This paper presents a 12-bit 10-GS/s time-interleaved (TI) ADC with distortion-cancelled track-and-hold amplifier (THA). A half-swing input buffer and ring amplifier (ringamp)-based output buffers are implemented to reduce THA nonlinearity and power consumption. Static and Dynamic distortion cancellation schemes are proposed to enhance THA linearity without imposing extra circuit loading on the buffers. These schemes leverage the opposite polarity of nonlinearities from each circuit component to realize cancellation—static distortion cancellation between the input buffer and output buffer, and dynamic distortion cancellation between the input buffer and sampling network. Fabricated in a 28-nm CMOS process, the prototype ADC consumes 198.1 mW, including 63.9 mW for the THA. At Nyquist input frequency, the ADC achieves 50.48-dB SNDR and 69.91-dB SFDR, equivalent to a Walden FoM of 72.6 fJ/conv.-step and a Schreier FoM of 154.5 dB, demonstrating outstanding linearity and power efficiency.

4D millimeter-wave radar has attracted significant research interest in 3D object detection for autonomous driving as its stability in adverse conditions. However, the sparsity and noise nature of the radar hinders their performance. The virtual-points-based methods with radar-camera fusion may alleviate these limitations. Nevertheless, these approaches primarily focus on the role of hybrid points (including virtual and raw points) in the radar branch and fail to exploit their full potential in the image branch. Hence, we introduce a novel 4D radar and camera fusion network, named HPAFusion for 3D object detection. Specifically, we first propose the Hybrid Points-assisted Depth Net (HPDN), which projects hybrid points onto the image coordinate system and uses voxel grids and points backbone encoding them to assist the image in obtaining depth information. Then, we present the Dual Attention Fusion Module (DAFM), which employs the uniform channel attention and spatial attention for facilitating cross-modal interaction and improving fused features. The proposed HPAFusion is validated on the TJ4DRadSet and View-of-Delft (VoD) datasets. Experimental results demonstrate that HPAFusion effectively fuses camera and radar with hybrid points assistance and outperforms state-of-the-art approaches.

The acoustic-based internet of underwater things (IoUT) is considered as one of the most challenging environments for communication because of issues in the underwater acoustic communication (UWAC) channel, such as multipath propagation and Doppler shift. Accurately estimating these effects without sacrificing a significant portion of the bandwidth is extremely difficult, underscoring the need for robust and sophisticated techniques. In this paper, we propose an acoustic-based unique word orthogonal frequency division multiplexing (UW-OFDM) scheme to enable communication between IoUT nodes over a doubly-selective channel. The proposed scheme employs a joint time-frequency channel estimation approach by leveraging the time-domain guard interval to identify the channel paths while utilizing only 3.1% of the frequency-domain subcarriers, compared to 25% in conventional methods, to track the channel path coefficients. The proposed method significantly enhances spectral efficiency while maintaining resilience to multipath propagation and Doppler shift impairments inherent in UWAC. Furthermore, the scheme eliminates inter-block interference, which is critical in UWAC due to its distinctive propagation characteristics. We evaluate the performance of the proposed methods using both simulations and real-world experimental tests over a 300-meter underwater channel. The results demonstrate that the proposed approach offers a reliable IoUT communication solution, achieving up to a 5 dB improvement in bit error rate and up to 17.56% higher subcarrier utilization compared to conventional schemes. In addition, the scheme exhibits strong robustness against Doppler shift effects with similar peak-to-average power ratio performance and a modest increase in computational complexity.

Millimeter-wave (mm-Wave) transceivers with high power, enhanced peak/back-off efficiency, and low noise are highly desired in 5G New Radio (NR) systems. This article presents a 26.5–29.5-GHz Doherty power amplifier (PA)-low noise amplifier (LNA) with a compact transformer-based matching network (MN). The boundary conditions of a three-port Doherty PA-LNA combiner network are thoroughly analyzed. Building on this analysis, a novel transformer-based MN topology is proposed, enabling simultaneous realization of Doherty operations, noise matching, and high isolation within a time-division duplex (TDD) transmit/receive (T/R) front-end. To validate this concept, a Doherty PA-LNA prototype is designed and fabricated in 65-nm bulk CMOS, occupying a core area of$1.3\times 0.65$mm2. In TX mode, it achieves a maximum output 1-dB gain compression point (OP1dB) of 17.5-dBm with a power-added efficiency (PAE) of 14.6% at 6-dB power back-off (PBO). The drain efficiency (DE) exhibits a superior 1.66 times efficiency enhancement over the normalized class-B DE at 6-dB PBO. In RX mode, it achieves a minimum noise figure (NF) of 4.8 dB at 28.6 GHz with an in-band NF ranging from 4.8 to 5.4 dB. The chip consumes 255 mW at OP1dB in TX mode and 33 mW in RX mode.

