-crossing of the modulated symbols are carried out to ensure higher diversity gain. By theoretical analysis, the designed scheme is proved to be full diversity. Finally, simulation results is presented and show that the proposed STBC-SM obviously improves the diversity gain and system performance.

Body area network (BAN) consists of intercommunicating sensors, which are either wearable, or can be implanted into the human body. Being a network that features critical information, BAN requires encryption, however common encryption methods found in computer networks are not ideal due to complex algorithms and high power consumption. The authors propose a new encryption method based on the human body channel. This new encryption method has the advantages of low-power, dynamic updating, rapid operation, and easy implementation, which is suitable for BAN systems.

In this paper, we investigate the robust power allocation problem in a two-tier orthogonal frequency division multiplexing (OFDM) access based heterogeneous networks, where the minimal rate requirement of each femtocell user and interference power constraint of macrocell user are properly controlled under channel uncertainties. We formulate an optimization problem to maximize the sum data rate of the femtocell users under the consideration of channel uncertainties of both cross-tier and intra-tier links while guaranteeing the rate requirement of femtocell users and controlling the interference to the macrocell users in terms of probability. According to the distribution models of estimation errors, we transform the original problem into a deterministic one and solve it by the subgradient method and Karush-Kuhn-Tuchker conditions. Simulation results show that our approach can fast converge to the optimal value and well protect the performance of macrocell users under channel uncertainties.

Spatial modulation (SM) is a promising multiple-input multiple-output (MIMO) transmission technology, which exploits the indices of transmit antennas to encode information into the spatial dimension and needs less radio frequency chains. To detect the transmitted information, the maximum likelihood (ML) algorithm is often adopted which has high computational complexity. Some low-complexity detection algorithms only consider hard-decision cases with no channel coding. In this paper, a low-complexity soft output detection algorithm is proposed for coded spatial modulation systems. Its computational complexity doesn't increase with the modulation order and hence much lower than the soft output ML detection algorithm. Simulation results demonstrate that the bit error rate (BER) performance is near to the ML detection with obvious complexity reduction. By sorting the detection metrics and selecting less transmit antenna indices as search candidates to compute soft bit information, further reduction of computational complexity could be obtained.

Traditional power allocation algorithms in heterogeneous networks (HeNets) are given under the assumption of perfect channel state information (CSI). However, the perfect CSI is difficult to obtain due to the efficiency of channel fading and feedback delays. In this paper, a robust power allocation algorithm is proposed to minimize the total transmit power of femtocell users under the constraints of macrocell interference power, maximum transmit power and signal-to-interference-noise-ratio (SINR). Though the original optimization problem becomes a semi-infinite programming problem (SIP) when using ellipsoidal uncertainty model. The SIP can be converted into a deterministic convex optimization problem by the worst-case approach, which can be easily solved in sub-gradient method. Simulation results show our proposal has good robustness and energy efficiency.

With the development of the fifth generation communication technology, how to improve system capacity and spectral efficiency is a key issue, which has attracted more and more attention from industry and academia. Heterogeneous network has been considered as a new promising technique for enhancing the quality of service, energy and spectrum efficiency as well as coverage of network due to different radio access technology (RAT) and different network structures. In this paper, the rate maximization resource allocation problem for multiuser cognitive heterogeneous networks is formulated to flexibly use network resource and improve the overall capacity, which simultaneously considers cross-tier interference constraint and maximum transmit power of cognitive microcell base station. The non-convex optimization problem is converted into a geometric programming problem which can be solved by Lagrange dual method in a distributed way. Simulation results are given to show the performance of the proposed algorithm in terms of the achievable system capacity and the interference to the macrocell network.

Airborne TIR remote sensing can obtain land surface temperature (LST) with high spatial resolution. However, the swath width of airborne stripes is usually limited. Therefore, it is necessary to generate the LSTs for a large area through temporal normalization of LSTs derived from different stripes. By selecting an agricultural oasis as the study area, this study compares the diurnal temperature cycle (DTC) model and polynomial regression (PR) technique in the temporal normalization of the LSTs derived from the Thermal Airborne Spectrographic Imager (TASI) data. The results show that the DTC model has better accuracy in normalizing the LSTs. However, the PR technique is simple and requires less ancillary data. The DTC method can normalize the LST to any specific time and generate temporally continuous LSTs, while the PR method can only do relative normalization. This study is helpful to reduce the temperature differences of different airborne stripes and obtain airborne LSTs with both high spatial and temporal resolutions.

