Integrated sensing and communication (ISAC) with orthogonal frequency division multiplexing (OFDM) waveform has been expected to be a key technique in the future 6th generation ( 6 G ) communication networks. In OFDM ISAC systems, joint range-angle estimation (JRAE) of targets is a essential requirement. Current works focus on far-field target sensing, while this paper studies JRAE for both near-field and far-field targets. First, the signal model is established and the Cramér-Rao bounds on JRAE and localization are derived. Then, an auto-paired high-accuracy JRAE method that applies to both near-field and far-field targets is proposed. Specifically, the proposed method consists of two stages. First, it performs frequency smoothing on the observation matrix to obtain multiple observation submatrices. Then, range estimation is performed by using the translational invariance of the submatrices, while angle estimation is achieved by utilizing the orthogonality between the noise subspace and the steering vectors. Under the 5G New Radio standard parameter setup, simulation results demonstrate that the proposed method achieves superior estimation performance, with its root mean square error approaching the root of CRB compared to the conventional methods. Specifically, at a signal-to-noise ratio (SNR) of 0 dB, the proposed method reduces the root mean square error in terms of range, angle and location estimation by 65.8%, 62.8% and 65.9%, respectively, compared to the benchmark schemes.

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for the future sixth-generation mobile communication systems. The joint sensing of target range and velocity is crucial in OFDM-based ISAC systems. When the targets are highly correlated with similar range and velocity, it is challenging for the conventional two-dimensional subspace-based sensing methods to achieve accurate joint range-velocity estimation (JRVE), particularly in the low signal-to-noise ratio (SNR) region. As such, this paper proposes a high-resolution JRVE method. Specifically, the proposed method first applies equal interval sampling smoothing to the observation signal, introducing ambiguity in range-velocity-induced phase pairs. It then utilizes the translation invariance of the signal subspace to extract these ambiguous phase pairs. Finally, it leverages the orthogonality between the constructed steering vector pair and the noise subspace to resolve the ambiguity and achieve accurate estimation. Under a 5G New Radio parameter setup, simulation results demonstrate that the proposed method significantly outperforms conventional methods in terms of both resolution and accuracy. At an SNR of -10 dB, the proposed method reduces the root mean square error of range and velocity estimation by 83.7 % and 87.3 %, respectively, compared to the benchmark scheme, while its computational cost is less than 50 % of that of the benchmark.

For 5G NR-based integrated sensing and communication (ISAC) systems, using the reference signal for sensing has attracted fast-growing attention, as it does not degrade communication performance. Existing works focus on utilizing pilot or positioning reference signal for sensing. In contrast, this paper concentrates on sensing exploiting demodulation reference signal (DM-RS) in physical downlink shared channel (PDSCH). First, we establish a framework in 5G NR-based ISAC systems using DM-RS in PDSCH for wireless sensing. However, a ghost target phenomenon will occur, due to the inherent periodic time-frequency structure and sparsity of DM-RS. Then, to address this challenge, a linear interpolation-based method is proposed, which applied linear interpolation on the target information matrix. Finally, simulation results demonstrate that leveraging DM-RS for sensing is a effective scheme, and the proposed interpolation method can mitigate ghost target phenomenon efficiently.

Integrated sensing and communication (ISAC) utilizing orthogonal frequency division multiplexing (OFDM) wave-forms is emerging as a critical technology for forthcoming sixth-generation mobile communication networks. The joint sensing of target range, velocity and azimuth is essential for OFDM-based ISAC systems. When the targets are highly correlated with similar range, velocity and azimuth, it is challenging for the conventional three dimensional subspace-based sensing methods to achieve accurate joint range-velocity-azimuth estimation (JRVAE), particularly in the low signal-to-noise ratio (SNR) region. Thus, this paper focuses on high-resolution JRVAE for highly correlated targets. First, a signal model is established, and the Cramér–Rao bounds for JRVAE are derived, considering communication symbols belonging to an arbitrary-order quadrature amplitude modulation constellation. Then, a high-resolution JRVAE method is proposed. Specifically, it first performs equal interval sampling smoothing on the observation signal, resulting in ambiguity in range-velocity-azimuth-induced phases, then uses the translation invariance of the signal subspace to extract the ambiguous phases, finally utilizes the orthogonality between the constructed steering vector pair and the corresponding noise subspaces to resolve ambiguity and obtaining accurate estimation. With 5G New Radio parameters setup, simulation results shows that the proposed method achieves higher resolution and accuracy compared to the conventional methods. At an SNR of -10 dB, the proposed method reduces the root mean square error in range, velocity, and azimuth by 76.0%, 82.7%, and 73.2%, respectively, compared to the benchmark scheme, while its computation cost is less than 1/3 of that of the benchmark.