This paper presents a K-band eight-element dual-beam receiver in 65-nm CMOS for satellite communications. Current-sharing low-noise amplifiers (LNAs) and passive beamforming networks (BFNs) are employed to achieve low-power operation. Single power supply and daisy-chain digital-control scheme could simplify the phased array design. On-chip low dropout regulator (LDO) reduces the sensitivity of the channel gain to the supply-voltage variation by keeping a steady bias for the amplifiers. The chip utilizes a flip-chip chip-scale package (FCCSP) and occupies 5.2 × 6.4 mm2. Each element achieves an electronic gain of 25 dB, a noise figure (NF) of 2.7 dB, and an input-referred 1-dB gain compression point of -37 dBm at 19.5 GHz. Each element achieves a 360° phase shifting range with a 5.625° resolution and a 15.5-dB attenuation range with a 0.5-dB step. The total power consumption is 275.3 mW (corresponding to 17.2 mW per element per beam). The proposed receiver demonstrates the state-of-the-art NF among K-band beamformers in bulk CMOS processes.

The atomic norm minimization (ANM) is widely applied in the off-grid direction of arrival (DOA) estimation as a super-resolution method. However, the workload of ANM will become unbearable with the rapid growth of snapshots. At the same time, the follow-up work for existing ANM algorithms is quite cumbersome. Therefore, in this paper, we propose an improved off-grid DOA estimation algorithm based on expanded matrix and matrix pencil. The contribution is as follows: Firstly, the cross-covariance matrix (CCM) is expanded to simplify the operation of ANM while maintaining high resolution. Secondly, weighted matrix enhancement matrix pencil (WMEMP) is introduced by us. Computing efficiency is significantly improved by the structure of WMEMP. Optimal weight also improves resolution. The results show that our algorithm is suitable for L-shaped arrays, with good robustness, similar accuracy to the ANM algorithm, and higher computing efficiency compared with other off-grid estimation algorithms.

Random multiple access is a candidate for next-generation multiple access because of its flexibility and simplicity. In this paper, we propose a novel random access scheme, each user holds a signature code as its ID decides whether to transmit or retransmit their packet based on its state and network status, while the receiver chooses to receive and provide feedback to the corresponding user based on the user’s state. We model the system as a Markov chain using a stochastic game, analyze its steady-state distribution, and theoretically analyze the performance of the network. In addition, we analyzed the optimal signature code capability. The simulation results match well with the theoretical analysis, and the proposed protocol significantly outperforms the existing protocols.

This letter examines the fusion detection challenge in multi-frequency-based passive bistatic radar (MF-PBR) in the presence of the signal-to-noise ratio (SNR) difference. The traditional equal-gain-combination (EGC) based methods suffer from performance loss when the SNR difference over the multiple frequency channels is enormous. To this end, a novel weighted combination strategy is proposed for quasi-coherent MF integration. Specifically, a closed-form expression of the fusion weight is derived via modifying the non-coherent integration-based fusion weight with respect to the MF channel parameters. Moreover, for the implementation of the proposed fusion technique, an iterative algorithm for the estimation of MF channel parameters is also developed. Field test results verify the superiority of the proposed method in terms of detection capability.

To address the issue that existing multi-domain fusion methods do not consider data correlation within mini-batch data, the first attempt is made in this letter to propose a method based on sample inter-correlation learning multi-domain fusion network (SIMFNet), which aims to accommodate multivariate domain data and further enhance the aquatic human activity recognition performance. To fully utilize the radar multidimensional information, the three-branch convolution neural network (CNN) feature extractor is first employed to extract domain-specific features from the time-range map (TRM), time-Doppler map (TDM) and cadence velocity diagram (CVD). Then, the multi-domain features are fused and fed into the graph construction layer (GCL) to generate instance graphs. Next, a graph aggregation layer (GAL) is applied to aggregate node information from various-hop neighborhood domains. Finally, node-level classification is used to achieve aquatic human activity recognition. The experimental results evaluated on the built aquatic human activity recognition dataset demonstrate that the proposed SIMFNet has better generalization performance than the state-of-the-art multi-domain fusion methods.

