In opportunistic cognitive radio networks, when the primary signal is very weak compared to the background noise, the secondary user requires a long sensing time to achieve reliable spectrum sensing, leaving little time for secondary transmission. To tackle this issue, we propose an active reconfigurable intelligent surface (RIS)-assisted spectrum sensing system, where the received signal strength from the target primary user can be enhanced and underlying interference within the background noise can be mitigated. In comparison with the passive RIS, the active RIS not only adjusts the phase shifts of the reflecting elements but also amplifies the incident signals. Notably, we study the optimization of the reflecting coefficient matrix (RCM) to improve the detection probability given a maximum tolerable false alarm probability and limited sensing time. Then, we show that the formulated problem can be equivalently transformed into a weighted mean square error minimization problem using the principle of the weighted minimum mean square error (WMMSE) algorithm, and an iterative optimization approach is proposed. In addition, to fairly compare passive RIS and active RIS, we study the required power budget of the RIS to achieve a target detection probability under a special case where the direct links are negligible and the RIS-related channels are line-of-sight. The conclusions drawn from this special case are further validated through simulations under more general channel conditions. Furthermore, the effectiveness of the WMMSE-based RCM optimization approach is demonstrated via extensive simulations. The results also reveal that the active RIS can outperform the passive RIS when the interference is relatively weak, whereas the passive RIS performs better in strong interference scenarios due to its ability to support a large number of reflecting elements under the same power budget.

To address the surge in wireless traffic, multiple-input multiple-output (MIMO) technology has become essential for advancing communication systems. Within MIMO cellular networks, coordinated beamforming (CBF), achieved through the collaborative design of beamformers across multiple base stations (BSs), presents a promising strategy for enhancing network performance. However, in large-scale multi-cell, multi-user MIMO environments, optimizing beamforming coordination remains challenging due to high-dimensional spaces and dynamic conditions. While centralized, optimization-based CBF algorithms provide near-optimal solutions, their dependence on real-time global channel state information (CSI) and high computational complexity renders them impractical for dynamic networks. To overcome these limitations, we introduce a diffusion model-enhanced multi-agent reinforcement learning (MARL) coordinated beamforming framework, termed Diffusion-enhanced MACBF, which enables BSs to determine optimal beamformers independently based on partial observations. Key innovations of this framework include: firstly, a novel limited-information exchange protocol that facilitates effective coordination among BSs with minimal communication overhead; secondly, the introduction of a diffusion model-enhanced Multi-agent Soft Actor-Critic (MASAC) algorithm that enables BSs to efficiently manage spatial and temporal variations in large-scale scenarios; and thirdly, an optimized network architecture combining an Encoder, Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (Bi-LSTM) layers to accurately map partial observations to optimal actions. Extensive simulations demonstrate the effectiveness and efficiency of the proposed algorithm, achieving an average rate of 7.5502 bps/Hz with up to 10.66% improvement over existing methods while significantly reducing information exchange requirements.

The advancement of Integrated Sensing and Communication (ISAC) has facilitated the adoption of target tracking technologies, yet most existing research focuses on waveform design rather than specific tracking algorithms within ISAC systems. To address this gap, this paper proposes an effective target tracking method based on Kalman filter (KF) algorithms within the ISAC framework. The method filters noisy radar measurements, such as bistatic range (BR), bistatic range rate (BRR), and direction of arrival (DOA), to enhance tracking accuracy. Performance is assessed by using trajectory maps and root-meansquare error (RMSE) curves, demonstrating that the proposed method significantly improves estimation accuracy while increasing robustness to measurement noise, especially in DOA. Compared to our previously proposed geometric target localization method, this approach delivers superior tracking performance, making it highly suitable for real-time applications in ISAC systems.

We propose an Joint Radar and Communication (JRC) system that utilizes the Orthogonal Time Frequency Space (OTFS) signals. The system features a fast radar sensing algorithm for detecting target range and speed by using the OTFS communication signals, and a self-interference cancellation for enhanced multi-target separation. In addition to target detection, we propose methods for monitoring human vital signs, such as breathing rate and heartbeat. Furthermore, we explore two approaches for distinguishing between human and non-human targets: one based on signal processing and the other based on machine learning. We have developed a prototype JRC system using the software-defined radio (SDR) technology. Experimental results are shown to demonstrate the effectiveness of the prototype in detecting range, speed, and vital signs in both human and mobile robot scenarios, as well as in distinguishing between human and non-human targets.

Accurate and real-time tracking of multiple moving targets remains a fundamental challenge in radar-based indoor sensing, particularly when the number of targets is unknown or varies over time. This paper presents a real-time motion tracking system RTCtrack that operates directly on signal variations induced by human movement. At each time step, the method extracts significant variation points relative to a static baseline and incrementally groups them into coherent trajectories without requiring prior knowledge of the number of targets. A tail-based spatial association strategy enables robust path formation, while weak or inconsistent clusters are automatically removed based on activity level. To maintain tracking stability in evolving environments, an adaptive baseline update mechanism replaces the reference signal when persistent global deviations are detected. RTCtrack is evaluated using a public indoor radar dataset involving multiple individuals moving independently. Simulation results show that the system reliably identifies and tracks multiple motion paths in real time without knowing the exact number of targets. Comparisons with video-based ground truth confirm strong spatial and temporal alignment, demonstrating its effectiveness for radar sensing and passive human motion tracking.

This work presents novel methods for detecting human presence in indoor environments using joint radar and communication (JRC) systems with 5G mmWave signals. Fast Cyclic Correlation Radar (FCCR) techniques are applied to generate range-Doppler maps (RDMs) from which phase variations are extracted. Three detection methods based on these phase variations are proposed: a signal processing approach and two machine learning models - one utilizing Time-Domain features and the other using Frequency-Domain features. Experimental results using both real-world data from a JRC prototype and synthetic datasets demonstrate the effectiveness of the proposed methods, highlighting their potential for reliable and efficient human presence detection in dynamic indoor environments.

This paper studies time difference of arrival (TDoA)based algorithms for drone controller localization and analyzes TDoA estimation in multipath channels. Building on TDoA estimation, we propose two algorithms to enhance localization accuracy in multipath environments: the Maximum Likelihood (ML) algorithm, and the Least Squares Bancroft with GaussNewton (LS-BF-GN) algorithm. We evaluate these proposed algorithms in two typical outdoor channels: Wireless Local Area Network (WLAN) Channel F and the two-ray ground reflection (TRGR) channel. Our simulation results demonstrate that the ML and LS-BF-GN algorithms significantly outperform the LSBF algorithm in multipath channels. To further enhance localization accuracy, we propose averaging multiple tentative location estimations. Additionally, we evaluate the impact of time synchronization errors among sensors on localization performance through simulation.

In large-scale urban environments, precise six-degree-of-freedom (6DOF) pose estimation is essential for vehicles and robots to perform autonomous driving and exploration, as well as to achieve high intelligence and full autonomy of Unmanned Aerial Vehicles (UAV). Achieving 6DOF pose estimation in Global Navigation Satellite System (GNSS)-denied environments is challenging. The performance of relative 6DOF localization systems based on Light Detection and Ranging (LiDAR), vision, and inertial data is easily affected by environmental conditions, leading to error accumulation and a significant decrease in estimation accuracy in complex environments. To address this issue, we propose a tightly coupled framework based on nonlinear optimization for vision, LiDAR, inertial, and GNSS raw data. In the experimental section, we validate the effectiveness of the proposed optimization factor model for GNSS data, LiDAR data, and visual data in improving position and orientation estimation accuracy through simulations. Additionally, we use real datasets to compare the proposed algorithm with several existing open-source programs in terms of computational efficiency, pose estimation accuracy, worst-case scenarios, and reliability. The experimental results show that, although the total processing time increases, the position estimation accuracy and orientation estimation accuracy of the proposed fusion algorithm improve by at least 58.0%. Overall, the proposed tightly-coupled algorithm outperforms the existing methods.

Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communication (URLLC) are two important wireless communication traffics to build a digital twin of physical reality. Therein, eMBB and URLLC traffics are to transmit high-quality sensed data and critical commands, respectively. To support these two important traffics, we develop an intelligent resource allocation mechanism. First, we model the time-frequency resource allocation as an optimization problem aiming to maximize the throughput for the eMBB traffics according to their urgency subject to the constraint on the successful transmission for the URLLC traffics. In this way, the amount of resources allocated to each traffic can be appropriately determined without causing waste in resource usage. Secondly, we propose a feasible low-complexity solution for the optimization problem by relaxing it and then applying linear programming. Thirdly, to address the possible failure of the algorithm due to the relaxation, we propose a post-processing by puncturing the resource initially allocated to eMBB traffics and thereafter reallocating this resource to URLLC traffics. By such, the characteristic of the eMBB traffic, i.e., high throughput, and that of the URLLC traffic, i.e., low latency and ultra reliability, can be achieved. We perform system-level simulations on Matlab 5 G simulation platform to evaluate the performance of the proposed mechanism under different scenarios. Simulations show that the proposed mechanism achieves better performance compared to existing schemes regarding the total eMBB throughput and URLLC failure probability on all the scenarios.

Antenna position errors caused by manufacturing accuracy limits lead to a great difference between the real and ideal models of antenna arrays in extremely high frequency (EHF) band MIMO radar with tiny physical elements and spacings. As the real array is a non-uniform array, the conventional window function fails when applied to non-uniform sampling data, which causes angular ambiguities and seriously degrades the performance of DOA estimation. Based on an advanced analysis of spectral distortion in non-uniform sampling, this paper introduces a method to design a specialized window function for non-uniform MIMO arrays, aiming to minimize the equivalent noise bandwidth (ENBW) while sufficiently suppressing the side-lobe-to-peak ratio of the window function's spectrum. The simulation results show the effectiveness of the optimized window function, which reduces angular ambiguities and thereby improves the performance of DOA estimation for non-uniform MIMO arrays.

The fusion framework based on Light Detection and Ranging (LiDAR) and vision is susceptible to environmental constraints. Therefore, they are unable to adapt to complex and challenging environments. In response to this, this paper proposes a tightly coupled framework based on Global Navigation Satellite System (GNSS) data, which tightly couples GNSS, inertial, LiDAR and visual data. The experimental results demonstrate that, using publicly available challenging environment datasets, the proposed fusion system can achieve a 52.68% improvement in positioning accuracy compared to some existing fusion algorithms.

Spatiotemporal calibration is an essential problem in the fusion system with heterogeneous multi-source information. Therefore, a universal spatiotemporal calibration algorithm for heterogeneous information is much needed. This paper proposes a universal spatiotemporal calibration technique with the inertial sensor as the central coordinate system. Firstly, it employs a high-order spline interpolation method to transform the output data of the inertial sensor into a continuous form. Subsequently, the calibration model is established by combining the output data from other sensors. The paper provides detailed descriptions of the spatiotemporal calibration models for LiDAR (Light Detection and Ranging) data, and visual data, respectively. In the simulations, the proposed calibration models are applied to existing open-source programs using publicly available datasets. The results demonstrate that, with the adoption of the proposed calibration algorithm, the position estimation accuracy of the fusion system can be improved by 39%.

This paper proposes a novel geometric solution for tracking a moving target through multistatic sensing. In contrast to existing two-step weighted least square (2SWLS) methods which use the bistatic range (BR) and bistatic range rate (BRR) measurements, the proposed method incorporates an additional direction of arrival (DOA) measurement of the target obtained from a communication receiver in an integrated sensing and communication (I SAC) system. Unlike the existing 2SWLS methods that require at least three transmitter-receiver (TX-RX) pairs to operate, the proposed algorithm can conduct location estimation with a single TX-RX pair and velocity estimation with two TX-RX pairs. Simulations reveal that the proposed method exhibits superior performance compared to existing 2SWLS methods, particularly when dealing with moderate levels of noise in DOA measurements.

In orthogonal time sequency multiplexing (OTSM) modulation, the information symbols are conveyed in the delay-sequency domain upon exploiting the inverse Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of attaining a bit error ratio (BER) similar to that of orthogonal time-frequency space (OTFS) modulation at a lower complexity, since the saving of multiplication operations in the IWHT. Hence we provide its BER performance analysis and characterize its detection complexity. We commence by deriving its generalized input-output relationship and its unconditional pairwise error probability (UPEP). Then, its BER upper bound is derived in closed form under both ideal and imperfect channel estimation conditions, which is shown to be tight at moderate to high signal-to-noise ratios (SNRs). Moreover, a novel approximate message passing (AMP) aided OTSM detection framework is proposed. Specifically, to circumvent the high residual BER of the conventional AMP detector, we proposed a vector AMP-based expectation-maximization (VAMP-EM) detector for performing joint data detection and noise variance estimation. The variance auto-tuning algorithm based on the EM algorithm is designed for the VAMP-EM detector to further improve the convergence performance. The simulation results illustrate that the VAMP-EM detector is capable of striking an attractive BER vs. complexity trade-off than the state-of-the-art schemes as well as providing a better convergence. Finally, we propose AMP and VAMP-EM turbo receivers for low-density parity-check (LDPC)-coded OTSM systems. It is demonstrated that our proposed VAMP-EM turbo receiver is capable of providing both BER and convergence performance improvements over the conventional AMP solution.

We propose a scheme which generates shared secret keys from the amplitude of frequency domain channel state information (CSI) of individual orthogonal frequency-division multiplexing (OFDM) subcarrier. A unique quantization scheme is proposed, with the use of two parameters,$a$and β, to set the quantization threshold. Based on multivariate Rayleigh distribution, we derive the analytical expressions for key bit disagreement rate (BDR) and key length. The theoretical analysis of BDR and key length aligns with the simulation results. We also present a mathematical model to optimize the parameters of our proposed quantization scheme and a guideline to set$a$and β. Our proposed method for iterative tuning of β can balance the number of zeros and ones in the generated key sequences so that the randomness requirement is met.

We propose a bistatic joint radar and communication (JRC) based on standardized 5G new radio (NR) waveform, where the receiver can decode the transmitted signal, and estimate the range, speed, and angle of the radar targets at the same time. The proposed solution consists of several key components, including a) a decision feedback architecture and two JRC algorithms for bistatic radar sensing; b) a multi-user bistatic JRC solution; and c) a direction of arrival (DOA) estimation algorithm based on the range-Doppler map (RDM). We present simulation results to demonstrate the effectiveness of our proposed solution, and its ability to work well in the multi-user scenarios where multiple users share the same spectrum, making it a promising solution for practical applications that require radar sensing in crowded environments.

For massive machine type communications (mMTC), it is critical to squeeze the transmission overhead as packet length is usually short and power is limited. Reducing preamble/pilot length for channel estimation is thus very important. In this paper, we propose to use short preamble for channel estimation in generalized frequency division multiplexing (GFDM) communication. Based on the short preamble, we can estimate a short channel first. We then show that the conventional zero-padding method for extending short channel to long channel in GFDM is not accurate. A new efficient method to construct the long effective channel from the obtained short channel is proposed. The proposed new method can construct a long channel for GFDM equalization without knowing the time sync error. It is proved theoretically that the constructed channel is correct given that the length of cyclic prefix (CP) and cyclic suffix (CS) are longer than the original channel length plus the time sync error. Simulations at various situations are shown to verify the results.

In this paper, we propose a novel scheme called subband random sensing (SRS) grant free uplink for ultra-reliable low-latency communication (URLLC) in unlicensed spectrum. The SRS grant free uplink creatively combines the subband sensing, user grouping and random access, which allows a user sensing the unlicensed spectrum to use available sub-resources in a wideband and reduce collisions with other users. The new scheme overcomes the severe spectrum wastage problem of the semi-persistent scheduling (SPS) uplink adopted by new radio (NR) release 16 in applications with sporadic traffic. Compared with the contention based grant free uplink, the new scheme achieves much lower collision probability, which directly leads to higher reliability. Analysis and simulations are provided to prove the exceptional performances.