Integrated sensing and communication (ISAC) is a main application scenario of the sixth-generation mobile communication systems. Due to the fast-growing number of antennas and subcarriers in cellular systems, the computational complexity of joint azimuth-range-velocity estimation (JARVE) in ISAC systems is extremely high. This paper studies the JARVE problem for a monostatic ISAC system with orthogonal frequency division multiplexing (OFDM) waveform, in which a base station receives the echoes of its transmitted cellular OFDM signals to sense multiple targets. The Cramér-Rao bounds are first derived for JARVE. A low-complexity algorithm is further designed for super-resolution JARVE, which utilizes the proposed iterative subspace update scheme and Levenberg-Marquardt optimization method to replace the exhaustive search of spatial spectrum in multiple-signal-classification (MUSIC) algorithm. Finally, with the practical parameters of 5G New Radio, simulation results verify that the proposed algorithm can reduce the computational complexity by three orders of magnitude and two orders of magnitude compared to the existing three-dimensional MUSIC algorithm and estimation-of-signal-parameters-using-rotational-invariance-techniques (ESPRIT) algorithm, respectively, and also improve the estimation performance.

The non-linear distortion caused by non-ideal radio frequency (RF) components especially the power amplifier (PA) limits the applications of higher order modulation and degrades power utilization efficiency. To improve the achievable rate in modern systems, it becomes critical to overcome the non-linear distortion so that we can maximize the opportunity of using higher order modulation such as 256QAM, 1024QAM and even 4096QAM at high transmission power. In this paper, we introduce an artificial intelligence (AI)-enabled non-linearity compensation scheme (AI-NC) to avoid the "model deficit" problem. The introduced AI-NC adapts the Echo State Network (ESN) to enable fast online training without additional training overhead. Furthermore, it is general enough to be used for any types of power amplifiers (PAs) with different non-linearity characteristics and different channel environments. It can also be used for the communication system using multiple antennas and supporting multiple users simultaneously. Simulation results and hardware-based tests show that the proposed AI-NC can drastically improve the link performance and/or coverage of higher order modulations in practice.

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for future sixth-generation mobile communication systems. For OFDM-based ISAC systems, it is important to accurately sense the target’s parameters. This paper studies the three-dimensional joint estimation (3DJE) of range, velocity, and azimuth for OFDM-based ISAC systems with multiple receive antennas. First, we establish the signal model and derive the Cramér–Rao bounds (CRBs) on the 3DJE. CRBs are widely used benchmarks that provide the theoretical lower bounds of the variances for unbiased estimation. Furthermore, an auto-paired super-resolution 3DJE algorithm is proposed by exploiting the reconstructed observation sub-signal’s translational invariance property in the delay, Doppler, and angle domains. Finally, with the 5G New Radio parameter setup, simulation results show that the proposed algorithm achieves better estimation performance and its root mean square error is closer to the square root of CRBs than existing methods.

Full duplex (FD) communication is considered as a promising technology in the development of 5G-advanced and 6G systems as it theoretically doubles the capability of channel. However, this improvement relying on a simultaneously bidirectional manner of communication induces intrinsic self-interference (SI) demanding to be fully cancelled, which is generally intractable. This challenge is minimized in the scenario of integrated access and backhaul (IAB) networks operated in millimeter wave (mmW) band, as its transceivers are stationary and the complexity of SI is greatly reduced by beamforming technique. As a matter of fact, FD can be a pioneering technique to unlock the full potential mmW-based IAB network by releasing its suffering of the half-duplex inefficiency. This article presents the design principle and validation of a practical SI cancellation (SIC) technique in the case of high transmission power class in mmW-based IAB networks. The proposed technique sequentially eliminates SI from the spatial, RF, and digital domains that reduces the SI down to the noise floor. To validate the technique, a prototype system is developed in accordance with 5G IAB specifications and field tests are conducted. Results suggest that the designed mmW SIC transceiver significantly reduces residual-interference to noise ratio (R-INR) to 2.1 dB or less. Additionally, a system-level simulation is conducted in line with 5G IAB evaluation methodology, which explores the potential performance gain of the proposed technique in presence of cross-link interference (CLI). Results indicate that the method could yield a cell throughput gain of about 84%, compared to the current time division duplex deployment.

