Spectral efficiency (SE) and energy efficiency (EE) are crucial for next-generation wireless Internet of Things networks. Device-to-device (D2D) communications and sparse code multiple access (SCMA) are promising solutions to meet the high Quality of Service. This requires a proper interference management for an ultradense user system. Considering both resource and power allocation, we herein aim to optimize the SE and improve the EE. First, rate fairness is considered to assign SCMA codewords to both cellular and D2D users. Subsequently, we propose a beamforming design and power allocation algorithm for D2D communications underlaying downlink and uplink operation of cellular networks, wherein energy harvesting is involved as a simultaneous wireless information and power transfer to assist an SCMA network.

Considering sporadic traffic in IoT networks, grant-free random access is inevitable. In a grant-free random access system, channel estimation and activity detection are crucial to enable data transmission. In this paper, we propose a deep learning-based activity detection scheme for mmWave grant-free IoT networks, and the detection accuracy is validated by the simulation result.

This paper provides a hybrid approach between Wi-Fi and pedestrian dead reckoning (PDR) for the iPhones in indoor environments, and addresses the following two problems. First, since Apple Inc. no longer provides public information about currently-connected Wi-Fi (e.g., service set identifier, received signal strength and channel), Wi-Fi based pedestrian tracking apps that run on the iPhones are more restricted compared to other ones on the Android platforms. Second, even the PDR approach provides such a great way for self-localization, it suffers from accumulated errors of inertial sensors embedded in the smartphones. We propose a conversion function from a Wi-Fi status value to a proximity for the localization purpose. Then, a mobile iPhone collects mobility information from inertial measurements unit (IMU), inputs to the PDR, and combines with Wi-Fi proximity in order to perform accurate self-localization and tracking. Moreover, we improve the PDR by reducing drifting effects caused by the IMU biases. Experiment results show that the proposed scheme is effective and has low complexity, while bringing the benefits from smartphone IMU.

Magnetic induction (MI) communication uses the mutual inductance between coil antennas to achieve the communication process. As MI communication is not affected by most factors of the propagation environment, we can achieve the detection and monitoring tasks through a stealth operation. In the MI system, the path loss is the most important parameter when estimating the channel and the communication range. The pipeline is used to transport the liquid, and thus it is a special scenario of the underwater communication. Since the index of refraction of the boundaries is different, there are three possible scenarios at the boundaries, i.e. semi-reflection, total reflection, and no reflection. Hence, the distribution of the magnetic field is changed and the path loss is difficult to be estimated. In this paper, we build an MI-based software-defined radio (SDR) system testbed in a water tank to simulate the underwater pipeline. Then, we adopt a deep neural network (DNN) with supervised learning to estimate the path loss of the MI communication. Also we discuss the communication range in the theoretical path loss model and our proposed model.

Since chirp spread spectrum (CSS) system has robustness to reduce multipath fading effect and to extend spectrum bandwidth with low energy consumption, the CSS system has attracted much attention not only for radio detection and ranging systems but also for commercial wireless communication systems. In the spread-spectrum systems, low probability of detection (LPD) and anti-jamming (AJ) abilities are two key issues. In this paper, a multiple linear chirp (MLC)-based binary orthogonal keying (BOK)-CSS system considering cross-correlation coefficient for improving the LPD and the AJ performances is proposed. In order to analyze the performance of the MLC-based BOK-CSS system, the cross-correlation coefficient between two different chirps is analytically derived according to the time-bandwidth separation product, and it is numerically compared to a Monte Carlo simulation. From the results, the proposed MLC-based BOK-CSS system guarantees higher survivability than the conventional schemes by considering the cross-correlation coefficient according to the bandwidth separation.

This study focuses on indoor localization of a smartphone (e.g., iPhone 7) by using its inertial sensors and a currently-connected Wi-Fi access point (AP). In the situations where the density of APs is low, the inertial sensors provide a way to localize the users smartphone, while the AP plays as a reference point and an additional source to provide a starting point for the user tracking its movements. We propose a new indoor localization scheme called the pedestrian dead reckoning with step compensation that can be implemented on mobile devices with limited resources. In order to test our localization scheme, we perform several experiments to track a mobile user in one of the largest shopping malls in Korea using public Wi-Fi network services. The results show that the successful localization percentage is about 92% within 2 m error, which is highly accurate with acceptable measurement errors.

