This letter investigates channel capacity bounds and constellation shaping for memoryless mixed Gaussian-impulsive noise. We derive capacity bounds utilizing the entropy power inequality and the dual capacity expression. We demonstrate that these bounds become asymptotically tight, yielding a closed-form asymptotic expression for the channel capacity. Guided by this theoretical analysis, we propose a low-complexity, non-iterative constellation shaping method. Simulation results validate the tightness of the derived bounds and confirm that the proposed shaped constellation achieves the highest mutual information among the considered baselines.

Communication systems operating in mixed white Gaussian and impulsive noise (IN) environments suffer from severe performance degradation, rendering conventional constellations suboptimal. In this paper, we propose a joint geometric and probabilistic shaping scheme designed to maximize the cutoff rate (CR) under such mixed noise conditions. Instead of relying on loose approximations, we derive rigorous closed-form lower and upper bounds for the CR. To overcome the analytical intractability of the integral terms, we develop a novel piecewise linear approximation that exploits the algebraic structure of the noise model, yielding tractable objectives for optimization. Subsequently, a projected gradient-based algorithm is employed to jointly optimize the constellation geometry and probability mass function. Numerical results demonstrate that our derived bounds are tight, and the proposed hybrid shaping scheme significantly outperforms conventional baseline schemes, especially when the input power is not large enough.

Ensuring reliable semantic communication under malicious jamming attacks is paramount for mission-critical intelligent applications. Traditional jamming mitigation methods, including zeroing, clipping, and scaling suppression, inherently risk degrading essential semantic features while suppressing interference. This degradation can significantly impact task-specific accuracy and overall operational effectiveness. To address this issue, we introduce a dual-strategy framework designed to address impulsive and continuous jamming scenarios in semantic-aware systems. The first strategy employs dynamic symbol-level adaptation at both transmitter and receiver ends to avoid impulsive jamming, while the second incorporates post-suppression semantic reinforcement at the receiver to counteract residual distortions caused by continuous jamming. Simulation results demonstrate that the proposed framework substantially enhances the robustness of semantic communication systems against malicious jamming attacks, while maintaining both system performance and information integrity compared to conventional approaches.

Radio map estimation (RME), also known as spectrum cartography (SC), aims to estimate instantaneous signal power distribution over a certain space-frequency region. Recent RME approaches typically discretize the to-be-estimated radio map into grid cells under a fixed resolution. Meshing subtly adds structural priors, e.g., low-rankness or deep image priors, to the radio map. These priors can effectively enhance the performance of RME, especially in blind scenarios. However, the downside is all the locations in a grid cell will share the same signal power, which is overly simplistic and contradict the continuity nature of power propagation. This work puts forth a blind grid-free RME framework. We introduce implicit continuous representation (ICR), which learns a mapping between spatial coordinates and power propagation pattern of each transmitter. This mechanism conceptually enables estimating the signal power at any spatial location within a certain region. With some model-based interpretations and designated optimization criteria, the ICR-based framework could be fully unsupervised, using only sampled data for training. This implies that our approach is not prone to the prevalent generalizability issue. Experiments under simulated and ray-tracing datasets verify the effectiveness of the proposed approach.

Bursty mixed noise is typically characterized by the combination of white Gaussian noise (WGN) and non-Gaussian impulsive noise (IN) with memory. The performance of communication systems can experience significant degradation when conventional signal detection algorithms designed for Gaussian noise are applied in the presence of bursty mixed noise. To handle this challenge, we investigate demodulation techniques and conduct a theoretical performance analysis under bursty mixed noise. Specifically, we utilize the statistical model of channel noise to derive demodulation methods for both linear and nonlinear modulation schemes. We further analyze the theoretical error probability for M-ary PSK/QAM and provide closed-form expressions. For MSK/CPM modulations, we derive asymptotic lower and upper bounds for the error probability, accompanied by analytical expressions. Simulation results demonstrate that the proposed demodulation methods outperform baseline techniques by more than 1.9 dB when the error probability approaches 10-3. Additionally, the theoretical analysis closely aligns with the simulation results.

In many communication scenarios, the communication signals are contaminated by both Non-Gaussian impulsive noise (IN) with memory and white Gaussian noise (WGN), i.e., bursty mixed Gaussian-impulsive noise. To achieve desirable communication performance, it is necessary to establish an accurate and tractable model of the bursty mixed noise. In this paper, we propose a closed-form and heavy-tail multivariate probability density function (PDF) to model the bursty mixed noise. The proposed model is the weighted combination of the Gaussian distribution and student distribution that separately describe the background WGN and the bursty IN. We design the model parameters estimation approach based on the empirical characteristic function of the proposed noise model. The performances of parameter estimation are also analyzed. Numerical results show that our proposed bursty mixed noise model can well match the measured noise in practical scenarios. Meanwhile, the estimation performances in terms of normalized mean square error (NMSE) verify the effectiveness of presented parameters estimation methods.