Context and location services become increasingly important in many Internet of Things (IoT) applications such as smart home, disaster assistance, security and safety in smart factory and so on. Traditionally, these services are provided by independent systems. In this paper, we propose a software defined radio (SDR) based multi-function radar for respiratory rate and moving speed detections as well as ranging, which can be used to provide human body detection and tracking in human-centric IoT applications. Since SDR concept is adopted, the same hardware platform could be shared with wireless communications periodically by re-defining. The system architecture design is introduced. The detection algorithms are presented. The ranging performance is compared with some typical ones using simulations. The detections for respiratory rate, ranging and moving speed are carried out on the SDR platform consisting of FMCOMMS5 and ZC706. The system occupies 2MHz bandwidth. It is shown that the proposed multi-function radar achieves the estimation of the human respiratory rate with <;5% errors compared to a clinic-grade device. The ranging resolution achieved is <;0.3 m. The tiny movement as slow as 0.19 m/s can be detected.

In IIoT application, various IoT devices with different protocols are connected. One of the major challenges in IIoT application is to detect interference and achieve coexistence across multiple technologies. In this paper, we propose a novel and efficient method to detect the presence of Microwave Oven (MWO) interference. In order to achieve coexistence between IEEE 802.11 WIFI and IEEE 802.15.4 based sensor network, we propose an enhanced spectral matching method to detect 802.15.4 packet and WIFI packet. Our proposed method can also detect the hopping frequency of 802.15.4 packet. Simulation results show that our proposed method can achieve high detection probability in various propagation channels.

With the increasing usage of software defined radio and digital signal processing, the hardware and RF frontend for radar and wireless communication tends to be similar. Thus, using the same RF and hardware platform for joint radar-communication becomes viable. Joint radar-communication would bring more efficient plan and usage for the radio spectral resource. Furthermore, it could enable new applications which require information exchange and precise localization at the same time. In this paper, efficient algorithms are proposed to use standardized waveform in wireless communication (IEEE 802.11ad, IEEE 802.11p, etc) for range and speed detection of targets. Novel approach is found to cancel the self-interference due to full duplexing operation. Detection performances are verified by simulations.

Multi-carrier modulation has been widely used in wireless communications. Thus accurately detecting multi-carrier signal is of great importance to dynamic spectrum access and spectrum sharing. In multi-carrier modulation, a common approach for enabling channel estimation and amplitude/phase compensation is to insert pilots on some subcarriers. In this paper the pilot subcarriers are used for the detection of the signal. Two methods are proposed: the average pilot energy detection (APED) and joint time and frequency processing (JTFP). APED uses the property of boosted power at pilot locations in the signal power spectrum density, while JTFP uses the property of repetition of pilots in signal blocks. Frequency offset is a major obstacle to many detection methods. To handle this problem, we propose a low complexity method to compensate the composite frequency offset in time domain. We also use neighborhood search in frequency domain to handle the pilot location change due to the frequency offset. Furthermore, the methods are extended to multi-slot sensing, where the sensing duration is non-continuous and divided into multiple slots. Extensive simulations have been carried out and the results have shown that the JTFP can achieve superior performance without the requirement of time and frequency synchronization.

As a major detection and identification method, cyclostationary detection (CSD) has been studied extensively in signal identification/classification and cognitive radio. CSD uses the cyclostationary features inherited in modulated communication signals to differentiate desired signal from other signals or noise. However, the actual cyclostationary features in the signal are affected by many factors. Especially oscillator error and clock error due to the impairments of the devices can change the cyclostationary features. In this paper, sensitivity of the cyclostationary detection is studied mathematically in detail for the digitally modulated signals. Closed-form expressions for the discrete cyclic auto-correlations at any given number of samples are found. Furthermore, robustness of the cyclostationary detection to the cyclic frequency mismatch is found in an explicit expression. Extensive simulations are done to verify the theoretic results.

Dynamic spectrum access or cognitive radio is widely recognized as a major technology in the coming 5G communications to greatly increase spectral efficiency. In cognitive radio, different networks#x002F;systems may dynamically share some spectrum to achieve the maximum overall capacity. Each network#x002F;system may just use a few subcarriers or a few non-contiguous frequencies at a given time. It is very challenging to design the waveform to effectively use the fragmented spectrum with high flexibility for dynamical change. Filter bank multicarrier (FBMC) and generalized frequency division multiplexing (GFDM) are regarded as good candidates for the waveform at such situations. In this paper, we propose fast algorithms to implement the FBMC and GFDM when a number of cognitive radio users sharing the same spectrum. The major contributions are as follows. (1) We use the "sparse" property of the user's spectrum to reduce the sampling rate and complexity in the discrete time implementation; (2) We propose fast algorithms for computing the special Fourier transform, which reduce the complexity by nearly half.

Preamble with repeated time domain blocks is a very common design for joint time and frequency synchronization in various communication systems and standards. While it is easy to design two or more repeated time domain blocks in orthogonal frequency division multiplexing (OFDM) and some other systems, it is not straightforward to design filter bank multicarrier (FBMC) signal that has the repetition property in time domain, as FBMC modulation spreads one block of frequency domain signal into multiple blocks in time domain. In this paper, a new design is proposed for constructing repeated time domain blocks in FBMC. The preamble based on this design can be used for joint time-frequency synchronization and channel estimation as well. Extensive simulations are shown to verify the performance of the design.

Spectrum sensing is the fundamental technology in cognitive radio to learn the radio environment. Energy detection is one of the simplest method for spectrum sensing and has the best performance in theory for signals without special features. However, it is well-known that energy detection is vulnerable to noise uncertainty. There is still no good approach to solve the problem. On the other hand, robust hypothesis testing has been known for a long time, which is a general paradigm to deal with uncertain signal and noise. In this paper, we use this paradigm to tackle the noise uncertainty problem. It is shown that the noise uncertainty can be formed as an ϵ-contamination model. Then it is proved that the robust hypothesis testing turns to robust energy detection for independent signal samples with Gaussian distribution. Methods are found for calculating the parameters in the testing. Simulations show that the robust energy detection achieves better average performance than the energy detection on uncertain noise environment.

In recent years, filter bank multicarrier (FBMC) has recaptured widespread interests for its possible applications in cognitive radio and dynamic spectrum access. A distinctive feature for cognitive radio is its adaptivity to environment. When environment changes, a cognitive radio will change its parameters to optimize the transmission and receiving. Thus it is desirable to design a unified structure and algorithm for FBMC that needs little change for different parameters. In this paper, we propose a unified structure and parallel algorithms to implement the FBMC. The FBMC system and parallel algorithms are constructed based on the normalized prototype filter. The coefficients of the normalized prototype filter can be pre-computed and stored. The proposed parallel algorithms have the same structure for various choices of time duration, subcarrier spacing and bandwidth. Combined with known parallel algorithms for the fast Fourier transform (FFT), the proposed algorithms fully parallelize the computations for the transmitter and receiver, which can run much faster than conventional serial algorithms as modern processors usually have massive parallel capability.

The total out-of-band emission (OOBE) from the secondary users (SU) is determined not only by the baseband signaling waveforms, but also by the design of the transmitter chain, which includes the nonlinear power amplifier (PA). The increase in OOBE due to a nonlinear PA has been well-observed, but its characterization involves complicated analysis. In this paper, we present a closed-form approximation to the power spectral density (PSD) of a signal at the output of a nonlinear PA, or more specifically, the solid-state power amplifier (SSPA). The SSPA's response can be approximated using a third order nonlinear model (TONM) which has been proposed in literature. Using the TONM, we proposed a statistical approximation, together with a piecewise linear model, to determine a closed-form estimate to the OOBE of the PA output signal. Simulation results show a close fit between the theoretical expression for the derived PSD and the simulated model. Since the nonlinear PA increases the OOBE due to spectral regrowth, the ability to characterize this behavior is of particular importance to the application of TV band devices (in television broadcast frequency bands) that need to fulfill certain OOBE limits.