The ever-increasing roll-out of 5G integrated access and backhaul (IAB) networks in millimeter wave (mmW) band and its current hard-constraint of half duplex operation motivate the study of full duplex (FD) communication in a high power beamforming system. In an mmW IAB network using FD, the intrinsic self-interference inherits the nature of mmW high attenuation and cross link interference can be quite manageable via sharp beamforming between fixed nodes. As a matter of fact, mmW IAB could be a pioneering use case of FD in near future. In this article, a practical self-interference cancellation (SIC) transceiver design is presented targeting for “down to noise floor” SIC in the case of high transmission power class. We developed an mmW prototype testbed system in accordance with 5G IAB specifications. Our experimental results via prototype verifies the feasibility of the designed mmW SIC transceiver and confirms the potential of 2 times throughput gain from FD compared with conventional half duplex in an IAB scenario.

Full duplex (FD) communication is an enabling technology with simultaneous uplink and downlink transmission over the same spectrum, which could not only virtually double the spectrum but also shorten the latency of bi-directional communications. One of the key challenges to bring full duplex into reality is how to design a practically implementable self-interference cancellation (SIC). This paper promotes a practical joint-design of SIC capable of >120dB SIC gain to make in-band FD practically viable. It consists of novel integrated SIC antenna, multi-tap tunable RF SIC and non-linear digital SIC. Further, a prototype system implemented using in-band FD with 5G NR commercial level hardware components is presented, which not only verifies in-band FD is practically implementable with the proposed joint SIC, but also achieves highest SIC results, i.e. 122.5dB SIC capability with 32dBm transmit power, to our best knowledge. This result confirms the appealing potential of in-band FD and conclusively, the in-band FD communication with the developed effective SIC turns out to be a promising enabler for future business thriving.

In this article, in order to substantially improve outage probability (OP), we propose a generalized optimal relays subsets selection algorithm in a cloud-based 6G large-scale relays network. We derive OP based on relays subsets selection for first hop and second hop, respectively. In addition, with the aid of the proposed power allocation method, we put forward optimal relays subsets paring algorithm. The generalized model can be converted into various relays selection algorithms, such as best relay selection,${N}$th best relay selection, partial relays selection, various relays subsets selection, and so on. Simulation results show that the proposed multiple relays selection algorithm has 2–6 orders of magnitude advantage than existing selection algorithms on OP.

The explosive growth of Internet of Things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical-layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN), and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce the running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.

With the maneuverability and mobility control of unmanned aerial vehicle (UAV), carrying mobile edge computing (MEC) servers on UAVs is able to effectively alleviate the explosive growth of data traffic pressure. However, UAV adopts line-of-sight transmission which has broadcasting characteristics. Malicious eavesdroppers can easily take advantage of the characteristics to eavesdrop information during the UAV edge computing. Therefore, the security of the UAV-MEC systems is a challenging problem. This article proposes a secure communication scheme for the dual-UAV-MEC system. In the proposed scheme, UAV server assists ground users in calculating the offloading tasks. In order to reduce the eavesdropping of offloading information by UAV eavesdropper, jammer sends interference signals on the ground. We aim to maximize the user's minimum secure calculation capacity by optimizing resources and trajectory of the UAV server. We first transform the optimization problem into a tractable form through mathematical methods and use successive convex approximation and block coordinate descent algorithms to solve it in an iterative manner. The final numerical results show that, compared with the benchmark schemes, the method proposed in this article effectively increases the secure calculation capacity of the system.

This paper is focused on the fusion of radar and wireless communication, called RadCom, which has been extensively studied in recent years for future intelligent transportation systems. We propose a new waveform design algorithm for reducing peak-to-average power ratio (PAPR) in OFDM-based RadCom systems. We consider a flexible and generic RadCom structure in which a number of non-contiguous sub-bands for data transmission are located within a large contiguous spectrum band for radar detection/sensing. New RadCom waveforms with low PAPR are obtained by carrying out optimization over those subcarriers which are complementary to the communication bands. As an application of the majorization-minimization (MM) optimization method, our major contribution is an$l$-norm cyclic algorithm which is capable of efficiently reducing the maximum PAPR of RadCom waveforms. We show by numerical simulation results that significant performance enhancements can be achieved compared to OFDM RadCom waveforms from legacy approaches.