Spectrum cartography (SC) and spectrum prediction (SP) are homologous tasks in the spectrum sensing (SS) realm, where the former reconstructs multi-domain radio maps from sparse historical samples, and the latter predicts upcoming radio frequency status. Nonetheless, the two tasks are oftentimes considered mutually exclusive and tackled separately in existing literature. This work artfully puts forth a joint spectrum cartography and prediction (JSCP) framework that addresses the two tasks in a unified way. Our idea rests upon the tensor (space-frequency-time) structure of radio maps, where we incorporate a latent temporal regression system into the relative tensor decomposition factor. Such a regression system enjoys a self-supervised optimization flavor, and is utilized for enhancing collaborative SC/SP performances. We propose both theoretical guarantee and effective algorithm to solve the resultant problems of JSCP framework. Simulation results verify that our approaches have more promising estimation accuracy and generalizability compared with a variety of baselines.

Spectrum Cartography (SC) crafts multi-domain (typically space, frequency and time) radio frequency (RF) map from limited sensor measurements. Recent state-of-the-art SC methods have assumed stationary RF environments over a period of time, which can be inadequate in real implementation. This work considers dynamic SC aiming at scenarios where the RF environment is time varying. Our insight rests upon that the RF map is contributed by all the emitters in the scenario of interests, and thereby individually capturing the dynamic propagation map of each emitter captures the whole RF map well. This idea evolves to an emitter separation-based tensor completion framework, under which we put forth effective algorithm to solve the resultant problem. Simulations results verify that the proposed method has decisive advantage in terms of estimation accuracy, for both mild and harsher conditions.

Bursty mixed noise composed of impulsive noise with memory and white gaussian noise exists in many practical applications. However, existing signal demodulation algorithms are designed without considering the characteristics of the bursty mixed noise, resulting in performance degradation. Therefore, an accurate model for the bursty mixed noise needs to be built to design the corresponding signal demodulation algorithms. In this paper, we propose a novel statistical model based on the mul-tivariate student distribution for the bursty mixed noise, based on which we design the maximum likelihood (ML) demodulation method and analyze the theoretical bit error rate performance. Simulation results show that the proposed model can describe the bursty noise well and there is remarkable demodulation gain of the ML method as compared with conventional ones.

Orthogonal frequency division multiplexing (OFDM) system is susceptible to carrier frequency offset (CFO). Therefore, CFO estimation and compensation is a necessary component for OFDM systems. In high-mobility environments, extremely large Doppler frequency shift will introduce severe CFO that is far beyond the subcarrier spacing. Conventional CFO estimation algorithms typically estimate the integer and fractional parts of CFO separately. This strategy is computationally expensive and time-consuming. To avert this deficiency, inspired by the Chinese remainder theorem, we propose a maximum likelihood estimation approach (CRT-MLE) to simultaneously estimate the integer and fractional parts of OFDM CFO. For the proposed CRT-MLE method, the CFO can be directly estimated via multiple sequences of different lengths. This approach can achieve a very large CFO estimation range up to the total number of OFDM subcarriers without significant additional computational complexity. Furthermore, we show that the proposed algorithm can approach the Cramér-Rao Bound. Finally, extensive simulation results show that our proposed CRT-MLE method is advantageous regarding estimation range and performance compared to baselines.

Spectrum cartography (SC) recovers a multi-domain radio map (RM), from limited sensor measurements. Existing mesh-grid-free SC methods rely on the key step that localizes the emitters on an aggregated 2D spatial loss field (SLF). As a consequence, they need the emitters being sufficiently scattered to identify them, and also fail to cover a multi-domain SC problem. This work puts forth an emitter disaggregation-based approach. The key idea is using the non-negative matrix factorization (NMF) to uniquely separate the power spectrum density (PSD) and SLF of each emitter, from high-dimensional sensor feedback. Then, each emitter can be localized by only using its own SLF, instead the aggregated one. Based on the localization information, the SLFs can be completed by using an approximated propagation model, and are assembled with the PSDs to reconstruct a multi-domain RM. This way, the defects of mesh-grid-free SC can be sufficiently overcame. Simulations verified that the propose method can significantly reduce the estimation error of SC, under multiple harsh environments.

Minimum shift keying (MSK) is a continuous phase modulation with both high spectrum efficiency and power efficiency, and has been widely used in wireless communication systems. Existing demodulation algorithms of MSK signals mainly consider either white Gaussian noise (WGN) or non-Gaussian impulsive noise (IN). However, the MSK signals suffer from the mixture of WGN and IN in many practical scenarios. For these environments, directly applying these demodulation algorithms could not achieve desirable bit error rate (BER) performance. In this paper, we address the problem of demodulation of MSK signals under the mixture of WGN and IN. Based on the approximate probability density function, the Viterbi algorithm based demodulation scheme is proposed by calculating the maximum-likelihood (ML) branch metric and the corresponding theoretical BER performance is analyzed. Furthermore, several popular branch metrics are compared in terms of both BER and complexity. Simulations results show that the proposed ML branch metric can obtain the best BER performance under all the scenarios, and the theoretical BER matches the simulation results well.

Communication systems suffer from the mixed noise which contains both non-Gaussian impulsive noise (IN) and white Gaussian noise (WGN) in many scenarios. However, there is little literature on the channel capacity under the mixed noise. In this paper, we analyze the existence of the capacity under p-th moment constraint and prove that there are only finite mass points in capacity-achieving distribution. Furthermore, we discuss the capacity relating to specific modulation format under the mixed noise and derive a lower bound with closed form. Numerical results reveal that the capacity of the mixed noise channel will decrease as the impulsiveness of the mixed noise becomes significant and the obtained analytical capacity lower bound is tight.