Deep learning (DL) frameworks are widely used in various applications due to their superiority over conventional handcrafted feature-based algorithms. However, applying DL to time-range feature map-based people flow detection (PFD) with frequency modulated continuous wave (FMCW) radar still faces several challenges: 1) simultaneously achieving people counting and motion direction recognition requires a unified framework, 2) existing mainstream network backbones designed for semantic information-rich optical images or natural language suffer performance loss in time-range feature maps with weak semantic information, and 3) the construction of labeled data sets in PFD scenes is usually costly, and limited data lead to performance loss due to overfitting. Therefore, this paper proposes novel solutions from various aspects to efficiently apply DL to time-range feature map-based PFD. First, new preprocessing pipelines with Doppler spectrum analysis-based feature map truncation are proposed for the first time to simultaneously achieve people counting and direction recognition using a single radar in the radar PFD field. Second, a novel lightweight multiscale feature space fusion-based convolutional neural network backbone (MFSNet) is designed to efficiently extract multichannel differentiated representative features from time-range feature maps. Finally, a multiencoder-classifier cotraining architecture based on embedded features with two data synthesis methods and a newly designed loss function is proposed to improve the generalization ability of the algorithm. Using the test data set collected from real scenes, the performance comparison results show that the proposed PFD algorithm outperforms the state-of-the-art algorithms and ablation studies demonstrate the effectiveness of each component of the proposed algorithm in PFD.

Radar is widely used in human activity recognition because of its powerful micro-doppler feature capture capability and environmental adaptability. In this work, we propose a novel radar-based potential drowning detection system. To enhance the cross-domain fusion efficiency and intra-domain feature learning, we design a two-stage fusion network for the drowning detection system. In the first-stage fusion, we integrate the encoded features of three-domain radar maps along either the temporal or spatial dimension. In the second-stage fusion, we use Attention-LSTM and 1D-CNN to extract deep information from temporal-fused and spatial-fused features, and further combine these features using a trainable weighted average strategy. Based on our proposed novel fusion architecture, fine-grained aquatic human activity recognition is achieved. In the experiments, we collect a nine-class aquatic human activity dataset. The experimental results demonstrate the superiority of the proposed TSFNet over the state-of-the-art models. The dataset and the associated codes are available at: https://github.com/DingdongD/aquatic-activity-dataset.

The multi-satellite tracking (MST) for fast link switch is of great importance in guaranteeing the link quality of low earth orbit (LEO) satellite communications. In the marine satellite communication scenario, the position and attitude of the ship and the satellite are known. However, due to the noise from uninterested flight vehicles and the communication path, as well as the effect of the coupling motion between the LEO satellite and the ship, an MST system is necessary to achieve high tracking accuracy. In this work, we propose an MST algorithm for shipborne digital phased arrays, which exploits the constellation orbit characteristics and then associates the interested target states with the orbit parameters. Upon performing a coarse MST using the probabilistic multi-hypothesis tracking (PMHT), the proposed algorithm further picks interested targets and updates their states by the satellite orbit association (SOA). In the process, states of the interested targets are determined according to the orbit parameters in a designed linear constellation orbit model (LCOM), and new strategies are applied to construct and update the LCOM to ensure its effectiveness. Numerical simulations show that the proposed algorithm performs better over the conventional ones.

Estimating ocean wave height is vital for coastal activities. Herein, we propose a method for the estimation of wave height using a phase time series of the frequency-modulated continuous-wave (FMCW) millimeter-wave (MMW) radar. We simulated a one-dimensional wavy ocean surface under different wind speeds and the electromagnetic echo of MMW radar on the ocean surface. The simulation results show that the unwrapping phase time series-based wave heights are highly consistent with the actual wave heights. This shows that the phase time series of the FMCW MMW radar is effective for estimating wave height, and the FMCW MMW radar can be used to monitor ocean states.