In this paper we consider multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM)-based systems under unknown narrow-band interference (NBI). We propose an iterative receiver to jointly estimate the channel information, which consists of channel coefficients and noise-plus-interference variances of each sub-carrier, and detect the transmitted signals. The simulation results show that our proposed receiver provides a close bit-error-rate (BER) to that of the case where perfect channel information is available at the receiver. Besides, Cramér-Rao lower bound (CRLB) of interested parameters are also derived. The mean-square-error (MSE) of the estimated parameters given by our proposed algorithm reaches the CRLB.

Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the auto-correlation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel.

With the prior knowledge that the primary user is highly likely idle and the primary signals are digitally modulated, we propose an optimal Bayesian detector for spectrum sensing to achieve higher spectrum utilization in cognitive radio networks. We derive the optimal detector structure for MPSK modulated primary signals with known order over AWGN channels and give its corresponding suboptimal detectors in both low and high SNR (Signal-to-Noise Ratio) regimes. Through approximations, it is found that, in low SNR regime, for MPSK (M > 2) signals, the suboptimal detector is the energy detector, while for BPSK signals the suboptimal detector is the energy detection on the real part. In high SNR regime, it is shown that, for BPSK signals, the test statistic is the sum of signal magnitudes, but uses the real part of the phase-shifted signals as the input. We provide the performance analysis of the suboptimal detectors in terms of probabilities of detection and false alarm, and selection of detection threshold and number of samples. The simulations have shown that Bayesian detector has a performance similar to the energy detector in low SNR regime, but has better performance in high SNR regime in terms of spectrum utilization and secondary users' throughput.

This paper considers multi-sensor multi-antenna spectrum sensing. First, it is assumed that all users are able to send their raw data to the fusion center. In this case the global optimial solution is the likelihood ratio test (LRT) using all the raw data. A simple closed-form expression for the LRT is found. Although LRT is optimal, it is hardly useful in practice due to its reliance on the knowledge of primary user's channels and noise powers of all users. Thus a method using the estimated channels and noise powers is proposed, which is called generalized LRT (GLRT). Secondly, the optimal fusion scheme (OFS) is found if each user computes its test statistic based on an eigenvalue based detection and sends the test statistic to the fusion center. Both GLRT and OFS need the SNR information of all users. To make the detections more practical, two totally blind detections, namely, approximated OFS and approximated GLRT, are proposed. Simulations are provided to support the results.

The essential tradeoff between sensing capability and achievable throughput of the secondary network is one of the active research topics for researchers working on cognitive radio. In this paper, noise uncertainty which has a great impact on sensing methods is taken into account in the maximization of throughput using eigenvalue based spectrum sensing schemes. This issue has not been tackled in the throughput associated studies before. First, the theoretical and empirical distributions of the decision statistics and the detection performances for eigenvalue based sensing techniques are studied in the presence of noise uncertainty. The computed detection probabilities of maximum-minimum eigenvalue (MME) detector and maximum eigenvalue detector (MED) are compared with the most widely used energy detector (ED). Then, in the light of the obtained results, the throughput of the secondary network is maximized in order to find out the sensing duration for each scheme using multiple receive antennas. It is shown that, under low signal to noise ratio (SNR) regime, the designed sensing slot duration achieves the best sensing throughput tradeoff.

In an opportunistic spectrum access based cognitive radio network, it is usually assumed that the secondary user (SU) will transmit at peak power when it detects that the primary user (PU) is absent while it will not transmit when the PU is detected to be present. However, when the PU is detected to be inactive, the PU may not definitely be inactive as it may be a case of miss-detection. If it is a miss-detection, the SU's data rate for that particular data frame will be reduced due to interference from the PU. In this paper, power allocation strategies are designed for each data frame based on the spectrum sensing information (SSI) gathered during the sensing period. Using the SSI, the miss-detection probability for a data frame is taken into consideration when maximizing the average data rate and minimizing the outage probability of the SU. The probability of detecting the PU is required to be above a targeted threshold and the SU is required to satisfy its long-term transmit power budget. Our results showed that the transmit power of the SU should be a function of the SSI in order to maximize the average data rate and minimize the outage probability of the SU.

Based on the Neyman-Pearson theorem, the optimal cooperative sensing for distributed sensors with time independent signals is derived. It is shown that the optimal scheme is simply a linearly combined energy detection and the combining coefficient is a simple function of the signal to noise ratio (SNR). To reduce the required information at the fusion center and simplify the decision-making process and threshold setting, an approximated optimal cooperative sensing is proposed and compared with some other sub-optimal methods. Finally the impact of decoding error in the reported results is analyzed. Based on the closed-form expression for the performance, it is proved that the impact of decoding error is equivalent to the reduction of sensing time. Simulations are provided to support the results.

Based on the high probability that primary user is idle in cognitive radio networks, we propose an optimal Bayesian detector structure for spectrum sensing. Although the optimal detector by Neyman-Pearson theorem maximizes the detection probability for a given false alarm probability, Bayesian detector can achieve a higher overall spectrum utilization and SU throughput and at the same time the primary user is well protected from secondary user's interference. For BPSK modulated primary signals we show that the optimal Bayesian detector can be reduced to an energy detector in lower SNR regime, and it can be approximated to a detector employing the sum of received signal magnitudes in high SNR regime to detect primary signals. We give the analysis for optimal Bayesian detector and the corresponding suboptimal detector structure in both low and high SNR regimes, and verify the performance of the detector with simulation results.

In an opportunistic spectrum access (OSA) based cognitive radio system, a secondary user (SU) is allowed to access the licensed spectrum of the primary user (PU) when it is inactive. Conventional power allocation strategies, which do not consider spectrum sensing information (SSI), may not be optimal in OSA based cognitive radio system because when the SU mis-detects the PU's presence, the interference from the PU will cause a lower data rate or a higher outage probability to the SU. In this paper, power allocation strategies for each frame are designed based on the SSI gathered by the SU during the sensing period of the frame. We consider both cooperative and non-cooperative spectrum sensing scenarios. In non-cooperative spectrum sensing, the SU transmitter (SU-Tx) has its SSI while in cooperative spectrum sensing, the SU-Tx has both its SSI and the SU receiver's (SU-Rx's) SSI. Using the SSIs, power allocation strategies are designed to either maximize the average data rate or minimize the outage probability of the SU. The proposed power allocation strategies have to ensure that the PU is sufficiently protected and the SU satisfies its long-term transmit power budget. Optimization of the sensing time is also considered to further enhance the performances of the system.

In recent years, some spectrum sensing algorithms using multiple antennas, such as the eigenvalue based detection (EBD), have attracted a lot of attention. In this paper, we are interested in deriving the asymptotic distributions of the test statistics of the EBD algorithms. Two EBD algorithms using sample covariance matrices are considered: maximum eigenvalue detection (MED) and condition number detection (CND). The earlier studies usually assume that the number of antennas (K) and the number of samples (N) are both large, thus random matrix theory (RMT) can be used to derive the asymptotic distributions of the maximum and minimum eigenvalues of the sample covariance matrices. While assuming the number of antennas being large simplifies the derivations, in practice, the number of antennas equipped at a single secondary user is usually small, say 2 or 3, and once designed, this antenna number is fixed. Thus in this paper, our objective is to derive the asymptotic distributions of the eigenvalues and condition numbers of the sample covariance matrices for any fixed K but large N, from which the probability of detection and probability of false alarm can be obtained. The proposed methodology can also be used to analyze the performance of other EBD algorithms. Finally, computer simulations are presented to validate the accuracy of the derived results.