In many practical applications, the communication system suffers from mixed Gaussian-impulsive noise consisting of both Gaussian white noise and non-Gaussian impulsive noise. Obtaining the optimal signal detection algorithm under these scenarios requires the estimate of the mixed Gaussian-impulsive noise parameters. Unfortunately, the estimation accuracy will deteriorate in the presence of the transmitted signal. To solve the problem, we propose a blind source separation method with a neural network, namely U-net++, to separate the transmitted signal from the mixed noise. Then, the parameter estimation can be implemented with the recovered noise samples. An adaptive clipping preprocessing module and a novel loss function of the network are designed based on the statistical property of the mixed noise. Results show that our algorithm outperforms existing baselines on both source separation and parameter estimation.

Existing E2E systems would suffer performance degradation when applied in the environments where the channel conditions deviate from the trained ones. To address this problem, we propose a comprehensive method to develop an E2E communication system that possesses strong adaptability under dynamically varying unknown environments. From the system structure prospective, our system consists of the impulse generative adversarial network (IGAN) and transceiver. Within the E2E system, a novel transceiver is proposed, which maps information bit sequences to symbol vectors to introduce the inter-symbols correlation such that the signals can be detected without pilots. Hence, the transceiver is more flexible in a changing fading environment while achieving higher spectrum efficiency. On top of that, the model-agnostic meta-learning (MAML) is utilized to meta-train the IGAN to enable fast adaptation to practical noise samples. The transceiver is then general-trained based on the designed fading channel set that includes various time and frequency selectivity and the simulated noise set. Simulation results show that our proposed E2E system can outperform conventional approaches both in terms of BER, overheads and adaptability.

Tensor-based spectrum cartography (SC) has received increasing interests for recovering multi-dimensional radio map (RM) from sparse measurements. However, existing tensor-based SC methods largely depends on an ideal assumption, that the sparse measurements are regularly located on grids. However, this assumption is largely unrealistic since the RM is continuous in essence, and can be measured at arbitrary positions deviating from the pre-divided grids. This work addresses the problem of parametric SC from irregular off-grid samplings. The main idea is combining interpolation with the multi-linear rank-$(L,L,1)$block-term tensor decomposition (LL1). The interpolation is first adopted to guarantee the uniqueness of LL1, under the guidance of the proposed sampling pattern theorem. Then, the power spectrum density (PSD) and spatial loss field (SLF) of each emitter can be smoothly estimated, and SC is completed via the aggregation model. For the whole procedure, the uncertainty derived from interpolation is grid-wisely specified, and imposed as a restriction. Simulations verified that the proposed method outperforms the baselines based on on-grid samplings in harsher environments.

Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis. In this paper, we propose a new waveform design paradigm with the knowledge graph (KG)-based intelligent recommendation system. The proposed paradigm aims to improve the design efficiency by structural characterization of existing waveforms and intelligently utilizing the knowledge learned from them. To achieve this goal, we first build a communication waveform knowledge graph (CWKG) with a first-order neighbor node, for which both structured semantic knowledge and numerical parameters of a waveform are integrated by representation learning. Based on the developed CWKG, we further propose an intelligent communication waveform recommendation system (CWRS) to generate waveform candidates. In the CWRS, an improved involution1D operator is introduced according to the characteristics of KG-based waveform representation, and the multi-head self-attention is adopted to weigh the influence of various components. Meanwhile, multilayer perceptron-based collaborative filtering is used to evaluate the matching degree between the requirement and the waveform candidate. Simulation results show that the proposed CWKG-based CWRS can recommend waveform candidates with high reliability, and utilize existing waveform knowledge much more effectively than existing methods.

Joint spectrum cartography and disaggregation from sparse spatial observations has been proven to be theoretically feasible based on block-term tensor decomposition (BTD) model. However, the existing BTD framework suffers from inherent drawbacks in terms of numerical stability, complexity and noise robustness. To combat with these drawbacks, we propose a new Constrained-BTD (CBTD) framework in this letter by fully utilizing practical traits of geographical power density spectrum (PSD) and spatial loss field (SLF). The cornerstone of CBTD framework is formulating the joint PSD and SLF estimation as a constrained matrix factorization problem, instead of addressing the factors of BTD. Further, a projection gradient-based (PG) algorithm, which has sublinear convergence, is proposed to handle the restrictions of PSD and SLF by projection on manifolds. Compared with the baseline methods, simulations verify that the proposed approach obtains better performances in terms of stability, complexity and noise robustness.

With the development of software-defined radio (SDR) and artificial intelligence (AI), smart jammers are becoming more and more powerful, and have cognitive and intelligent capacities such as spectrum sensing, learning and reconfigurability. To cope with the AI-enabled smart jammers, this paper investigates the naive weakness of smart jamming strategies and proposes a deceptively adversarial attack (DAA) based proactively intelligent anti-jamming algorithm to ensure reliable communication for wireless communication networks (WCNs). In contrast to the existing intelligent schemes, the proposed DAA-based anti-jamming algorithm focus on minimizing total reward of the smart jammer and destroy their spectrum sensing and learning abilities by delivering a carefully designed deceptively adversarial signal. We implement the DAA-based anti-jamming algorithm in both white-box and black-box settings according to the degree of information acquisition from the smart jammers, respectively. Simulation results show that system performance in terms of anti-jamming reward, computational complexity and anti-jamming efficiency can be significantly improved compared with the existing intelligent schemes.