Multitarget detection is very challenging, especially when targets are densely distributed. In conventional constant false alarm rate (CFAR) detection algorithms, the detection threshold is determined based on a pre-estimated background level (BL). However, interfering targets inevitably lead to inaccurate BL estimation, resulting in degraded detection performance. In our previous work, a new solution was developed and verified to be effective; in that solution, compressed sensing (CS)-based detector performs target detection without relying on BL estimation, and a subsequent CFAR regulation processor achieves the desired false alarm rate based on the statistical information of the reduced samples obtained by removing the detected targets and adjacent guard cells from the original samples. However, for scenes with a high target density, the CS-based detector suffers from performance degradation while acquiring the correlation between linear measurements of the signal and the sensing matrix due to the high local signal sparsity. To address this shortcoming, this article proposes a deep neural network (DNN)-based detector that further improves the detection performance by converting the target detection into a problem of peak sequence classification of frequency intensity (FI) measurements from radar. A DNN detector trained on augmented simulated data shows excellent generalization ability for deployment in real scenes. In addition, to achieve better computational efficiency, a Taylor-series-based approximate maximum likelihood estimator with an explicit expression is applied in the CFAR regulation process for the first time. Both simulation and field tests are performed to verify the superiority of the proposed algorithm over conventional algorithms.

Particle swarm optimization (PSO) has received great attention for its ftexibility and brevity in conformal antenna array pattern synthesis (CAAPS). However, it also suffers from local optimum and increased computational complexity. In this paper, a solution space pruning particle swarm optimization (SSP-PSO) dedicated to the CAAPS is proposed, which selectively optimizes the peak sidelobe level (PSLL) among the multiple optimization objectives and accordingly applies iterative fast Fourier transform (IFT) at the first stage, and then avoids ineffective search by accomplishing SSP on the basis of dynamic range ratio constraints and the element excitation that is obtained from the foregoing PSLL optimization. The simulation results verify the superiority of the proposed method in terms of both convergence accuracy and convergence speed.

Constant false alarm rate (CFAR) detection algorithms, which are widely used in frequency-modulated continuous wave (FMCW) radar systems, achieve target detection by employing a threshold determined on the basis of a predicted background level. However, in multitarget scenarios, the multitarget shadowing effect can lead to inaccurate prediction of the background level and improper setting of the threshold, which then results in severely degraded CFAR performance. To combat this multitarget shadowing effect, a novel CFAR algorithm based on sparsity adaptive correlation maximization (SACM-CFAR) is proposed in this work. The proposed SACM-CFAR algorithm realizes target detection by utilizing the correlation between linear measurements of the radar intermediate frequency (IF) signal and the sensing matrix. To achieve a desired false alarm rate, the proposed algorithm determines the threshold by estimating the distributed parameters of the reduced sample set obtained by removing the detected targets from the original sample set. Both simulation results and field test results verify that the proposed algorithm outperforms conventional algorithms in multitarget scenarios.

Constant false alarm rate (CFAR) detection plays a vital role in frequency modulated continuous wave (FMCW) radar systems. Most existing CFAR algorithms need to estimate background level before determining the detection threshold. In a multi-target scene, existence of interference targets could cause inaccurate estimation of background level, which will then lead to severely degraded performance of the CFAR algorithm. This is also called multi-target shadowing effect. To overcome the challenge, a compressed sensing CFAR (CS-CFAR) is proposed in this work, which detects the target of interest (TOI) relying on the correlation between the linear measurements of signal and the sensing matrix, instead of relying on an estimated background level as in conventional CFAR algorithms, and achieves a required false alarm rate by estimating the distributed parameters of reduced sample model. Through doing so the proposed CS-CFAR algorithm effectively mitigates impact of interference targets on the detection performance. Superiority of the proposed algorithm over the conventional ones in multi-target scenarios is verified by both simulation and test results.

In this letter, the problem of sparse signal reconstruction from quantized noisy magnitudes y = Q (|z + w| + n) is studied, where z = Ax, Q(·) denotes a quantizer, and x is the sparse signal. According to expectation propagation, the abovementioned problem can be solved by exchanging extrinsic information between the standard linear model (SLM) module and the minimum mean square error (MMSE) module. For the MMSE module, exploiting the fact that the likelihood is only a function of the absolute value of z, the magnitude and phase information of z are decoupled, and only the posterior means and variances of the magnitude of z are calculated. While for the SLM module, the approximate message passing (AMP) is used. To obtain the closed-form expression during the iteration, a novel approximation is adopted. We refer to the resulting algorithm as generalized AMP (Gr-AMP) based quantized phase retrieval algorithm. Finally, several numerical simulations are conducted to demonstrate the performances of the proposed approach.