Cyclostationary detection is regarded as a major method for spectrum sensing in cognitive radio and other applications as well. The rationale behind the detection is that the second order statistic of the interested signal is periodical. The period is therefore used as the critical feature for detection. In practice, due to clock error or oscillator error or other errors, the detector is hardly able to know the exact period of the signal. This causes a cyclic frequency mismatch in the detection. In this paper, the origin of the mismatch and the impact of it are analyzed. Theoretic analysis and simulations are presented to show that the cyclostationary detection is actually very sensitive to the mismatch. The theoretic analysis on the test statistics matches very well with simulations and can be used for predicting the detection performances and designing the detection parameters.

In cognitive radio networks, both the sensing time and the fusion schemes used for cooperative spectrum sensing affect the detection probabilities of the primary users and the throughput of the secondary users. Therefore, joint optimization of the sensing time and the cooperative fusion scheme has been studied before in terms of sensing-throughput tradeoff design. In this paper, different from the previous studies, we consider the case that each secondary user may have different detection signal- to-noise ratio (SNR), and requires different threshold for energy detection. Weightings are used to weigh the decisions from the secondary users before combining. A new algorithm is proposed to compute the thresholds for the secondary users and the optimal weightings for the decisions are shown. Computer simulations are presented to show the performance of the proposed algorithm.

We consider a cognitive radio (CR) network that makes opportunistic use of a set of channels licensed to a primary network. During operation, the CR network is required to carry out spectrum sensing to detect active primary users, thereby avoiding interfering with them. However, spectrum sensing may cause negative effect on the performance of the CR network, as all CR communications has to be postponed during channel sensing. This paper focuses on adaptively scheduling spectrum sensing and data transmission so that negative impacts to the performance of the CR network are minimized. We first consider the case when CR nodes always have data to transmit and experience time-varying channels. Based on knowledge of channel conditions, the sensing periods are adaptively scheduled to maximize the spectrum efficiency of the CR operation. We show how optimal sensing/transmission scheduling policies can be obtained and prove some important structural properties of such optimal policies. We then consider the case when CR nodes experience both stochastic data arrival and time-varying channels. By treating each sensing period as a 'virtual sensing packet'', we convert the problem of joint spectrum-sensing/datatransmission scheduling into a standard queueing model. Based on that, an efficient scheduling algorithm that takes into account channel and queue conditions of the CR network is proposed.

In this letter, we propose multi-antenna based spectrum sensing methods for cognitive radios (CRs) using the generalized likelihood ratio test (GLRT) paradigm. The proposed methods utilize the eigenvalues of the sample covariance matrix of the received signal vector from multiple antennas, taking advantage of the fact that in practice, the primary user signals to be detected will either occupy a subspace of dimension strictly smaller than the dimension of the observation space, or have a non-white spatial spectrum. These methods do not require prior knowledge of the primary user signals, or the channels from the primary users to the CR. By making different assumptions on the availability of the white noise power value at the CR receiver, we derive two algorithms that are shown to outperform the standard energy detector.

In cognitive radio networks, joint optimization of sensing time and cooperative fusion scheme has been studied in the past in terms of sensing-throughput tradeoff design. In this paper, different from previous studies, we consider the case that the secondary users have different detection signal-to-noise ratios (SNRs) and their decisions are weighted based on the likelihood-ratio test at the fusion center. We consider three scenarios. In Scenario I, we optimize individual secondary users' thresholds together with the fusion rule's threshold at the fusion center. In Scenario II, all the secondary users' thresholds are constrained to be the same and we seek the optimal threshold jointly with the fusion rule's threshold at the fusion center. In Scenario III, each secondary user computes its own threshold while the fusion center optimizes the fusion rule's threshold based on the secondary users' threshold results. Solutions are provided for the three different scenarios and computer simulations are presented to compare their performances.

Spectrum sensing is a fundamental problem in cognitive radio. As a result, it has been reborn as a very active research area in recent years despite its long history. Although various sensing methods have been proposed, their robustness at very low signal-to-noise ratio (SNR) and uncertain noise/interference environment is generally not satisfactory. In this paper, the concept of robust sensing is discussed first. Subsequently the cooperative covariance and eigenvalue based detections are proposed for robust spectrum sensing. It is proved mathematically that under some conditions the proposed methods are robust to uncertain and unpredictable noise and interference. The performances of the methods are also verified by simulations.

A pilot aided joint symbol timing and frequency offset estimator for OFDM systems over power line channel (PLC) with colored noise is proposed in this paper. In the proposed estimator, the received time domain OFDM samples are first divided into groups where noise samples are approximately independent. Initial maximum likelihood (ML) based symbol timing and frequency offset estimates are obtained based on each group and final estimate is then derived by combining the initial estimates. We further simplify the proposed estimator to a practical one which does not require the knowledge of PLC fading profile, noise profile and signal-to-noise-ratio (SNR). The simulation results show that, under the presence of colored noise, the proposed practical symbol timing estimator achieves performance gain in terms of correct synchronization probability, around 2 dB at SNR=15 dB, and with improved mean square error (MSE) performance, compared to the widely adopted estimator in. With the knowledge of the statistics of colored noise, another algorithm is also proposed to further improve the estimation performance.

Powerline communication (PLC) is a preferred choice for smart home network. In this paper, a new system structure is proposed for PLC, which is based on distributed orthogonal frequency division multiple access (DOFDMA). Subcarrier sensing, i.e., sensing every OFDM subcarrier to see if it is occupied or not, is proposed to replace the conventional carrier sensing multiple access (CSMA) for multi-user contention of a channel. This structure, borrowed from cognitive radio, allows multiple users to opportunistically share the same channel on different subcarriers and therefore increases the channel capacity. Two subcarrier sensing methods are proposed: one is based on information theory and the other is based on successive energy comparison. Simulations are provided to verify the methods.

The problem of multi-user cooperation to achieve cooperative diversity for single-antenna terminals is considered. A cooperative strategy is proposed which divides multiple users into groups with each group having two users as partners. The two users alternately transmit their own information first, then simultaneously amplify and forward the partner's data in the same channel. Comparing to the conventional multi-user cooperation where the two users relay the partner's data in two orthogonal channels, the spectral efficiency of the proposed scheme is increased. However, as the two users relay the partner's data in the same channel, multi-user detection problem arises. An iterative detection making use of the new scheme's property is proposed in the presence of frequency offsets. The proposed scheme only requires time and frequency synchronization but no channel state information at the user terminals. To estimate the channel at the destination, a training scheme is proposed. The simulation results show that the proposed scheme can achieve the same cooperative diversity as the conventional scheme, while the spectral efficiency is lifted by 4/3.

Spectrum sensing is a fundamental component in a cognitive radio. In this paper, we propose new sensing methods based on the eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum eigenvalue to minimum eigenvalue; the other is based on the ratio of the average eigenvalue to minimum eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed algorithms. We also find the thresholds of the methods for a given probability of false alarm. The proposed methods overcome the noise uncertainty problem, and can even perform better than the ideal energy detection when the signals to be detected are highly correlated. The methods can be used for various signal detection applications without requiring the knowledge of signal, channel and noise power. Simulations based on randomly generated signals, wireless microphone signals and captured ATSC DTV signals are presented to verify the effectiveness of the proposed methods.

This paper considers a scenario in which a secondary user (SU) opportunistically accesses a channel allocated to some primary network (PN) that switches between idle and active states in a time-slotted manner. At the beginning of each time slot, SU can choose to stay idle or to carry out spectrum sensing to detect the state of PN. If PN is detected to be idle, SU can carry out data transmission. Spectrum sensing consumes time and energy and introduces false alarms and mis-detections. The objective is to dynamically decide, for each time slot, whether SU should stay idle or carry out sensing, and if so, for how long, to maximize the expected reward. We formulate this as a partially observable Markov decision process and prove important properties of the optimal control policies. Heuristic control policies with low complexity and good performance are also proposed. Numerical results show the significant performance gain of our dynamic control approach for opportunistic spectrum access.