Spectrum cartography aims to estimate multidimensional radio map from limited samples taken over a geographical region. The radio map, formulated as a third-order tensor, admits an approximated low rank CANDECOMP/PARAFAC (CP) decomposition (CPD). We formulate the spectrum cartography problem as a low-CP-rank tensor completion problem. To handle the sequential spectrum observations which have not been addressed in existing research, the time-varying spectrum cartography problem is handled via online tensor completion based on incremental CPD and then solved by block coordinate descent approach. In addition, we show that the incremental CPD generates a sequence of latent factors estimates converging to a stationary point. Numerical simulations show that our proposed algorithm has better performance than the baseline methods and is suitable for realtime radio map estimation.

An ultra-massive phased array can be deployed in high-throughput millimeter-wave (mmWave) communication systems to increase the transmission distance. However, when the signal bandwidth is large, the antenna array response changes with the frequency, causing beam squint. In this paper, we investigate the beam squint effect on a high-throughput mmWave communication system with the single-carrier frequency-domain equalization transmission scheme. Specifically, we first view analog beamforming and the physical channel as a spatial equivalent channel. The characteristics of the spatial equivalent channel are analyzed which behaves like frequency-selective fading. To eliminate the deep fading points in the spatial equivalent channel, an advanced analog beamforming method is proposed based on the Zadoff-Chu (ZC) sequence. Then, the low-complexity linear zero-forcing and minimum mean squared error equalizers are considered at the receiver. Simulation results indicate that the proposed ZC-based analog beamforming method can effectively mitigate the performance loss by the beam squint.

This paper addresses the issue of anti-jamming communication in dynamic and unknown environment for ultradense network (UDN). A deep reinforcement learning based antijamming algorithm is proposed by exploiting the frequency-hopping technology to cope with the jamming attack without estimating the mode and parameters of the jamming. In contrast to existing learning based schemes, in which the anti-jamming action, e.g., frequency hopping and transmit power adjustment, is taken from a pre-defined discrete anti-jamming strategy space, the proposed anti-jamming algorithm takes anti-jamming action from the continuous action space based on deterministic policy gradient. We represent this anti-jamming algorithm in an actor-critic framework, for which a convolution neural network (CNN) is adopted as the actor for anti-jamming action selection, and a deep neural network (DNN) is used as the critic for value estimation. We have implemented the proposed algorithm based on Google TensorFlow. Simulation results show that the system performance can be significantly improved by the proposed algorithm.

In some communication scenarios, non-Gaussian impulsive noise is dominant, for which conventional Gaussian noise based signal detection algorithm cannot achieve desirable performance due to the mismatch of noise model. Therefore, it is necessary to develop novel signal detection algorithm for communication receiver in non-Gaussian impulsive noise. In this paper, we focus on the detection of continuous phase frequency shift keying (CPFSK) signals for bursty impulsive noise, which is modeled as the stationary m-order α-sub-Gaussian (αSG(m)) process. For coherent detection, a sequence detection algorithm is proposed based on Viterbi algorithm by utilizing the Markov property of αSG(m) noise. To reduce the computational complexity, a multidimensional myriad branch measure is proposed to replace the complicated Maximum Likelihood(ML) branch measure. For non-coherent detection, a multiple symbols aided method is applied to minimize the detection error. The multidimensional myriad measure is also used. For the sake of comparison, the Gaussian measure derived from Gaussian distributed noise is also considered in both coherent and non-coherent detection. The simulation results show that the performance of the myriad measure based algorithm can closely approach that of the ML measure based algorithm, and is much better than that of the Gaussian measure based algorithm.

With the development of demands in various applications for array signal processing, the number of antennas in the array and the signal bandwidth are increasing. Several direction-of-arrival (DOA) estimation algorithms have been proposed for wideband signal sources. However, the wideband algorithms are generally more complex compared with the narrowband algorithms, especially in massive antenna array systems. In this paper, we focus on analyzing the bias of narrowband multiple signal classification (MUSIC) algorithm to obtain an effective bandwidth scope in the case of non-zero bandwidth signal sources, which can guide the selection of wideband and narrowband DOA estimation algorithm in real applications. To end it, we describe the time-domain array spatial covariance matrix in non-zero bandwidth case and analyze the trend of its eigenvalues with the signal fractional bandwidth. And then an approximation DOA estimation error expression of the MUSIC algorithm is derived based on Taylor series expansion of the null spatial spectrum function. The simulation results show that the narrowband MUSIC algorithm will appear to two extreme cases, which are approximately unbiased and completely invalid in massive antenna array systems.

Hybrid beamforming architecture is a practical implementation in millimeter-wave (mmWave) communication systems with large-scale antenna arrays for future fifth-generation (5G) cellular networks, which offers a compromise between hardware complexity and system performance. Fully-connected and sub-connected are two popular connected structures in the hybrid beamforming architecture. However, the hardware complexity and the required number of phase shifters in the fully-connected structure is rather high when the number of RF chains increases. Sub-connected structure can effectively decrease the required number of phase shifters but at the expense of beamforming gain loss of each RF chain. In this paper, we consider an overlapped subarray structure between fully-connected and sub-connected structures and determine how many antenna elements should be connected for each RF chain. For the mmWave downlink multi-user multiple-input multiple-out (MIMO) communication, we design a two-stage hybrid beamforming algorithm based on the overlapped subarray structure. Simulation results indicate that there exists a best cost-effective overlapped subarray spacing associated with the required number of phase shifters for the sparse mmWave channel, which provide a guideline for the design of hybrid beamforming architecture.