Abstract Deep learning (DL) has been applied to digital signal modulation identification (DSMI) due to its powerful feature learning ability. However, most of the existing DL‐based DSMI methods are limited to specific experimental scene relating to the additive white Gaussian noise (AWGN) channel or static multipath channel. The result is that the trained network has deteriorative identification accuracy when the channel conditions change unless retrained. To solve the problem, this paper proposes a DSMI method suitable for orthogonal frequency division multiplexing (OFDM) under different multipath channels, including the variation of delay, path number and channel coefficient. This method can accurately detect the modulation feature rather than the channel's to identify the modulation type, thus reducing the network training amount. The method is divided into two parts. Firstly, traditional signal processing methods are combined, including various channel estimators and equalisers to compensate for the channel. Then a robust DL network, RSN‐MI, is designed as a classifier. Unlike other DL‐based DSMI methods, the influence of signal processing algorithms on DSMI performance are focused on rather than model parameters. Besides, the proposed classifier is compared with the DSMI classifier in other contributions. The results show that the classifier works better in different multipath channels.

In view of noteworthy communications performance improvements for future B5G/6G ubiquitous networks (such as cognitive Internet of Things (IoT) network, UAV communications, air-space-ground integration network, and so on), cooperative communications (CCs) diversity with relays selection algorithms has been extensively studied to significantly improve communications quality. In light of unsolved millennium issue in CC—nondeterministic polynomial (NP) and NP-hard problems have not been solved efficiently for relays subsets selection, in this article, theorems of relays subsets with${K}$-layers power allocation standard have been further put forward to explore better performance in B5G/6G networks. We propose unified layers-based optimized mobile relays subsets algorithms for full-duplex (FD) nonorthogonal multiple access (NOMA) to greatly improve transmission rates. After taking into account fundamental properties of relays, such as mobile relays nodes state, relays locations, fading characteristics, and so on, optimized FD-NOMA algorithm based on these relays features has been presented to improve transmission rates, and a related series of relays subsets theorems has been derived and proved, then minimum upper bound of maximum transmission rates is estimated to reveal two-way balanced optimal transmission conclusion for FD-NOMA. Furthermore, simulation results show that proposed algorithm has 1–3 dB advantage than other relays subset algorithms for signal to interference and noise ratio (SINR), and it can more efficiently transform NP-hard problem to P problem for relays selection. Importantly, under revealed two-way balanced optimal transmission phenomenon for FD-NOMA, the proposed scheme with FD-NOMA has more than two times minimum device-to-device (D2D) transmission rates higher than other classical relays selection algorithms.

Sixth generation (6G)-enabled Internet of Things (IoT) needs sufficient spectrum resources to provide spectrum access for massive IoT’s terminals. However, traditional orthogonal multiple access restricts the full use of limited spectrum resources. In this article, a nonorthogonal multiple access (NOMA)-based hybrid spectrum access scheme is proposed for 6G-enabled cognitive IoT (CIoT), where the CIoT may access both the idle and busy spectrum via NOMA regardless of the primary user’s (PU) state. The uplink resource allocations for the CIoT are optimized in the decoding-PU-last and decoding-PU-first schemes, respectively, which seek to maximize the average total transmission rate of CIoT while ensuring the minimum transmission rates for PU and each CIoT node. Then, a clustering NOMA-based CIoT is presented to decrease the interuser interference, where the nodes in each cluster use NOMA to transmit in the allocated subchannel. The simulation results have shown the performance advantages for the NOMA-based hybrid spectrum access and better rate guarantee for the clustering NOMA-based CIoT.

Cognitive industrial Internet of Things (CIIoT) can extend available spectrum resources by accessing the spectrum licensed to primary user (PU) on the premise of not disturbing the PU's communications. However, additional spectrum sensing and long-time working may consume much energy of CIIoT. In this article, a CIIoT with wireless energy harvesting (WEH) is proposed to harvest the radio frequency energy of PU's signal, and energy-efficient resource allocations in different spectrum access modes are presented to maximize the average transmission rate of CIIoT while guaranteeing its energy saving requirements. The underlay and overlay spectrum access modes for CIIoT with WEH are described, respectively, in which the energy-efficient resource allocations are formulated as joint optimization problems that can be solved using the alternating direction optimization and water-filling algorithm. By combining underlay and overlay modes, a hybrid spectrum access mode is proposed to enable the CIIoT to access both idle and busy spectrum without limiting its transmission power at the absence of PU. Simulation results show that the CIIoT with WEH can consume less power to achieve larger transmission rate, and the hybrid mode outperforms the underlay and overlay modes in the aspects of transmission rate and energy saving.