The pulse wave signal contains a variety of noises and has strong nonlinear and non-stationary. According to the previous wavelet transformation method, this paper proposes a PPG signal de-noising algorithm based on dual-tree complex wavelet transform (DTCWT) and morphological filtering. With the advantage of simple construction, clear mathematical implications and low computational complexity, this method overcomes the shift sensitive and the frequency aliasing in the discrete wavelet transform. The simulation results show that this algorithm could remove the power line interference and EMG interference, and the quantitative index of SNR and mean square error is superior to the traditional threshold de-noising algorithm. Therefore, the DTCWT and morphological filtering de-noising algorithm would obtain a clear pulse wave signal.

The channel characteristics of the body area network are important to transmission rate and transmission robustness. Current explorations mainly concern the channel characteristics in static state of the human body. In this paper, we establish human motion models of sitting to standing, walking and running postures, and calculate the path loss values in 2.4GHz for the models. The experimental results show that the path loss values vary sharply in the process of human movement, which is very helpful to determine the transmission power and design BAN sensor nodes.

Partial interference cancellation (PIC) group decoding is an attractive method to reduce the maximum-likelihood (ML) decoding complexity of a layered Alamouti code. However, different grouping schemes for the PIC group decoding may have different performances. In this paper, by fully taking advantage of the Alamouti code structure, we analytically calculate the power gains of the information symbols after the PIC for a grouping scheme. We then propose an optimal grouping scheme for the PIC group decoding by proposing a criterion to choose the grouping schemes adaptively so that the power gains of the information symbols are maximized after the PIC.

For a two-user MIMO system, the decoding complexity of layered Alamouti code is high with the maximum-likelihood (ML) decoding while full diversity is achieved. Partial interference cancellation (PIC) and its combination with successive interference cancellation (PIC-SIC) group decoding, which group the symbols in one layered Alamouti code into several groups and decoding each group separately, provide low decoding complexity with sacrifice of full diversity. In this paper, we propose one space-time code for two-user multi-antenna systems with two antennas at each user and N(N ≥ 2) antennas at the receiver. Full diversity order 2N is proved to be achieved with the PIC-SIC group decoding by the analytical derivation of the symbol error rate. Simulation results presented confirms the derivation and its better bit-error rate (BER) performance over layered Alamouti code with the PIC and PIC-SIC group decoding.

For large cooperative wireless networks where direct links are limited in the neighbors of each node, we develop a diffusion automatic repeat request (ARQ) protocol based on selection cooperation. The number of potential relays for selection increases as the ARQ process continues and even the nodes that have no direct link with the source could cooperate with it. We analyze the diversity-multiplexing-delay (D-M-D) tradeoff performance of the protocol and demonstrate that the diffusion results in additional diversity benefit. The proposed protocol is suitable for delay-tolerant networks, such as wireless sensor networks and certain systems with relaxed delay constraints. It can also be used for highly reliable and loose latency services in the networks that support different quality of service (QoS) grade.

Through automatic repeat request (ARQ) mechanism, additional diversity benefit can be obtained for delay-tolerant wireless networks. In this paper, we combine the distributed selection cooperation protocols with ARQ mechanism to develop more powerful cooperative schemes and analyze their performance from the perspective of diversity-multiplexing-delay (D-M-D) tradeoff. We prove that directly extending the selection cooperation protocol with single round of feedback to multiple rounds does not lead to optimal D-M-D tradeoff. Then we develop an improved scheme achieving the upper bound of D-M-D tradeoff and also propose an efficient scheme that greatly reduces the number of feedbacks and almost achieves the optimal performance. The D-M-D tradeoff analysis and simulation results demonstrate the significant performance improvement of all the proposed schemes.