In many communication scenarios, the signal is corrupted by impulsive noise, which is modeled as symmetric α- stable (SαS) distribution. In this paper, we investigate the signal pre-processing and detection techniques in SαS noise. Due to its near-optimal performance, the myriad filter is a commonly used pre-processing method in SαS nolse. However, due to its memory, the myriad filter has high computation complexity. Hence, we propose an optimal memoryless pre-processing in this paper. We provide the criteria of optimizing the memoryless pre-processing function and propose a feasible optimization procedure. Based on the obtained pre-processing method, the signal detection method for SαS noise is further investigated. In order to evaluate the performance of the pre-processing based signal detection, the optimal signal detection in SαS noise is also studied. In our work, it is assumed that the pulse shape of the transmitted signal occupies multiple symbol periods. In this case, each transmitted symbol will have influence on detecting its following symbols. Thus, a multiple symbols aided maximum likelihood detection (MLD) is proposed, and its theoretical symbol error rate (SER) is also analyzed. Simulation results verify that the proposed memoryless pre-processing method can achieve nearly the same waveform recovery performance as myriad filter. Moreover, the SER performance of the proposed memoryless pre-processing based signal detection can approach that of optimal MLD.

Analog beamforming with large-scale phased arrays is an attractive technology for long-range millimeter- wave (mmWave) communication. However, the beam squint will emerge when the signal bandwidth and the number of antenna increase. In this end, we develop a digital compensation wideband analog beamforming architecture to eliminate the beam squint. Specifically, we view the array response and the analog beamforming as a frequency-selective channel, which is compensated in the digital baseband. The signal direction is estimated to design the analog beamforming vector and the digital compensation values using the incoherent signal subspace (ISS) wideband direction-of-arrival (DOA) estimation algorithm with 1-bit quantization. Finally, simulation results indicate that the proposed algorithm is valid in the large-scale antenna arrays, and the beam squint effect can be eliminated effectively by the digital compensation method.

In this letter, the optimal and suboptimal nonlinear processing for correlation-based signal detection is addressed in symmetric alpha-stable noise. By maximizing the correlator output signal-to-noise ratio, a constrained functional optimization problem is established. As this optimization problem is hard to get analytical solution, we apply finite discretization to it and prove that the resulting approximation problem is a convex quadratic programming problem. This optimal nonlinear processing provides performance bound and design criteria for correlator detection. Based on the noise parameter α and the order statistic of received data, we further propose an adaptive method to determine the optimal threshold of the commonly used soft limiter. Simulation results show that the proposed method achieves near-optimal performance.

Energy efficiency has now become a key pillar in the design of communication networks. With millions more base stations and billions of connected devices, the demand for energy-efficient system design and operation will be even more compelling in the fifth generation (5G) communication network.In this paper, we focus on maximizing the downlink system energy efficiency (EE) of the 5G ultra dense network through efficient resource allocation algorithms. First, a constrained EE maximize problem is formulated. However, the resulting optimization problem is challenging due to its non-convex nature. To overcome this issue, the transformations based on fractional programming theory are applied, so that the primal problem can be converted into a convex form. Then, we adopt a centralized algorithm to solve the optimization problem and get the global optimal EE. In order to reduce the complexity, an efficient distributed algorithm based on alternating direction method of multipliers (ADMM) is further proposed. The simulation results show that both the centralized algorithm and the distributed algorithm converge to the same EE, but the latter one has lower computational complexity.

Impulsive noise modeled as symmetric α-stable SαS distribution is commonly seen in many practical communication scenarios. In this paper, we focus on the synchronization of single-carrier signals in the mixture of SαS impulsive noise and Gaussian noise. We first derive the Cramér-Rao lower bound (CRLB) of the joint estimation of carrier phase and timing offsets for both liner modulation (LM) signals and continuous phase modulation (CPM) signals. In order to minimize the CRLB, the optimal training sequence (TS) is then designed for the synchronization of the LM signals and the CPM signals, respectively. We further propose a practical low-order cross-correlation based synchronization algorithm, which can be used for both LM and CPM signals. Numerical simulations show that our designed TS can outperform the pseudo-random (PN) sequence. By taking minimum shift keying (MSK) signals for instance, simulation results show that the performance of our proposed synchronization algorithm has negligible gap with the CRLB.

We study the optimal precoding for a full-duplex (FD) system, where one FD multi-antenna base station (BS) respectively transmits to and receives from two half-duplex single-antenna mobile users (MUs) on the same time slot and frequency band. At the FD BS, the received signal from the desired MU is severely affected by the extremely strong self-interference (SI) from its transmit antennas to the receive antennas. In the presence of residual SI after imperfect SI cancellation, the downlink transmission rate maximization problem subject to a targeted uplink rate is formulated as a non-convex optimization problem to characterize the achievable rate region for the considered system. Considering the case in which the SI channel is strongly correlated, the above problem is transformed into a convex problem by exploiting the rank-one property of the SI channel, which can be solved efficiently. Finally, numerical results validate the effectiveness of the proposed scheme.