Passive radar has shown lots of advantages as compared to active radar and has become a significant branch of bistatic radar. Detection difficulty of non-cooperative reflected signals at very low SNR is a well-known challenge in passive radar, and previous works have mainly studied multi-user co-operative detection based solutions. In this work, we study a passive radar system using the television satellite as illuminator of opportunity(IO), and focus on finding solution for detection challenges caused by time-varying interference and high-speed moving target through improving performance of each single detector. A multi-channel based passive detection strategy is proposed. The proposed method is characterized by sufficient information based fusion and weighted combining. Theoretical analysis and Monte Carlo simulation are conducted to evaluate the performance of our strategy.

Some important issues remain to be better resolved before sensing technologies can be widely used in applications. For the reason, spectrum sensing is not selected as the mandatory solution for protection of primary users in current FCC regulations on usage of TV white space (TVWS). In this work, we introduce a sensing prototype for detection of Japanese digital TV signals. The prototype targets at fulfilling the strict FCC sensing requirement under reasonable interference. Moreover, efforts are made to improve physical features and cost-performance ratio. Computer simulation and hardware tests are conducted to evaluate performance of the sensing prototype under adjacent channel interference and co-channel interference. The results verify that the prototype has improved robustness for sensing under interference.

This study introduces a white space device (WSD) prototype developed for the white space technology trial organised by Infocomm Development Authority of Singapore (Singapore Trial). A draft regulation was issued by the Infocomm Development Authority for the Singapore Trial. To meet the strict requirements on some performance criteria defined in the draft regulation, some solutions on designs of the system and algorithm are developed and adopted to the WSD prototype. The prototype has a complete design of hardware and software to realise real‐time and intelligent operations of periodical spectrum sensing and database access, dynamic frequency selection and non‐continuous transmission featured with low adjacent channel power leakage and power control. During the Singapore Trial, tests were conducted to check the above cognitive radio (CR) technologies and CR‐enabled operations. The official test results verify the effectiveness of implemented system and algorithm designs.

This paper reports measurements and characterizations of broadband indoor Television White Space (TVWS) channel with center frequency of 670 MHz. The measurements have been carried out in small- and medium-size mixed office and lab indoor environments recently. Channel multipath delay spread of both line-of-sight (LOS) and non-LOS (NLOS) connections are identified by using BPSK signaling of 20-Mbps data rate. The root-mean-square (RMS) delay spreads are found between 20 ns to 32 ns for LOS and 32 ns to 41 ns for NLOS in small-size office and lab environment, and is found about 38 ns for LOS and 39 ns for NLOS in medium-size indoor environment. A general complex low-pass impulse response model is proposed based on our findings and the classic Saleh-Valenzuela (S-V) channel model. With the proposed channel impulse response model, ray clusters may appear before and after the strongest cluster and rays may exist on both sides of the strongest ray in a cluster. Model parameters are extracted from the measurement results. Extensive simulation results show that the model fits the measurements well.

Considerable efforts have been made by research entities in the world to develop sensing prototypes for digital TV (DTV) signals that can fulfill the FCC sensing requirement. FCC official test results for some sensing prototypes have shown that adjacent channel interference (ACI), which truly exists in real sensing world for DTV signals, has significantly increased the difficulty of fulfilling the FCC sensing requirement. In this work, we aim at developing a real-time sensing prototype that achieves robust sensing under ACI. A partial (channel) bandwidth based feature detection (PB-FD) method is proposed, which not only exploits identical features of DTV standards, namely the DVB-T and ISDB-T, to produce high process gain in detection, but also significantly mitigates the ACI produced by transmission power leakage. The PB-FD is specified to the center segment based FD when it is implemented into a sensing prototype to detect ISDB-T. Computer simulation results verify that PB-FD achieves superior performance over the optimal conventional sensing method, especially when sensing is performed under ACI. The PB-FD is then implemented into a sensing prototype. Hardware performance tests are performed to detect ISDB-T at FCC required threshold level of -114 dBm/6MHz under a moderate level of ACI. The test results indicate that above 0.9 detection probability and below 0.01 false alarm probability are achieved by the sensing prototype.