A conventional relaying system is based on an orthogonal two‐phase transmission protocol where time slots for the source (the first phase) and relays (the second phase) are exclusively allocated to avoid interference. For this reason, the conventional relaying schemes suffer from the reduction in spectral efficiency. In order to overcome this drawback, a parallelised relaying protocol with simultaneous transmission has been proposed. However, simultaneous transmission may not be a good strategy in such a strong cross‐relay interference. In this study, the authors propose a parallelised relaying protocol with an opportunistic priority assignment. In the proposed scheme, they consider the cognitive radio technique as a way to improve the spectral efficiency of the conventional parallelised relaying protocol in the cross‐relay interference. They evaluate the instantaneous capacity of the proposed scheme through computer simulations and conclude that their scheme can improve the spectral efficiency of the conventional relaying systems significantly.

This paper considers device-to-device (D2D) communications underlaying an uplink cellular network that adopts sparse code multiple access technology. We aim to maximize the spectral efficiency for D2D links in a transmission block via a joint power and resource allocation subject to the quality-of-service requirements for both cellular user equipments and D2Ds. We propose a low-complexity two-phase algorithm based on heuristic search and inner approximation method to efficiently solve the problem.

In this paper, we propose a selective Gaussian approximation to reduce the complexity of message passing algorithm (MPA) detection for uplink sparse code multiple access (SCMA) systems with multiple antennas. A subset of resource nodes, associated with antennas and subcarriers, are selected and the conventional MPA detection based on maximum likelihood (ML) is applied to those nodes. For the remaining resource nodes, Gaussian approximation is performed instead of ML. We first propose a simple but efficient antenna-subcarrier selection algorithm, and then develop a method to combine the two MPA detection schemes based on ML and Gaussian approximation. Numerical results are presented to show that the proposed MPA detection scheme provides an efficient tradeoff between the performance and receiver complexity.

In radio frequency (RF) energy harvesting (EH) cognitive radio network (CRN), the EH secondary users (SUs) access an idle channel to transmit data and an occupied channel to harvest energy. Therefore, the EH SUs can reliably transmit data only if sufficient energy and an idle channel are available. In this paper, we analyze the probability that the EH SUs may completely run out of energy and the achievable throughput of the EH SUs is derived accordingly. To improve the throughput of the SUs, we consider a 2-channel sensing scheme that the EH SUs are allowed to sequentially sense up to 2 channels to further search for the opportunities for data transmission. Consequently, the opportunities for data transmission increase, while less opportunities are available for energy harvesting. To validate the proposed analysis, we use the Monte-Carlo simulation to show an agreement between the analytical and simulated values, and the simulation results turn out to be reasonably acceptable.

Node localization in wireless sensor networks (WSNs) aims to determine unknown target position given anchors' known positions and available relative pairwise measurements among directly-connected neighboring nodes. From this point of view, WSNs can be divided into two groups based on collaboration between targets: cooperative networks in which the measurements between the targets are also used for positioning procedure, and non-cooperative networks in which only measurements between pairs of anchor nodes and target nodes are utilized. Obtaining a good estimation of where targets are located at is critical in sensor network applications such as monitoring, geographic routing, and industrial manufacturing. Since the positions of the targets are unknown and only local distance information is given, we need to find true positions from these local distance measurements. Maximum likelihood estimation is one of the state-of-art method for this problem, which is non-convex and consumes much effort to solve. Moreover, the quality of obtained solution is highly dependent on the chosen search methods and the used initial points. In this paper, we formulate the non-cooperative sensor network localization as a convex feasibility problem where the unknown node belongs to a sufficient intersection set of a family of closed convex sets, then apply a projection method to find the solutions. This approach leads us to the use of different algorithm which is fast, robust and convergent. With an appropriate step-size sequence, estimated sensor locations will converge to the global optimal solution in a finite number of steps, as shown by the numerical results. The proposed approach has good stability, robustness and lower complexity, which is effective for the localization problems with small sensor size constraints.