Symmetric α-stable (SαS) distribution noise is widely used to model co-channel and network interference in wireless communication systems. As robust and adaptive techniques, myriad filtering (MyF) and spherically symmetric vector MyF have been applied to suppress univariate and spherically symmetric multivariate SαS distribution noise, respectively. At a communication receiver, the received band-pass noisy signals are usually down-converted to complex baseband, and the resulted complex baseband SαS noise has been demonstrated not to be circularly symmetric. In this paper, we proposed a complex baseband MyF (CBMyF) to suppress the non- circularly symmetric complex baseband SαS noise. Besides, there are few researches with respect to the demodulation of memory modulation signals, e.g., minimum shift keying (MSK) signal, under SαS noise. Thus, based on CBMyF, we proposed coherent and non-coherent MSK demodulation algorithms under SαS noise in this paper. Furthermore, maximum likelihood (ML) MSK demodulation under SαS noise also been proposed. Simulation results show that the bit error rate (BER) performance of the proposed CBMyF based MSK demodulation can closely approach that of ML demodulation. Meanwhile, the proposed CBMyF is compared with the common used clipper, and the results validate its advantage of robustness and adaptivity.

Carrier Interferometry Orthogonal Frequency Division Multiplexing (CI/OFDM) can mitigate the problem of high peak-to-average power ratio (PAPR) of OFDM signal. In practice, some subcarriers have to be deactivated in order to avoid harmful interference to licensed system in cognitive radio (CR) context. For these cases, Non-Continuous CI/OFDM (NC-CI/OFDM) has recently been proposed, for which some subcarriers are unused intentionally. Unfortunately, the PAPR performance of current NC-CI/OFDM schemes is sensitive to the distribution of unused subcarriers. In this paper, we propose a new spectrum adaptive NC-CI/OFDM system with improved CI spreading scheme to achieve desired PAPR performance. A novel iterative method is proposed herein to design pilot symbols so that desirable balance between the channel estimation performance and the PAPR can be obtained. We further propose a minimum mean square error (MMSE) based soft signal detection and its complexity-reduced implementation. Simulation results show that our proposed new NC-CI/OFDM scheme can achieve significantly better PAPR performance than that of existing schemes, while its bit-error-rate (BER) performance close approaches that of the existing schemes with negligible performance gap.

Considering the radio frequency (RF) self-interference cancellation of the full duplex transceiver, which can transmit and receive simultaneously at the same frequency band. The current studies mainly focus on the feasibility verification, most of which are realized by manual adjustment of the self-interference parameters, and lack of the theoretical analysis of the interference cancellation capability. To solve this problem, an adaptive least mean square (ALMS) algorithm for the self-interference cancellation at the RF stage is presented in this paper. Besides, the convergence and steady state performances of the ALMS algorithm are proved and analyzed, respectively. Analysis and simulation results show that the ALMS algorithm can achieve significant cancellation of the self-interference at the RF stage. Moreover, it is revealed that the interference cancellation performance and convergence speed of the ALMS algorithm are related with the following parameters: the duration per iteration, normalized step size, as well as the ratio of the interference power and total power of the desired signal plus noise.

As a fundamental technology of cognitive radio, the spectrum sensing scheme is required to perform well in low signal-to-noise ratio (SNR) environments. In this paper, we propose a novel spectrum sensing method based on adaptive bistable stochastic resonance (A-BSR). By maximizing the SNR gain introduced by the BSR system, we first present an A-BSR system, of which the parameters can be adaptively adjusted based on the background noise. Then, we propose an A-BSR aided spectrum sensing scheme by passing the received signal through an A-BSR system to improve SNR. Based on the characteristics of A-BSR system output in frequency and time domains, we further propose two energy detection (ED)-based spectrum sensing algorithms. As the output of an A-BSR system given a noise input is concentrated around frequency zero, we propose a modified ED based on periodogram (P-ED) in frequency domain. Moreover, as the A-BSR system output for noise input has approximate constant amplitude in time domain, a novel deviation-based ED (D-ED) is proposed. Extensive simulation results show that the proposed A-BSR aided spectrum sensing scheme can achieve much better performance than the existing ED and BSR aided spectrum sensing schemes with fixed parameters, especially under very low SNR region.

Spectrum sensing is a fundamental technology to detect the presence of primary users (PU) in cognitive radio. Usually, the spectrum sensing scheme is required to have good performance even in extremely low signal-to-noise ratio (SNR) environments. In this paper, we proposed a novel spectrum sensing method based on adaptive bistable stochastic resonance (A-BSR) to meet this requirement. For the proposed A-BSR aided spectrum sensing scheme, the received signal is first passed through an A-BSR system to improve SNR. Furthermore, we proposed a novel simple but efficient energy detection (ED) scheme to make decision on the existence of PU's signal. Extensive simulation results show that the proposed A-BSR aided novel ED based spectrum sensing can achieve much better performance than exiting ED and bistable stochastic resonance aided ED based spectrum sensing schemes.