Abstract Measurements performed at several locations clearly show that frequency spectrum is under‐utilized. Cognitive radio (CR) is a strong candidate to ensure better spectrum utilization by providing access in an opportunistic manner, i.e., when the spectrum is temporary or spatially unutilized (a.k.a. white‐space). The key enabling technology for realizing better frequency spectrum utilization in CR system is spectrum sensing. In particular, spectrum sensing in television (TV) bands is of great interest due to the potential availability of spectrum as a result of the planned migration of analog TV broadcasting to digital TV broadcasting. In this paper, we present the TV white‐space prototype that we developed to detect vacant spectrum in TV bands. The prototype is implemented on a real‐time platform and tested in practical environments. Results show that our prototype is able to detect vacant channels up to sensitivities from −116 to −120 dBm under realistic conditions. This confirmation will give the much needed confidence in the capability of CR systems to detect the operation of primary users and protect their use of spectrum. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we propose several spectrum sensing methods designed using the generalized likelihood ratio test (GLRT) paradigm, for application in a cognitive radio network. The proposed techniques utilize the eigenvalues of the sample covariance matrix of the received signal vector, taking advantage of the fact that in practice, the primary signal in a cognitive radio environment will either occupy a subspace of dimension strictly smaller than the dimension of the observation space, or have a spectrum that is non-white. We show that by making various assumptions on the availability of side information such as noise variance and signal space dimension, several feasible algorithms result which all outperform the standard energy detector.

Channel sensing, i.e., detecting the presence of primary users, is a fundamental problem in cognitive radio. Energy detection is optimal for detecting independent and identically distributed (iid) signals, but not optimal for detecting correlated signals. In this paper, a method is proposed based on the sample covariance matrix calculated from a limited number of received signal samples. The maximum eigenvalue of the sample covariance matrix is used as the test statistic. Since the covariance matrix catches the correlations among the signal samples, the proposed method is better than the energy detection for correlated signals. For iid signals, the method approaches to the energy detection. The random matrix theory is used to analyze the method and set the threshold. Similar to energy detection, the methods do not need any information of the source signal and the channel as a priori. Also, no synchronization is needed. Simulations based on wireless microphone signals and iid signals are presented to verify the method.

In this paper, linear preceding is introduced into MIMO-CPSC system to enhance channel estimation. Thanks to the preceding, the input symbols have well-defined statistical correlations. This property is then used for blind channel estimation. By modifying and eigen-decomposing the statistical auto-correlation matrix of the frequency domain received signals, the channels are estimated subject to a matrix (with size K x K, where K is the number of transmitters) ambiguity. Several linear precodings are presented. The precodings do not change the power of a precoded block. Also, there are fast algorithms to implement the precodings and decodings. Simulations show that the method is effective and robust.

We consider a scenario in which a secondary user makes opportunistic use of a channel allocated to a primary network. The primary network operates in a time-slotted manner and switches between idle and active states according to a stationary Markovian process. At the beginning of each time slot, the secondary user can choose to stay idle or to carry out spectrum sensing to check if the primary network is idle or active. If the primary network is detected as idle, the secondary user can carry out data transmission. Spectrum sensing consumes energy and introduces false alarms and mis-detections. Given the delay cost associated with staying idle, the energy cost associated with spectrum sensing and data transmission, and the throughput gain associated with successful transmissions, the objective is to find an optimal sequence of idle/sensing actions, together with the optimal spectrum sensing durations, to maximize the expected net reward for the secondary user. We formulate this problem as a partially observable Markov decision process (POMDP) and obtain optimal control policies. Heuristic control policies that can be obtained at low complexity are also proposed.

Signal detection is a crucial phase in building a cognitive radio system. However, energy detection, as well as other 'moment detector', has been shown to exhibit considerable performance degradation in the presence of noise power uncertainty. In this paper, we discuss another method that employs fast Fourier transform (FFT) and max-to-mean ratio (MMR) to detect weak narrowband signals. An analytical approximation of the threshold used is derived, and compared with the empirically simulated constants to assess the degree of accuracy of the former. Simulation results are then presented to illustrate the impact of different parameters on the performance of the detector. Finally, comparisons with energy detector are drawn to highlight some important advantages of the MMR method, i.e. noise power independence and lower sample size requirement.

A novel subspace (SS) based blind channel estimation method for multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed in this work. With an appropriate re-modulation on the received signal blocks, the SS method can be effectively applied to the cyclic prefix (CP) based MIMO-OFDM system when the number of the receive antennas is no less than the number of transmit antennas. These features show great compatibility with the coming fourth generation (4G) wireless communication standards as well as most existing single-input single-output (SISO) OFDM standards, thus allow the proposed algorithm to be conveniently integrated into practical applications. Compared with the traditional SS method, the proposed algorithm exhibits many advantages such as robustness to channel order over-estimation, capability of guaranteeing the channel identifiability etc. Analytical expressions for the mean-square error (MSE) and the approximated Cramer-Rao bound (ACRB) of the proposed algorithm are derived in closed forms. Various numerical examples are conducted to corroborate the proposed studies.

In this letter, a method is proposed to optimally combine the received signal samples in space and time based on the principle of maximizing the signal-to-noise ratio (SNR). After the combining, energy detection (ED) is used. However, optimal combining needs information of the source signal and channel, which is usually unknown. To overcome this difficulty, a method is proposed to blindly combine the signal samples. Similar to energy detection, blindly combined energy detection (BCED) does not need any information of the source signal and the channel a priori. BCED can be much better than ED for highly correlated signals, and most importantly, it does not need noise power estimation and overcomes ED's susceptibility to noise uncertainty. Also, perfect synchronization is not required. Simulations based on wireless microphone signals and randomly generated signals are presented to verify the methods.

Singlecarriercyclicprefix(SCCP) modulation has been adopted as one of the physical layer air-interfaces for IEEE802.I6 standards due to its less stringent requirement onpeak-to-averagepowerratio(PAPR) than multicarrier modulation. As a single carrier transmission, however, the received SCCP blocks suffer from inter-symbol interference (ISI). Theblockiterativegeneralizeddecisionfeedbackequalizer(BI-GDFE) is an effective interference cancellation scheme which generally shows performance improvement over linear equalizers. However, in order to achieve a significant gain, the channel must contain rich multipath components, and this may not necessarily be true in many systems. In this paper, we propose to applycyclicdelaydiversity(CDD) to a SCCP system so that the possible performance improvement by using a BI-GDFE detector can be enhanced. The asymptotic performance of the proposed system is analyzed using random matrix results. We propose a scheme to determine the optimal delay parameter for CDD. Computer simulations have shown that by incorporating CDD into the transmitter and using a BI-GDFE receiver, the BER performance of the system can approach the MFB within several iterations for high SNR region.

In a cognitive radio network, the secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. To support this spectrum reuse functionality, the secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance in cognitive radio networks. There are two parameters associated with spectrum sensing: probability of detection and probability of false alarm. The higher the probability of detection, the better the primary users are protected. However, from the secondary users' perspective, the lower the probability of false alarm, the more chances the channel can be reused when it is available, thus the higher the achievable throughput for the secondary network. In this paper, we study the problem of designing the sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected. We formulate the sensing-throughput tradeoff problem mathematically, and use energy detection sensing scheme to prove that the formulated problem indeed has one optimal sensing time which yields the highest throughput for the secondary network. Cooperative sensing using multiple mini-slots or multiple secondary users are also studied using the methodology proposed in this paper. Computer simulations have shown that for a 6 MHz channel, when the frame duration is 100 ms, and the signal-to-noise ratio of primary user at the secondary receiver is -20 dB, the optimal sensing time achieving the highest throughput while maintaining 90% detection probability is 14.2 ms. This optimal sensing time decreases when distributed spectrum sensing is applied.