In this paper we study interference reducing and resource allocation in Unmanned aerial vehicle (UAV) wireless powered communication system with two UAVs and two ground nodes (GNs). In existing scenarios interference exists at the receiver because multiple GNs transmit information at the same time. In order to reduce interference at the receiver, a new scenario is proposed in this paper. In the proposed scenario, one GN transmit information while another is receiving energy. Minimum uplink throughput is maximized by optimizing trajectory of UAVs and resource allocation. The optimization problem is decomposed into three subproblems which are approximated to convex optimization problems. Simulation results show that the new scenario achieves larger minimum uplink throughput than original scenario.

Wireless power transfer (WPT) and mobile edge computing (MEC) are two prospective technologies to enhance the computing power and endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be effectively improved with the short‐distance line‐of‐sight path power transfer. However, WPT is susceptible to the ‘double near‐far’ effect. Therefore, in this study, the authors study power optimisation in UAV‐assisted wireless powered cooperative MEC system, which utilises the user cooperation to make the mobile device which is closer to the UAV acting as a relay for offloading. They aim to minimise the total transmission energy of the UAV through the joint power optimisation while satisfying the delay and size of the computational task. Simulation results demonstrate the performance of the proposed scheme.

Complementary set sequences (CSSs) are useful for dealing with the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM) systems. In practical OFDM transmission, however, certain sub-carriers maybe reserved and/or prohibited to transmit signals, leading to the so-called spectral null constraint (SNC) design problem. For example, the DC sub-carrier is reserved to avoid the offsets in D/A and A/D converter in the LTE systems. While most of the current research focus on the design of low PAPR CSSs to improve the code-rate, few works address the aforementioned SNC in their designs. This motivates us to investigate CSSs with SNC as well as low PAPR property. In this article, we present systematic constructions of CSSs under SNCs and low PAPR. First, we show that mutually orthogonal complementary sets (MOCSs) can be used as seed sequences to generate new CSSs with SNC and low PAPR, and then provide an iterative technique for the construction of MOCSs which can be further used to generate complementary sets (CSs) with low PAPRs and spectral nulls at varying positions in the designed sequences. Next, inspired by a recent idea of Chen, we propose a novel construction of these seed MOCSs with non-power-of-two lengths from generalized Boolean functions.

In order to improve the efficiency of information transmission, this paper proposes an energy trading method based on wireless power supply. In the proposed method, the system consists of a source node, a relay node, a destination node and several energy supply points. Since the relay node is selfish, it does not consume its own energy to help the source node forward information. So this paper designed a series of energy-reward pairs. Through the contract theory designed in this paper, we study how to optimize the rewards that the destination node pays to the ESP and the energy provided by the ESP to the relay node to maximize the utility of the social welfare. The simulation results show that the energy trading method proposed in this paper can make the utility of the destination node reach the optimal value and effectively improve the information transmission efficiency.

In this paper, we study energy efficiency optimization in OFDM based two-way DF relaying network with energy harvesting. Instead of time switching (TS) and power splitting (PS) schemes, we adopt OFDM modulation method, where subcarriers are divided into two groups to achieve information decoding (ID) and energy harvesting (EH) separately. The formulated EE optimization problem is non-convex constrained by the minimum information rate and the maximum transmission power. By exploiting fractional programming, an iterative resource allocation algorithm is proposed to solve the problem. Then we adopt dual decomposition and sub-gradient method to obtain the optimal variables, where subcarrier grouping and power allocation are jointly optimized to maximize the system energy efficiency.

To supporting the systematic requirements of higher spectrum efficiency and user deployment density in future Industrial Internet of Things (IIoT), traditional orthogonal multiple access schemes, such as orthogonal frequency-division multiplex, are quite limiting. Nonorthogonal multiple access (NoMA) has been recognized as one of the enabling technologies for IIoT in 5G network. In this paper, we have proposed an alternative NoMA scheme called interleave-grid multiple access (IGMA). Depending on interleaving and grid-mapping process, IGMA is capable to provide reliable block error rate performance, marvelous user multiplexing capability, as well as robustness against intercell interference. IGMA can apply various detection and decoding techniques at receiver sides to improve the detection performance with acceptable complexity. Both of link-level and system-level results show the promising benefits of IGMA, in particular that nearly seven times user multiplexing gain compared with orthogonal frequency-division multiple access has been observed in the system-level simulation. In addition, a hardware test bed has been implemented to verify the IGMA performance and the testing results proved the strong competitiveness of IGMA over orthogonal frequency-division multiple access in terms of block error rate performance in user overloading scenarios.