In this paper, we investigated the cooperative sensing in a heterogeneous cognitive radio (CR) network scenario, where each secondary user (SU) may be equipped with different number of receive antennas and have different signal processing capacity, e.g., sampling rate. By considering the discrepancy in sensing reliability of different SUs, we extended the existing researches to propose an optimal cooperative sensing (OCS) scheme based on normalized energy detection (NED) by considering reporting errors, i.e., the fusion center (FC) may receive erroneous results from some SUs due to channel fading. It is derived that the proposed OCS scheme is the linear combination of modified local detection statistics. The optimal combining coefficient is just a function of the numbers of the antennas and the received signal samples, the signal to noise ratio (SNR) and the variance of the reporting errors at each SU. Meanwhile, the performance of the proposed OCS scheme, the well-known equal gain combination (EGC) method and the maximum normalized energy (MNE) detector with reporting errors is analytically derived, respectively. Furthermore, to avoid the prior information of each SU's SNR and the variance of the reporting errors, and simplify the decision-making as well as threshold setting, a sub-optimal cooperative spectrum sensing (SOCS) is proposed. Numerical results show that the proposed OCS and SOCS schemes all perform much better than the existing EGC and MNE methods.

ABSTRACT For wireless multiple‐input multiple‐output (MIMO) communications systems, both channel estimation error and spatial channel correlation should be considered when designing an effective signal detection system. In this paper, we propose a new soft‐output MMSE based Vertical Bell Laboratories Layered Space‐Time (V‐BLAST) receiver for spatially‐correlated Rician fading MIMO channels. In this novel receiver, not only the channel estimation errors and channel correlation but also the residual interference cancellation errors are taken into consideration in the computation of the MMSE filter and the log‐likelihood ratio (LLR) of each coded bit. More importantly, our proposed receiver generalizes all existing soft‐output MMSE V‐BLAST receivers, in the sense that, previously proposed soft‐output MMSE V‐BLAST receivers can be derived as the reduced forms of our receiver when the above three considered factors are partially or fully simplified. Simulation results show that the proposed soft‐output MMSE V‐BLAST receiver outperforms the existing receivers with a considerable gain in terms of bit‐error‐rate (BER) performance. Copyright © 2011 John Wiley & Sons, Ltd.

Spectrum sensing is a fundamental technology to detect the presence of primary user in cognitive radio networks. Usually, the requirements for spectrum sensing are very stringent. It requires that the spectrum sensing scheme has a good performance even in extremely low signal-to-noise ratio (SNR) environments. In this paper, we propose a novel spectrum sensing method based on stochastic resonance (SR) noise enhance detection (NED) to meet the requirement. For the proposed SR NED based spectrum sensing scheme, a specific signal-independent SR noise is added to the received signal so that the probability distribution of the detection statistics is modified to match the applied nonlinear suboptimal detector better. The performance of the applied detector can then be significantly improved according to the basic principle of SR NED. Under the constraint of the false alarm probability, the method to find the optimal SR noise is provided for both single node sensing and multiple nodes cooperative sensing by maximizing the probability of detection. For single node sensing, the popular energy detector is considered. For cooperative sensing, both maximal ratio combination (MRC) and equal gain combination (EGC) based energy detectors are investigated. Theoretical analysis and simulations results show that the proposed SR NED based spectrum sensing schemes can achieve much better performance than that of the applied original detectors.

For multiple-input multiple-output (MIMO) wireless communication system with minimum mean square error (MMSE) detection, power allocation between pilot and data symbols is investigated under MMSE channel estimation in this paper. We first propose a novel soft-output MMSE MIMO detector by taking the channel estimation error into consideration. Then, by random matrix theorem, we propose an effective power allocation scheme between pilot and data symbols to maximize the average lower bound of minimum post-processing signal-to-interference and noise ratio (SINR) for the MIMO system, which has equal number of transmitter and receiver antennas. Simulation results validate our power allocation scheme.

Power allocation between pilot and data symbols is investigated for multiple-input and multiple-output (MIMO) spatial multiplexing (SM) system with zero-forcing receiver and minimum mean square error (MMSE) channel estimation under flat block-fading channel. Based on a modified zero-forcing receiver, which takes channel estimation error into account, a new power allocation scheme to maximize the average post-processing signal-to-noise-ratio (SNR) is proposed. This scheme only requires the computation of scalars and does not need any form of channel coefficients feedback. Simulation results show that the proposed scheme outperforms simple equal power allocation with an improvement of around 3 dB and 2 dB for QPSK and 16-QAM modulations when FER = 10-2, respectively.

As channel estimation errors are not taken into account by existing soft-output minimum mean square error (MMSE) multiple-input multiple-output (MIMO) detector, its performance can therefore be degraded under imperfect channel estimation. In this letter, we propose a novel soft-output MMSE MIMO detector under maximum likelihood (ML) channel estimation for a MIMO system with spatially correlated receiver and transmitter antennas. Our proposed detector takes both channel estimation errors and spatial correlation of antennas into account when constructing MMSE filter and computing log-likelihood ratio (LLR) of each coded bit. Simulation results show the proposed novel detector outperforms existing soft-output MMSE MIMO detector with considerable improvement.

Multiple-input multiple-output (MIMO) bit-interleaved coded modulation (BICM) has been proposed to apply in fast-fading environment. In this paper, we investigated the link-level capacity (LLC) and bit-error-rate (BER) performance of MIMO BICM system with soft-output MMSE soft interference cancellation (SIC) detection. We compared two SIC schemes in this paper. The first one is ordering successive SIC, in which the transmitted symbols are detected sequentially with post-detection signal-to-noise ratio (SNR) based ordering and interference cancellation error compensation. In the second scheme, we proposed to perform parallel interference cancellation (PIC) and detect the transmitted symbols simultaneously. We compared the LLC and BER performance of different detection schemes through extensive simulation.