Both spectrum regulators and standardization organizations are making efforts to realize licensed exempt utilization of TV white space (TVWS). One of major concerns in the usage of TVWS is the potential interference imposed by a TV band device (TVBD) to primary users (PU) in TV bands. Spectrum sensing is used as one important solution for protection of PU. In this work, we aim at developing a real-time sensing prototype that fulfills FCC regulation requirement for sensing only TVBD. To overcome the sensing challenges arising from adjacent channel interference (ACI) in real sensing world, a partial bandwidth based feature detection (PB-FD) method is proposed, which can be used to detect many types of OFDM based signals. When the PB-FD is applied to detect DTV signals of Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) standard, it is specified to detect the centre segment of ISDB-T spectrum. In this paper, we present the sensing algorithm and its simulation results, major system features and performance of the sensing prototype. Hardware performance test for sensing ISDB-T signals under ACI is performed. A sensing performance of above 0.9 detection probability with a false alarm probability of 0.01 is achieved for signal level ≥-114dBm/6MHz, with a sensing time equals to 0.6 second for sensing one channel.

In this paper, we describe the design challenges of a recently developed television white space (TVWS) device prototype and how those were resolved. The prototype, which includes the following components: a cognitive management entity (CME), a sensing module, a geolocation device, a TV band database, a transmitter and a receiver, was designed to take part in Singapore TVWS test trial. The major features the prototype has are: cognitive functionality, spectrum sensing and database access. The technical challenges faced during its development are described in terms of wide dynamic range, adjacent channel rejection and synchronization issues of the spectrum sensor, baseband vs intermediate frequency based sensing, non white environmental noise, security regarding database access and troubles inherent in wireless environment. Performance of the prototype is also presented in terms of cognitive operations such as, success of TV signals and other incumbent detection through database access and sensing, time required for database access, sensing, channel selection, channel switching etc. Discussion regarding setting a reasonable sensing target is also presented.

In this paper, we present a coexistence protocol design to provide coexistence solution among dissimilar or independently operated autonomous decision-making networks in a wireless communication environment over, specifically but not limited to, TV white space (TVWS) frequency bands. The designed coexistence protocol for the information service has three main functionalities: (1) To collect basic information of subscribed TVWS networks; (2) To support generating neighbor lists for the TVWS networks based on the geography information or propagation parameters; (3) To provide the necessary information for TVWS networks to make coexistence decisions. Both theoretical analysis and simulation results shows that the designed coexistence protocol ensures harmonious communication among multiple dissimilar networks and finally achieves coexistence over a particular area with a limited white space channel resource.

This paper discusses a way to determine which devices interfere with each other in order to provide efficient information for coexistence control among dissimilar or independently operated networks in wireless communication environment over, specifically but not limited to, TV white space (TVWS) frequency bands. We refer this as coexistence neighbor discovery in TVWS. Coexistence neighbor discovery is a key algorithm for coexistence control system, which provides the necessary input for coexistence manager or for TVWS networks or devices subscribing for coexistence information service to make coexistence decisions. It therefore significantly impacts the complexity and accuracy of coexistence decision-making. In this paper the potential interference between two networks are statistically evaluated. The 90% confidential interference level that 90% of devices inside communication area receives interference equal or less than is taken as a potential interference of two networks. This value is then compared with the tolerable interference level of a device to determine interference relationship of two networks. The simulation results show the rational to select 90% as confidential interference level, the accuracy of the proposed algorithm was also approved in this paper.

This paper describes the challenges of frequency channel selection for WLANs operating in the TV White Space (TVWS) and proposes a dynamic frequency selection (DFS) technique as a solution. Based on the statistics gained from past usage experience of channels, of the TVWS channels' vacancies and channel occupation by the adjacent WLANs, each WLAN evaluates and prioritize the channels independently using a priority function. Then by selecting the channel of the highest priority among vacant channels, each WLAN can quickly find a channel in the low interference regime and whose vacancy is adaptive to its traffic load distribution as well. Performance of the proposed DFS is evaluated through simulation using QualNet, based on TVWS channels with realistic feature and by setting the weight factors of different metrics in the priority function to different values. The evaluation results show that compared to conventional DFS, the proposed DFS avoids adjacent APs selecting the same channel and reduces channel switch rate, and is more adaptive to diverse environments. As a consequence, it maximally reduces total time consumption of channel switch by 40% than random selection.