In this paper, we propose an inter-cell interference management scheme using a multi-cell SRN (Shared Relay Node) in order to avoid inter-cell interference and to increase the cell throughput in 3GPP LTE (3rd Generation Partnership Project Long Term Evolution)-Advanced networks. Recently, the CoMP (Coordinated Multi-Point transmission/reception) scheme has received growing attention as a promising scheme for overcoming the inter-cell interference in 3GPP LTE-Advanced networks. In practice, however, when feedback delays and channel estimation errors are considered, the performance of the CoMP scheme may severely degrade. The objective of this paper is to present a new SRN architecture located at the neighboring area of adjacent cells so as to manage the inter-cell interference. The SRN can overhear control signals transmitted by adjacent eNBs (enhanced Node-B) and UEs (User Equipments). Based on the overheard information, the SRN not only performs data retransmission as a usual type II relay but also manages the inter-cell interference. In particular, we suggest a resource allocation scheme and a power control scheme assisted on the SRN for effective interference avoidance, and evaluate their performances through simulations.

As a scheme to efficiently reduce the effects of multipath fading in next generation wireless communication systems, cooperative communication systems have recently come into the spotlight. Since these cooperative communication systems use cooperative relays with diverse fading coefficients to transmit information, having all relays participate in cooperative communication may result in unnecessary waste of resources, and thus relay selection schemes are required to efficiently use wireless resources. In this paper, we propose an efficient relay selection scheme through self-learning in cooperative wireless networks using Q-learning algorithm. In this scheme, we define states, actions and a reward to achieve good SER (Symbol Error Rate) performance, while selecting a small number of cooperative relays. When these parameters are well-defined, we can obtain good performance. The simulation results show that, compared to a scheme that obtains optimum numbers of relays through a mathematical analysis, the proposed scheme uses resources efficiently by using smaller numbers of relays with comparable SER performance. In particular, whereas the number of cooperative relays linearly increases as the number of relays that can participate increases in the case of the scheme modeled by a mathematical analysis, the proposed scheme selects 2.5 relays on average for cooperative communication. According to these simulation results, the proposed scheme can be considered as a good attempt for future communication.

We present an effective scheme for preventing eavesdropping in wireless communication systems. In the proposed scheme, the modulation scheme is designed to change based on an expanded signal constellation, in such a way that the eavesdropper cannot be aware of the change. As compared to previously proposed schemes that rely on specific relay nodes, the proposed scheme is advantageous in that it can keep the transmit messages secure without requiring any assistance of relays.

In order to overcome fading in wireless channels and increase the performance of wireless systems, cooperation is needed between the source node and relay node. In a cooperative wireless communication system, PA (Power Allocation) and RS (Relay Selection) are important issues for improving performance of the system. In particular, PA is an effective way to save transmit power and improve the coverage and capacity of the system. Some papers analyze full CSI (Channel State Information) for obtaining optimal power allocation. However, this makes the system increasingly complex and presents a practical problem. Thus, there is a need for partial CSI to reduce the complexity. With PSO (Particle Swarm Optimization), individuals referred to as particles are “flown” through hyper-dimensional search space. Changes to the position of a particle within a swarm are influenced by the knowledge of its neighbors. The PSO algorithm maintains a swarm of particles, where each particle represents a potential solution. Therefore, we propose an improved scheme for power allocation in relay-based cooperative wireless communications. For this purpose, we employ partial CSI and PSO algorithms to solve the optimization problem of power allocation with reduced system complexity as well as optimized performance.

In this paper, we investigate the performance of opportunistic scheduling in uplink cognitive radio (CR) systems taking into account the spectrum sensing reliability of each CR user. In uplink CR systems, each secondary transmitter (CR user) has a spectrum sensing responsibility to protect the primary system, and the secondary receiver (base station) schedules the transmission opportunity for each CR user. Unlike non-CR multi-user systems, the spectrum sensing reliability may degrade the achievable multi-user diversity (MUD) gain. We propose novel scheduling schemes taking into account the spectrum sensing reliability and data channel quality simultaneously to improve the MUD gain in uplink CR systems.