We investigate spectrally-constrained sequences (SCSs), which are applicable to the communication and radar systems operating over non-contiguous carriers or frequency slots. Typical examples of such systems are overlay cognitive radio or cognitive radar networks. First, we derive the periodic- and aperiodic-correlation lower bounds for single-channel SCSs and multi-channel SCSs by convex optimization in the frequency domain. Each of these bounds reduces to a Welch bound when the number of forbidden carriers is set to zero. We then propose systematic constructions of optimal unimodular single-channel SCSs with the aid of cyclic difference sets and the theory of maximal-length shift register sequences.

Control data separation architecture (CDSA) is a more efficient architecture to overcome the overhead issue than the conventional cellular networks, especially for the huge bursty traffic like Internet of Things, and over-the-top (OTT) content service. In this paper, we study the optimization issue of network energy efficiency of the CDSA-based heterogeneous cloud radio access networks (H-CRAN) networks, which has heterogeneous fronthaul between control base station (CBS) and data base stations (DBSs). We first present a modified power consumption model for the CDSA-based H-CRAN, and then formulate the optimization problem with constraint of overall capacity of wireless fronthaul. Then we work out the resource assignment and power allocation by the convex relaxation approach using fractional programming, norm approximation, and Lagrangian dual decomposition method, with a derived the close-form optimal solution. Finally, we verify the proposed method by system- level simulation. The comprehensive simulation results show that our proposed algorithm has 10% EE gain compared to the static algorithm, and the CDSA-based H-CRAN networks can achieve up to 14% EE gain compared to the conventional network even under strict fronthaul capacity limit.

Summary Matrix factorization is one of the leading techniques for many applications such as social network‐based recommendation systems. As of today, many parallel stochastic gradient descent (SGD) methods have been proposed to address the matrix factorization issue on shared‐memory (multi‐core) systems and distributed systems. However, these methods cannot be improved significantly on graphics processing unit (GPU) because the serious over‐writing problem and thread divergence may occur. The fundamental reason for such undesired results is that GPU is a parallel single instruction multiple data device, which only can greatly improve the applications with fine‐grained parallelism. In this paper, we propose an efficient GPU algorithm, named GPUSGD, to solve the matrix factorization problem based on SGD method. The major advantage of the proposed GPUSGD is that such method not only can handle the over‐writing problem but also can avoid the performance loss caused by the thread divergence. The experimental results show that GPUSGD performs much better in accelerating the matrix factorization compared with the existing state‐of‐the‐art parallel methods. To the best of our knowledge, this is the first work that develops a parallel SGD method to improve the matrix factorization on GPU. Copyright © 2015 John Wiley & Sons, Ltd.

Space shift keying (SSK) modulation is an emerging transmission technique for multiple-input multiple-output (MIMO) wireless channels that exploits spatial domain to convey information. In this paper, we present a layered space shift keying (LSSK) modulation scheme to fully exploit spatial domain to transmit information bits, where a layered architecture is developed to achieve spatial multiplexing transmission in SSK system. With the layered structure, LSSK can achieve much higher spectrum efficiency than the conventional SSK modulation system. The proposed LSSK scheme introduces layer mapping and bit-mapping operations at the transmitter to achieve layered SSK modulation directly with low computation overhead. More precisely, leveraging the phase shift keying (PSK) modulation symbols previously known at the transceiver to identify different layers, multiple antennas are activated simultaneously to emit layered signals. The theoretical bit error probability of LSSK with optimal maximum likelihood (ML) detection is also derived in this paper. Results demonstrate that the proposed LSSK scheme substantially improves the spectrum efficiency of SSK system and outperforms other existing MIMO schemes.

Smartphones now become an indispensable part of our daily life. However, their continuing operations consume lots of battery energy. For example, a fully-charged smartphone usually cannot support its continuing operation for a whole day. A fundamental problem related to this energy issue is how to prolong the smartphone lifetime so that it can last as long as possible to meet its user needs. Wireless energy transfer has been demonstrated as a promising technique to address this challenge. In this paper, we study the smartphone charging problem, using wireless chargers deployed on public commuters, e.g., subway trains, to charge energy-critical smartphones when their users take subway trains to work or go home. Since the residual energy of different smartphones are significantly different, the charging satisfactions of different users are essentially different too. In this paper we formulate this charging problem as a novel optimization problem that allocates limited wireless chargers on subway trains to charge energy-critical smartphones such that the overall charging satisfaction of mobile users is maximized, for a given monitoring period (e.g., one day). Specifically, we first devise a 1 over 3-approximation algorithm if the travel trajectory of each smartphone user in the monitoring period is given; otherwise, we devise an online algorithm dealing with dynamic energy-critical smartphone charging requests. We finally evaluate the performance of the proposed algorithms through experimental simulations with a real dataset of subway-taking in San Francisco. The experimental results show that the proposed algorithms are very promising, and 93.9% of energy-critical user smartphones can be satisfactorily charged in one-day monitoring period.