In cognitive radio networks, secondary users need to conduct spectrum sensing to properly detect the presence of primary signals that may have much lower power than noise plus interference power. In such a case, time-varying noise plus interference, which is briefly called noise uncertainty in this paper, can substantially degrade the sensing reliability of hard information combining (HIC) and soft information combining (SIC). To improve the sensing reliability in the circumstance with heavy noise uncertainty, we propose an adaptive two thresholds based energy detection and a two stage HIC based cooperative decision for cooperative spectrum sensing. The proposed sensing technique has shown better performance than conventional HIC and comparable performance with SIC when a small number of sensing nodes are used in spectrum sensing.

In this paper, Iterative Unequal Length Search (ULS) Syndrome Decoding for Product Codes is proposed by introducing an idea of unequal length search space. The principle and realization of the algorithm is presented. Moreover, a mathematical performance and decoding complexity analysis is illustrated. Compared to the conventional Equal Length Search (ELS) Syndrome algorithm, experimental results indicate that the iterative ULS syndrome decoding algorithm, not only significantly reduces the decoding complexity, but also improves the error-correcting performance. Because of the balance between the odd and even circuits, the speed of parallel processing is increased and hardware resources are saved accordingly. Simulations results have shown that the proposed algorithm leads to a decrease of 25%~30% of search complexity and a gain of 0.3 dB in the BER performance for various subcodes.

M-ary Elliptical Phase Shift Keying (M-EPSK) have been proposed and evaluated through simulations in our previous works. According to the simulation results, M-EPSK (M=4,8) can achieve better noise performance than MPSK; in particular, M-EPSK can adaptively realize trade-offs between bandwidth efficiency and power efficiency by varying the eccentricity. Eccentricity is the main modulation variable that makes M-EPSK distinctive comparing with conventional MPSK. Substantial effects of eccentricity to performance of M-EPSK have been verified in simulations; however, prior to exploring applications of M-EPSK in communication systems, principle behind the effects of eccentricity to performance needs to be further studied. In this paper, we theoretically evaluate effects of eccentricity to decision parameters of performance, and then explore a novel solution based on approximate treatment to derive closed-form BER approximations of M-EPSK over an AWGN channel. In the BER approximations, the effects of eccentricity to both error performance and bandwidth efficiency of M-EPSK are mathematically derived. In addition, the theoretical results agree with simulation results.

By replacing a circle with ellipses in constellations of MPSK (M=2,4,8), this paper propose several modulation schemes defined as M-ary elliptical phase shift keying (M-EPSK, M=2,4,8). Both mathematical analysis and computer simulations show that M-EPSK can improve power efficiency over MPSK, and have high flexibility to make trade-offs on power efficiency and bandwidth efficiency by varying eccentricities

Some modulation schemes with the general name of elliptical modulation schemes, have been proposed and generally evaluated through computer simulations in our previous works, namely eccentricity shift keying (ESK), inclination angle shift keying (IASK) and elliptical phase shift keying (EPSK). This paper aims to propose and evaluate 8-ary elliptical phase shift keying (8-EPSK), a new modulation scheme with 3-bit information transmission capability. In the paper, 8-EPSK was defined mathematically at first; it was then evaluated mathematically and by computer simulation in terms of BER, along with comparison to 8PSK. Both analysis results and simulation results showed that 8-EPSK outperforms 8PSK.

This work aimed to evaluate some modulation methods, which have been defined based on elliptical modulation scheme, namely are eccentricity shift keying (ESK), inclination angle shift keying (IASK) and elliptical phase shift keying (EPSK). In this paper, signal characteristics of these three modulation schemes and effect of eccentricity to the waveforms have been demonstrated and analysed, through observing real signals generated by using DSPs, both in time-domain and frequency-domain; performances of IASK, 4-EPSK and 8-EPSK, as three modulation methods capable of 1-bit, 2-bit and 3-bit information transmission under the same carrier frequency respectively, have been evaluated through Matlab simulation in terms of BER. Advantages over existing modulation schemes suggested that the newly proposed modulation schemes can contribute to the improvement of wide-band wireless communication systems.