We propose a new cognitive beamforming scheme for coexistence of IR (incumbent radio) CR (cognitive radio). The proposed cognitive beamforming scheme does not cause any interference to the IRU (IR User), while the CRU (cognitive radio user) is served without additional radio resource consumption. Simulation results show that when using the proposed scheme the CRU does not interfere to the IRU and there is little bit error rate performance degradation of the CRU as compared to that of the ideal beamforming system which does not consider the IR priority.

In mobile WiMAX systems, uplink collaborative MIMO (multiple input multiple output) performs spatial multiplexing with two PSSs (portable subscriber stations), each with one antenna. As two PSSs transmit collaboratively on the same sub-channel, the overall uplink capacity will be doubled. To perform this interesting technique with high performance, most system venders demand the optimal MLD (maximum-likelihood detection) in the RAS (radio access station). However, the MLD is difficult to implement due to its explosive computational complexity. In this paper, we propose a two-step MIMO decoding scheme which achieves near ML performance with low complexity for CSM (collaborative spatial multiplexing) in uplink mobile WiMAX systems that having an iterative channel decoder using bit LLR (log-likelihood ratio) information. Simulation results show that the proposed scheme has almost the same BLER (block error rate) performance of the MLD with only about 15.75% computational complexity in terms of real multiplication, when both PSSs transmit with 16 QAM (quadrature amplitude modulation) modulation.

We propose a selective detection scheme based on pulse repetition for coherent IR-UWB (impulse radio-ultra wide band) systems. For signal modulation and demodulation, we consider bi-phase modulation and on-off keying that are widely used in the coherent IR-UWB systems. The proposed selective detection scheme transmits the UWB signals by pulse repetition at the UWB transmitter to take system complexity into account, like conventional PRC (pulse repetition coding). However, the proposed scheme performs selective detection by estimating the SNR (signal-to-noise ratio) of the received pulse-repeated signal to effectively improve BER (bit error rate) performance at the UWB receiver, unlike the conventional PRC. Simulation results in the IEEE 802.15.4a channel models reveals that the proposed scheme effectively improves BER performance of the coherent IR-UWB systems without much increment of system complexity, as compared to the conventional PRC.

In a wireless channel with a large delay spread, a detection probability of conventional TDoA (Time Difference of Arrival) positioning algorithms cannot satisfy the U.S. E-911 (Enhanced-911) standard. In this paper, we propose an improved TDoA-based tracking algorithm for mobile-WiMAX (Worldwide Interoperability for Microwave Access) systems. The position of a MS (Mobile Station) is estimated by the network-based TDoA location scheme when the MS is located in the GPS (Global Positioning System) shadow area. The proposed algorithm compares the estimated position of a current state to the previous states, for achieving high reliability. Simulation results show that the proposed algorithm can improve the detection probability about 10% points, compared to the conventional TDoA-only positioning algorithm in the ITU-R (International Telecommunication Union-Radiocommunication sector) micro-cell environment, using the Pedestrian-B channel model which has a large delay spread profile. The proposed scheme's improvement helps to satisfy the E-911 standard when utilizing information from three RASs (Radio Access Stations).

We present an adaptive transmission scheme for the CR (cognitive radio) systems. The proposed scheme estimates the distance between a CR user and an incumbent user utilizing IT (interference temperature) information recognized through the spectrum sensing procedure. Then, the CR user determines the maximum transmission power which does not cause any interference to the incumbent user by using CR-APC (CR-adaptive power control). At the same time, in order to provide the maximum throughput to the CR user, the proposed scheme adaptively selects the modulation order considering the determined transmission power and the IT value by applying CR-AMC (CR-adaptive modulation and coding) in the given channel state. Hence, wireless communication equipments using the proposed scheme are able to adaptively transmit the data without interference on the incumbent users in various channel environments.

We propose a selective detection scheme based on pulse repetition considering Bit Error Rate (BER) performance and complexity of noncoherent Ultra Wide Band (UWB) systems. To consider system complexity, the proposed scheme transmits the UWB signals by pulse repetition at the transmitter, like the conventional Pulse Repetition Coding (PRC). However, to effectively improve BER performance of the system, the proposed scheme performs selective detection by estimating the Signal-to-Noise Ratio (SNR) of the received pulse-repeated signal at the UWB receiver. Hence, the proposed scheme effectively improves BER performance of the noncoherent UWB systems without increase of the system complexity, as compared to the conventional PRC algorithm.