A sequence is said to be spectrally-constrained if it has to satisfy a spectral map consisting of several non-contiguous nulled frequency-slots. Such sequences play a key role in emerging spectrally-constrained systems such as cognitive radio and cognitive radar. In this paper, we study two types of spectrally-constrained sequences (SCSs), one with low periodic auto-correlation function (PACF) sidelobe, the other with zero auto-correlation zone (ZACZ). By deriving a correlation lower bound, we show that Type-I SCSs are optimal with minimum total PACF sidelobe energy provided that uniform power allocation is applied to all active (non-nulled) frequency-slots. We also propose optimal Type-II SCSs, each having maximum ZACZ width, for certain spectral map patterns.

Transform domain communication system (TDCS) is a multi- carrier cognitive radio (CR) technique which uses cyclic code shift keying (CCSK) for overlay opportunistic spectrum access. Specifically, at any given time, a TDCS system cyclically shifts a fundamental modulation waveform (i.e., a CR sequence satisfying a dynamic spectrum hole constraint) according to specific input data symbol. In practical TDCS system, an interesting research problem is how to design CR sequence with inter-carrier interference (ICI) resilience, where ICI is caused by carrier frequency offset (CFO) or Doppler spread. In this paper, we present a novel family of CR sequences which are able to achieve ICI self-cancellation in TDCS. Analysis and simulations validate that our proposed CR sequence is effective for ICI suppression in TDCS. A future work of this research is to optimize the peak-to-mean power ratio of such CR sequences.

Most existing works on two‐way relay systems (TWRSs) are based on the assumption that the channels are reciprocal. However, in high‐speed moving scenarios, the channels between the multiple access channel phase and broadcast channel phase become non‐reciprocal, which significantly complicates the indispensable channel estimation for TWRSs employing coherent detection. In this study, the challenging problem of channel estimation and data detection (CEaDD) is investigated for amplify‐and‐forward orthogonal frequency division multiplexing‐based TWRSs under unknown non‐reciprocal doubly selective fading channels. First, an independent CEaDD algorithm according to the minimum mean‐square error (MMSE) criterion is proposed. To further improve system performance, an iterative joint CEaDD algorithm employing the variational Bayesian inference (VBI) framework is developed. It is shown by simulations that initialised by the proposed MMSE‐based algorithm, the proposed VBI‐based iterative algorithm converges in a few iterations and after convergence, its performance approaches the ideal case which assumes perfect knowledge of channel state information.

To support interference-free quasi-synchronous code-division multiple-access (QS-CDMA) communication with low spectral density profile in a cognitive radio (CR) network, it is desirable to design a set of CDMA spreading sequences with zero-correlation zone (ZCZ) property. However, traditional ZCZ sequences (which assume the availability of the entire spectral band) cannot be used because their orthogonality will be destroyed by the spectrum hole constraint in a CR channel. To date, analytical construction of ZCZ CR sequences remains open. Taking advantage of the Kronecker sequence property, a novel family of sequences (called "quasi-ZCZ" CR sequences) which displays zero cross-correlation and near-zero auto-correlation zone property under arbitrary spectrum hole constraint is presented in this paper. Furthermore, a novel algorithm is proposed to jointly optimize the peak-to-average power ratio (PAPR) and the periodic auto-correlations of the proposed quasi-ZCZ CR sequences. Simulations show that they give rise to single-user bit-error-rate performance in CR-CDMA systems which outperform traditional non-contiguous multicarrier CDMA and transform domain communication systems; they also lead to CR-CDMA systems which are more resilient than non-contiguous OFDM systems to spectrum sensing mismatch, due to the wideband spreading.