In this paper, we consider the problem of blind channel estimation for multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems via second order statistics (SOS) only. By an appropriate re-modulation on the received signal blocks, we find an effective way to apply the subspace based channel estimation for the cyclic prefix (CP) based MIMO-OFDM system when the number of the receive antennas is no less than the number of transmit antennas. Suitable relationships are built between the proposed algorithm and one existing technique (called ZPSOS in the future) for zero-padding (ZP) based MIMO-OFDM systems, thanks to the re-modulation considered. Consequently, many advantages could be directly inherited from ZPSOS, for example, robustness to channel order over-estimation, guaranteeing the estimation identiflability. By comparing these two algorithms, we found that they could provide similar channel estimation accuracy but the proposed method could beat the other in terms of bit error rate (BER). Moreover, adopting CP-based OFDM is compatible with many existing standards and applications, which shows great potential of our proposed algorithm.

In cognitive radio networks, the secondary network (users) are allowed to utilize the frequency bands of primary network (users) when they are not currently being used. To support this function, the secondary users are required to sense the radio frequency environment, and once the primary user is found to be active, the secondary users have to vacate the channel within certain amount of time. There are two parameters related to channel sensing: probability of detection and probability of false alarm. The higher the detection probability, the better the primary users can be protected. However, from the secondary users' perspective, the lower the false alarm probability, the more chances the channel can be reused, thus the higher the achievable throughput for the secondary users. In this paper, we study the fundamental tradeoff between sensing capability and achievable throughput of the secondary users. Particularly, we study the design of sensing slot duration to maximize the achievable throughput for the secondary users under the constraint that the primary users are sufficiently protected. Using energy detection scheme, we prove that there indeed exists an optimal sensing time which provides the best tradeoff. Cooperative sensing is also studied based on the methodology of the proposed sensing-throughput tradeoff. Computer simulations are presented to evaluate the proposed tradeoff methodology.

We address the issue of delay selection for cyclic delay diversity (CDD) for single carrier cyclic prefix (SC-CP) transmissions over correlated clustered channel and detected using the BI-GDFE. Due to the correlation among the transmit antennas, beamforming is used to generate uncorrelated virtual channels for the application of CDD using the generalized transmit MMSE method. As the BI-GDFE can exploit the frequency diversity present, judicious selection of the cyclic shifts would improve the performance of system. Delay selection is performed by finding the delays which minimizes the variance of the even order channel statistics, and using these delays will yield the best performance. Computer simulations are presented to show the effectiveness of the delay selection scheme for the considered system.

Sensing (signal detection) is a fundamental problem in cognitive radio. In this paper, a new method is proposed based on the eigenvalues of the covariance matrix of the received signal. It is shown that the ratio of the maximum eigenvalue to the minimum eigenvalue can be used to detect the signal existence. Based on some latest random matrix theories (RMT), we can quantize the ratio and find the threshold. The probability of false alarm is also found by using the RMT. The proposed method overcomes the noise uncertainty difficulty while keeps the advantages of the energy detection. The method can be used for various sensing applications without knowledge of the signal, the channel and noise power. Simulations based on randomly generated signals and captured ATSC DTV signals are presented to verify the methods.

We present a downlink single carrier cyclic prefix transmission method which employs directional beamforming and artificial delay for channels with dominant cluster paths. The proposed technology robustly manages the overall delay of the transmission channel for performance gain. For channels with delays larger than the cyclic prefix, the proposed method shortens the delay for IBI-free transmission. When the cyclic prefix duration exceeds that of the channel duration, the proposed technique can lengthen the effective channel delay to possibly improve the system's performance. Computer simulations are presented to verify the efficacy of the proposed transmission technology.

In multiuser MIMO uplink, different pairs of transmitter and receiver may have different time delays and carrier frequency offsets (CFOs). This causes the time delay and CFO estimations much more difficult than those for a single input single output (SISO) system. It is a challenging problem to resolve the time delays and CFOs in the MIMO uplink. In this paper, an asynchronous multiuser MIMO system with multipath channels and CFOs is remodeled into another system. A blind method based on second order statistics is then used for estimating the shaped channels with ambiguity. Based on the shaped channels, specially designed pilots are proposed to find the CFOs. The same pilots are also used for resolving the time delays and the ambiguity in the estimated channels. Only upper bounds for the channel orders and time delays are needed for implementing the algorithms, which makes them applicable to practical multiuser MIMO systems.

In multiuser OFDMA uplink, different users usually have different carrier frequency offsets (CFOs). Estimation of these multiple CFOs is a crucial and challenging task. In this paper, a dedicated pilot OFDM symbol is designed to estimate the CFOs. Based on the maximum likelihood (ML) principle, a method is derived to estimate the CFOs of all users by optimizing a multi-dimensional (MD) cost function. To reduce complexity, the MD optimization problem is turned into independent one-dimensional (ID) optimization problems based on a specially designed pilot OFDM symbol. It is proved that the CFOs are identifiable by optimizing the ID cost functions. Although the ID optimization is substantially simpler than the MD optimization, it may still be vulnerable to local minima/maxima and sometimes too complex for wireless applications. A fast algorithm is then derived, which finds the CFOs in closed forms and needs very low computational complexity.

In this letter, a Rake-based multiuser detection technique, consisting of multiuser single-path signal separation, time-delay estimation, and multipath combining, is proposed for quasi-synchronous spatial-division multiple-access (SDMA) systems. Time diversity is achieved for performance improvement. In addition, only the upper bounds of the channel length and the time delays are required. Simulation results verify the effectiveness of the proposed technique, as well as its robustness against overestimation of the maximum channel length and the maximum time delay

A semiblind method is proposed for simultaneously estimating the carrier frequency offsets (CFOs) and channels of an uplink multiuser multiple-input multiple-output (MIMO)–orthogonal frequency-division multiplexing (OFDM) system. By incorporating the CFOs into the transmitted symbols and channels, the MIMO–OFDM with CFO is remodeled into MIMO–OFDM without CFO. The known blind method for channel estimation [1] is then directly used for the remodeled system to obtain the shaped channels with an ambiguity matrix. A pilot OFDM block for each user is then exploited to resolve the CFOs and the ambiguity matrix. Two dedicated pilot designs, periodical and consecutive pilots, are discussed. Based on each pilot design and the estimated shaped channels, two methods are proposed to estimate the CFOs. As a result, based on the second-order statistics of the received signal and one pilot OFDM block, the CFOs and the channels are found simultaneously. Finally, a fast equalization method is given to recover the signals corrupted by the CFOs.

A semiblind method is proposed for simultaneously estimating the carrier frequency offsets (CFOs) and channels of an uplink multiuser multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system. By incorporating the CFOs into the transmitted symbols and channels, the MIMO-OFDM with CFO is remodeled into an MIMO-OFDM without CFO. The known blind method for channel estimation (Zeng and Ng in 2004) (Y. H. Zeng and T. S. Ng, ldquoA semi-blind channel estimation method for multi-user multi-antenna OFDM systems,rdquo IEEE Trans. Signal Process., vol. 52, no. 5, pp. 1419-1429, May 2004.) is then directly used for the remodeled system to obtain the shaped channels with an ambiguity matrix. A pilot OFDM block for each user is then exploited to resolve the CFOs and the ambiguity matrix. Two dedicated pilot designs, periodical and consecutive pilots, are discussed. Based on each pilot design and the estimated shaped channels, two methods are proposed to estimate the CFOs. As a result, based on the second-order statistics (SOS) of the received signal and one pilot OFDM block, the CFOs and channels are found simultaneously. Finally, a fast equalization method is given to recover the signals corrupted by the CFOs.

An asynchronous multiuser MIMO system with multipath channels and carrier frequency offsets (CFOs) or Doppler shifts is remodeled into another system. A blind method based on second order statistics is then used for estimating the shaped channels with ambiguity. Based on the shaped channels, specially designed pilots are proposed to find the CFOs. The same pilots are also used to resolve the time delays and the ambiguity in the estimated channels. After the CFOs, time delays and ambiguity are found, an equalization method based on the minimum mean square error (MMSE) criteria is obtained to recover the transmitted symbols. Only upper bounds for the channel orders and time delays are needed for implementing the algorithms, which makes them applicable to practical multiuser MIMO systems.