We propose a clipped companding scheme to effectively reduce PAPR (peak-to-average power ratio) of OFDM (orthogonal frequency division multiplexing) signals. The proposed scheme is based on the companding to take both complexity and BER (bit error rate) performance into account, and composed of the clipping followed by the companding. In the first step of the proposed scheme, some peak signals which do not influence the BER, are clipped. In the second step, the proposed scheme utilizes the mu-law companding to effectively reduce the PAPR Simulation results show that the proposed scheme provides more PAPR reduction without degradation of the BER performance as compared to the conventional mu -law companding.

We propose a CCP (companding combined with predistorter) scheme that maintains BER (bit error rate) performance, while effectively reducing PAPR (peak-to-average power ratio) of OFDM (orthogonal frequency division multiplexing) signals. The proposed CCP scheme combines the conventional companding scheme proposed by the authors with the predistorter, in order to prevent degradation of BER performance caused by signal distortion from nonlinear property of the HPA (high power amplifier) and the compression property of the companding scheme. Hence, the proposed scheme effectively reduces the PAPR and simultaneously maintains the BER performance independent of nonlinear property of the HPAs. Simulation results show that the proposed scheme provides the lower total degradation for all the modulations, when compared with the conventional companding scheme proposed by Wang and the clipping scheme.

In this paper, we propose P-SLM (partitioned-selective mapping) scheme with low complexity for PAPR (peak-to-average power ratio) reduction of OFDM (orthogonal frequency division multiplexing) signals. In the proposed scheme, a symbol sequence in frequency domain is partitioned into several sub-blocks, and each sub-block is multiplied by different phase sequences whose length and number are shorter and smaller than those used in the conventional SLM. Then, among statistically independent sequences in time domain generated after the IFFT (inverse fast Fourier transform) for the phase rotated sub-blocks, the sub-block combination with the lowest PAPR is selected and transmitted. Simulation results reveal that the proposed P-SLM scheme can remarkably reduce the number of IFFT calculation and multiplication than the conventional SLM without loss of PAPR reduction performance

In this paper, we propose the ADRX (adaptive discontinuous reception) mechanism with the EPI (extended paging indicator) for power saving in the UMTS (universal mobile telecommunications system). Regardless of various traffic patterns and network situations, the conventional FDRX (Fixed DRX) mechanism currently specified by 3GPP (third generation partnership projects) employs only fixed DRX period with a given DRX cycle length of the same length in which each UE (User Equipment) is to have one paging occasion with the uniquely allocated PI. However, improvement of power saving performance cannot be expected since this FDRX does not support longer DRX periods to save more power consumed in the UE for intermittent data packets. In the proposed ADRX mechanism, to solve this problem and efficiently reduce the power consumption, DRX period is easily controlled by the EPI which is decided by Node B considering the current traffic situation. In addition to its original functions to indicate paging messages, the proposed EPI also includes four extended functions; preservation, increase, decrease, and reset of the DRX periods. Simulation results reveal that the proposed ADRX mechanism achieves remarkable improvement of power saving performance as compared to the conventional FDRX mechanism. Furthermore, it is also observed that the proposed ADRX based on the EPI has a trade-off relationship between power saving performance and packet transmission reliability.

A joint estimator for OFDM symbol timing and fractional frequency offsets is proposed based on cyclic shifting and autocorrelation properties of PN (pseudo noise) codes. In the proposed scheme, for the joint estimation of both timing and frequency offsets, a newly designed time domain preamble sequence which has the same length with the number of IFFT points and consists of twice-repeated PN codes, is utilized. Moreover, in order to achieve robustness in multipath fading channels, a new timing metric by considering delay spread of the multipath channel is also applied. Simulation results show that the proposed estimation scheme is more robust to multipath channels than the conventional OFDM symbol timing estimation schemes. Furthermore, the proposed joint estimator also provides quite reliable fractional frequency estimation.