As a cognitive radio (CR) modulation technique, transform domain communication system (TDCS) has been proposed by utilizing white space spectrum for the network access and achieving low probability of interception (LPI). In the previously reported TDCS- based cognitive radio networks (CRN), however, the non-zero periodic correlation function between any pair of users results in multiuser interference (MUI). In this paper, a novel framework under the CR constraints is presented by employing a two- dimension spreading scheme (i.e., time and frequency domains). We first obtain a class of spreading sequences with almost perfect periodic correlation function for arbitrary spectrum utilization pattern. Then, an universal criteria is presented for synchronous MUI-free CRNs when employing TDCS. Simulation results demonstrate that the proposed TDCS-based CRN architecture is a preferable candidate for distribution wireless networks, such as CR Ad-hoc wireless sensor networks.

Energy efficiency is becoming more and more important for mobile devices in future green radio network. This paper addresses the tradeoff between energy-efficiency (EE) and spectral efficiency (SE) in uplink multiuser orthogonal frequency division multiple access (OFDMA) systems. In this paper, EE is measured by the sum of instantaneous bits-per-Joule of each user, and it is optimized with the restrictions on both the minimum rate requirements and available transmit power over frequencyselective channels. Based on this optimization model, a novel low-complexity energy-efficient resource allocation algorithm is proposed. Simulation results demonstrate that the proposed scheme provides a better tradeoff between SE and EE than the conventional schemes with reduced complexity.

Transform domain communication system (TDCS), as an overlay cognitive radio communication system, has been proposed to obtain a low probability of interception by using spectrum bin nulling to synthesise an adaptive waveform corresponding to the spectrum sensing output. However, its low spectral efficiency limits potential practical applications. In this study, an efficient modulation scheme, namely quadrature cyclic code shift keying (Q‐CCSK), is proposed for TDCSs by generating another fundamental modulation waveform as the data‐bearing quadrature branch. Although using Q‐CCSK doubles the spectral efficiency for TDCSs, the instinct inter‐branch interference arises from the added quadrature branch. Through the orthogonality analysis, it is proven that the two branches (in‐phase and quadrature‐ branches) are still satisfied the property of quasi‐orthogonality. Moreover, the performances of TDCSs employing Q‐CCSK in additive white Gaussian noise (AWGN) and multipath fading channels are discussed, respectively. Analytical and simulation results demonstrate that, compared with conventional schemes, the TDCS employing Q‐CCSK can double the spectral efficiency with comparable systematic performance.

For overlay cognitive radio networks (CRNs), transform domain communication system (TDCS) has been proposed to support multiuser communications through spectrum bin nulling and frequency domain spreading. In TDCS-based CRNs, each user is assigned a specific pseudorandom spreading sequence. However, the existence of multiuser interference (MUI) is one of main concerns, due to the non-zero cross-correlations between any pair of TDCS signals. In this paper, a novel framework of TDCS-based CRNs with the joint design of sequences and modulation schemes is presented to realize MUI avoidance. With the uncertainty of spectrum sensing results in CRNs, we first introduce a unique sequence design through two-dimensional time-frequency synthesis and obtain a class of almost perfect sequences whose periodic auto-correlation and cross-correlations are identically zero for most circular shifts. These correlation properties are further exploited in conjunction with a specially-designed cyclic code shift keying in order to achieve the advantage of MUI avoidance. Numerical results demonstrate that the proposed TDCS-based CRNs are well suited for decentralized networks against the near-far problem.

This study presents a cluster-based transform domain communication system (TDCS) to improve spectrum efficiency. Unlike the utilities of clusters in orthogonal frequency division multiplex systems, the cluster-based TDCS framework divides entire unoccupied spectrum bins into L clusters, where each one represents a data stream independently, to achieve L times of spectrum efficiency compared to that of the traditional one. Among various schemes of spectrum bin spacing and allocation, the TDCS with random allocation scheme appears to be an ideal candidate to significantly improve spectrum efficiency without seriously degrading power efficiency. In multipath fading channel, the coded TDCS with random allocation scheme achieves robust bit error rate (BER) performance owing to a large degree of frequency diversity. Furthermore, this study shows that the smaller spectrum bin spacing should be configured for the cluster-based TDCS to achieve higher spectrum efficiency and more robust BER performance.

The mutation testing focuses on the most possible mistakes of the software and so it has high ability to expose mistakes. However, at present it is still mainly used in procedure-oriented program testing. In recent years, object-oriented programming becomes more and more popular. This paper sets up the concepts for two new kinds of class level mutants for object-oriented program testing. One kind is of attribute mutants. The other is of method mutants. The formal description for the concepts is presented and the algorithms for generating attribute mutants and method mutants are proposed in this paper. A tool prototype for the algorithms is implemented and the related testing adequacy is also analyzed.