This paper addresses the problem of signal detection for a single carrier cyclic prefix (SC-CP) system. As a single carrier transmission, the received SC-CP blocks suffer from intersymbol interference (ISI). Although it is possible to deploy linear equalizers to mitigate the ISI, their performance is usually far away from the matched filter bound (MFB). The block iterative generalized decision feedback equalizer (BI-GDFE) is an effective interference cancellation scheme which generally shows performance improvements over linear equalizers. However, in order to achieve a significant gain, the channel must contain rich multipath components, and this may not necessarily be true in many systems. By applying cyclic delay diversity (CDD) to a SC-CP system, the frequency diversity of the system can be improved, and thus, the possible performance improvement by using a BI-GDFE detector is also enhanced. Computer simulations have shown that by incorporating CDD into the transmitter and using a BI-GDFE receiver, the BER performance of the system can approach the MFB within several iterations for high SNR region.

Efficient estimations of the carrier frequency offset (CFO) and channel are vital to orthogonal frequency division multiplexing (OFDM). In this paper, a new method based on limited channel length and null subspace is proposed to estimate the CFO in OFDM. The method needs only one OFDM block (symbol) as pilot. The identifiability of the method is analyzed and requirements for the pilot are found. The same pilot is also used for channel estimation. It is shown that a pilot with constant amplitude and random phases is the best choice for both channel and CFO estimation. Furthermore, an enhanced method is proposed by using multiple receiving antennas. Simulations demonstrate that the proposed methods are effective.

In this paper, we consider the performance enhancement of signal detection for single carrier-cyclic prefix (SC-CP) transmissions over correlated multiple inputs single outputs (MISO) channels. The block-iterative generalized decision feedback equalizer (BI-GDFE) receiver is used to combat the effect of intersymbol interference (ISI). While BI-GDFE outperforms linear equalizers, the improvement could be limited if the channel does not contain a high degree of frequency-selectivity. On the other hand, direct application of cyclic delay diversity (CDD) to a highly correlated MISO channel generally cannot increase the frequency diversity significantly. In this paper, we propose combining eigen-beamforming and CDD for frequency diversity enhancement, with which the performance of the BI-GDFE is also greatly improved. Computer simulations show that the proposed system not only offers diversity improvement but also beamforming gain. It is also observed that the BER performance of the proposed system can approach its matched filter bound (MFB)

A new fast algorithm is proposed for estimating the CFO based on the pilot structure in [1], [2]. The new method first identifies a coarse estimation of the CFO and then refines it by a least square (LS) method. It always gives a more accurate estimation than the multi-stage method in [2] and is better than the method in [1] at low SNR. It only slightly worse than the maximum likelihood estimator at high SNR. Furthermore, the proposed method can be used for CFO tracking. Also, in this paper, channel estimation by using the same pilot block is considered. The best values for the non-null subcarriers are found to optimize the channel estimation and peak to average power ratio (PAPR) simultaneously. Simulations demonstrate that the proposed methods are effective.

In this paper, signal detection with time delay estimation for quasi-synchronous MIMO systems on multipath channels is considered. The paper first formulates the problem by shifting each user's signals so that previous results on synchronized system for ISI cancellation based on second-order statistics of the received signals can be applied. The first step effectively reduces the quasi-synchronous system on the multipath channels to the quasi-synchronous system on single-path channels. In the second step, an equivalent model is constructed with the delay information embedded in the reduced channel model. A simultaneous time delay estimation and multiuser detection algorithm is then proposed. The proposed technique can easily be implemented in practical situations as perfect synchronization and knowledge of the channel length and the time delays are not required. Simulation results show that the proposed technique can achieve good performance and is not sensitive to overestimation of the maximum channel length and the maximum time delay.

Many known second-order statistics based blind algorithms for MIMO channel estimation are sensitive to channel order overestimations. To overcome this problem, an algorithm is proposed in Gazzah, H et al. (2002) for SIMO system only, and then a simple generalization of it to MIMO system is presented. In this paper, improvements and refinements on the algorithm are given, which makes the method robust to noise and round-off error. The method can give estimations of all channel impulse responses subject to a scalar matrix ambiguity when only an upper bound for all MIMO channel orders is known.

A semi-blind method is proposed for simultaneously estimating the carrier frequency offsets (CFOs) and channels of an uplink multi-user MIMO-OFDM system. By incorporating the CFOs into the transmitted symbols and channels, the MIMO-OFDM with CFO is re-modeled into a MIMO-OFDM without CFO. The known blind method for channel estimation (Y.H. Zeng and T.S. Ng, 2004) is then directly used for the re-modeled system to obtain the shaped channels with an ambiguity matrix. A dedicated pilot OFDM block for each user is then designed to resolve the CFOs and the ambiguity matrix. As a result, based on the second-order statistics (SOS) of the received signal and one pilot OFDM block, the CFOs and channels are found simultaneously

Cyclic prefixed single carrier (CP-SC) has emerged as a promising technique for wideband wireless communication. The cyclic prefix (CP) in CP-SC costs some bandwidth, but it cannot be used for channel estimation in fast-variant channel environment. In this letter, a new scheme is proposed for CP-SC which not only adds a CP but also a suffix. With the aid of the CP and suffix, channel responses can be resolved by the fast Fourier transform. The additional known symbols also ensure input symbols recoverable regardless of the channel null locations. Better synchronization is also possible.

A subspace-based blind method is proposed for estimating the channel responses of a STC-OFDM system with two transmit antennas and one receive antenna. It gives estimations to the two channel responses subject to two ambiguity parameters. The method is valid whether the two channel transfer functions are coprime or not and does not require precise channel order information (only an upper bound for the orders is required). Furthermore, a method is presented to resolve the ambiguities by using two or more pilot symbols. Simulations show that the methods are effective and robust.

Muquet et al. (2002) have proposed a subspace-based blind method for estimating the channel responses of cyclic-prefixed OFDM systems. Their proof for the channel identifiability uses the roots of channel transfer function and therefore depends on the channel order. When only an upper bound is known for the channel order, the proof fails. In this letter, a new proof for the identifiability is given which does not require knowledge of the precise channel order and can handle various choices of length of the OFDM block.

A general method is proposed to factor a discrete W transform (DWT) into lifting steps and additions. Then, based on the relationships among various types of discrete sinusoidal transforms, other types of transforms such as the discrete Fourier transform (DFT) and discrete cosine transform (DCT) are factored into lifting steps and additions. After approximating the lifting matrices, we get various types of new integer discrete transforms such as IntDWT, IntDFT and IntDCT which are floating-point multiplication free. Transforms which map integer to integer are also proposed. Fast algorithms are given for the new transforms and their computational complexities are analyzed. Based on a polynomial transform and an index mapping, multi-dimensional integer transforms are presented with especially low computational complexity.

The harmonic transform is designed for harmonic signals, which are composed of a base tone and some harmonics (e.g., voiced speech). As a generalization of the Fourier transform, the harmonic transform represents signals by the sum of a base tone and some harmonics, which may give more concise results for harmonic signals than the Fourier transform. Some experiments of speech signals are used to demonstrate the advantages of the harmonic transform on harmonic signal processing.

Polynomial algorithms for multidimensional discrete Hartley transform (MD-DHT) are proposed. Based on the multidimensional polynomial Transform, the MD-DHT is converted into a series of one-dimensional type-II discrete W transforms (DWT). The algorithms are clearly described and detailed analysis of the computational complexity is also presented, The proposed algorithm achieves considerable savings on the number of operations. The number of multiplications for computing an r-dimensional DHT is only 1/r times that needed by the row-column method. The number of additions is also reduced considerably.