In this work, we propose a rapid acquisition scheme for ultra wide band (UWB) signals in indoor wireless channels. The proposed acquisition scheme is based on the bit reversal search combined with a variant of the double-dwell search in which two-step thresholds and search windows are employed. Simulation results show that the proposed two-step acquisition scheme for the UWB signals can achieve remarkable reduction of the required mean acquisition time as compared to general double-dwell search schemes.

The paper proposes a JBSTBC (joint beamforming space-time block coding) scheme, for MIMO (multi-input multi-output) communication systems. To enhance the order of spatial diversity in the presence of deteriorative fading correlation as well as inter-substream interference, the proposed JBSTBC method employs a joint eigen-beamforming technique together with the block ordered STBC decoding scheme. In order to confirm the superiority of the proposed JBSTBC method, computer simulations are conducted in highly correlated fading situations with detailed mathematical derivations provided to clarify the functionality of the proposed scheme.

We present a predistorter to compensate for nonlinear distortion with memory introduced by linear pulse shaping filters and nonlinear high power amplifiers (HPAs) employed in multi-code code division multiple access (CDMA) systems. Since the multi-code CDMA system has multi-level properties in its signal amplitude, the performance of the system is significantly degraded by the nonlinear effect of the HPA in the transmitter. Furthermore, the memory of the linear filter employed in the multi-code CDMA transmitter generates intersymbol interference. In order to mitigate these distortions, we develop a new data predistorter, which rests upon the fixed point approach associated with the contraction mapping theorem. With utilization of the proposed predistorter, we can significantly improve the performance of the multi-code CDMA system compared to that of the system without the predistorter in terms of bit error rate.

In this paper, we present a predistorter for compensating for nonlinear distortion introduced by high power amplifiers (HPAs) employed in multi-code code division multiple access (CDMA) systems. Since the multi-code CDMA system has the multi-level property in its signal amplitude, the performance of the system is severely degraded by the nonlinear effect of the HPA in the transmitter. In order to mitigate this undesirable distortion, we develop a new data predistorter, which rests upon the fixed point approach associated with the contraction mapping theorem. With utilization of the proposed predistorter, we can significantly improve the performance of the multi-code CDMA system compared to that of the system without the predistorter in terms of bit error rate. Furthermore, since the proposed predistorter employs an iterative method, its implementation is relatively simple.

This paper presents a polynomial connectionist network called ridge polynomial network (RPN) that can uniformly approximate any continuous function on a compact set in multidimensional input space R (d), with arbitrary degree of accuracy. This network provides a more efficient and regular architecture compared to ordinary higher-order feedforward networks while maintaining their fast learning property. The ridge polynomial network is a generalization of the pi-sigma network and uses a special form of ridge polynomials. It is shown that any multivariate polynomial can be represented in this form, and realized by an RPN. Approximation capability of the RPN's is shown by this representation theorem and the Weierstrass polynomial approximation theorem. The RPN provides a natural mechanism for incremental network growth. Simulation results on a surface fitting problem, the classification of high-dimensional data and the realization of a multivariate polynomial function are given to highlight the capability of the network. In particular, a constructive learning algorithm developed for the network is shown to yield smooth generalization and steady learning.

This paper introduces a class of higher-order networks called pi-sigma networks (PSNs). PSNs are feedforward networks with a single “hidden” layer of linear summing units and with product units in the output layer. A PSN uses these product units to indirectly incorporate the capabilities of higher-order networks while greatly reducing network complexity. PSNs have only one layer of adjustable weights and exhibit fast learning. A PSN with K summing units provides a constrained Kth order approximation of a continuous function. A generalization of the PSN is presented that can uniformly approximate any continuous function defined on a compact set. The use of linear hidden units makes it possible to mathematically study the convergence properties of various LMS type learning algorithms for PSNs. We show that it is desirable to update only a partial set of weights at a time rather than synchronously updating all the weights. Bounds for learning rates which guarantee convergence are derived. Several simulation results on pattern classification and function approximation problems highlight the capabilities of the PSN. Extensive comparisons are made with other higher order networks and with multilayered perceptrons. The neurobiological plausibility of PSN type networks is also discussed.