In practical wireless multiple-input multiple-output (MIMO) communications system, the channel estimation must be imperfect. Unfortunately, the channel estimation errors have not been taken into account by existing soft-output MIMO minimum mean square error (MMSE) vertical Bell lab space time (V- BLAST) detectors when calculating the log-likelihood ratio of each coded bit, i.e., the soft information. As a result, the system performance can be significantly degraded. In this paper, we propose a novel soft-output MIMO MMSE V-BLAST detector based on random vector theorem, which takes the channel estimation error into account in the computation of the MMSE filter and LLR of each coded bit. Furthermore, a simplified implementation of the proposed detector is given so that the variance of channel fading coefficient is not needed. When compared with existing MIMO MMSE V-BLAST detectors, our simulation results show that the proposed novel detector can obtain significant performance gain at the cost of negligible increase of complexity.

For any practical wireless multiple-input multiple-output (MIMO) communication systems, the channel estimation can never be perfect, i.e., it is always imperfect. Unfortunately, the channel estimation errors have not been taken into account by existing soft-output two-stage parallel minimum mean square error (MMSE) detector when calculating the log-likelihood of each coded bit, i.e., the soft information. As a result, the system performance can be drastically degraded. In this paper, we propose a novel soft-output two-stage MMSE MIMO detector based on random vector theorem, which takes the channel estimation error into account in the computation of the MMSE filter and LLR of each coded bit. When compared with existing two-stage MMSE MIMO detector, our simulation results show that the proposed novel detector can remarkably reduce the residual error floor and obtain significant performance gain at the cost of negligible increase of complexity. Furthermore, it is observed from our simulation results that the proposed detector is not sensitive to the inaccuracy of variance of the channel estimation error. Therefore, it can be a promising candidate to be applied in practical systems.

There are two important constraints for cooperative spectrum sensing in cognitive radio (CR) context. Firstly, as the CR system can only tolerate low transmitting overhead, the local sensing data must be compressed before transmitting. Secondly, many sophisticated data fusion techniques cannot be used in CR system because of the lack of the signal's prior knowledge. In this paper, we proposed a novel cooperative spectrum sensing scheme which adapts to different overhead tolerance and does not need any prior knowledge. This scheme consists of two main parts: the quantization schemes and the data fusion rule. Two different quantization schemes, according to whether or not the distribution functions of test statistic are known, are proposed. Correspondingly, the optimal data fusion rule of multi-bits decisions is derived. Furthermore, to make the optimal fusion rule more practical, an iterative scheme, which does not need any prior knowledge of the signal, is proposed to estimate the likelihood ratio of local decisions. Simulation results show that our scheme achieves better performance than the schemes with "OR" and "AND" combinations. Furthermore, it is also shown that the proposed scheme could achieve the theoretically optimal performance by only two or three bits quantization.

In reality, this is no such thing as perfect channel estimation. But unfortunately, channel estimation errors have never been taken into account by conventional soft-output minimum mean square error (MMSE) multiple-input multiple-output (MIMO) detector when calculating the log-likelihood ratio (LLR) for each coded bit, i.e., the soft information. As a result, its system performance can be significantly degraded. In this paper, we propose a novel soft-output MMSE MIMO detector by the random vector theorem, which takes channel estimation errors into account in the computation of the MMSE filter and LLR of each coded bit. When compared with conventional soft-output MMSE MIMO detector, our simulation results show that the proposed novel detector can remarkably reduce the residual error floor due to the channel estimation errors, while achieving significant performance gain at the cost of negligible increase of complexity. Furthermore, it is observed from our simulation results that the proposed detector is not sensitive to the inaccuracy of channel estimation error statistics. Therefore, it can be a promising candidate to be applied in practical systems.

A reduced-complexity minimum mean square error (MMSE) soft interference cancellation (SIC) based multiple-input multiple-output (MIMO) turbo receiver is proposed. In this new proposal, the computational complexity intensive part of the MMSE SIC detector is ignored if the reliability of the a priori information is larger than a specific threshold. As the co-antenna interference (CAI) of MIMO system can be cancelled out with very high probability at higher iteration numbers, the computational complexity can be effectively reduced. Compared with those existing schemes which simply replace the MMSE filter with match filter (MF) at iteration stages, this new proposal takes the reliability of the a priori information into account and thus can reduce the computation complexity at the cost of negligible performance loss. Meanwhile, it is very flexible for this proposal to make a tradeoff between the performance and the computational complexity through changing the threshold of reliability. Simulation results confirm the advantages of this scheme.

While most current researches on opportunistic beamforming assume that users are uniformly distributed around BS, no attention has been paid to address more pratical scenarios where spatial user density is heterogeneous. In this paper, we propose a novel opportunistic beamforming scheme to better exploit the multiuser diversity in case of heterogeneous user density. The key idea is to grant the highest service priority to users in the area with highest spatial user density. It is implemented by an improved selection of the weight vector at each time slot without explicit knowledge of the spatial user density. To address the fairness issue, the joint area-user proportional fair scheduling (JAUPFS) is proposed, which simultaneously guarantees the fairness between areas with different spatial user densities and different users in the same area. Simulations are conducted to demonstrate that our proposed scheme has a better rate performance than that of the conventional opportunistic beamforming.

In opportunistic beamforming, the throughput can be improved by broadcasting multiple weight vectors in one time slot and selecting the best one. However, broadcasting too many weight vectors will consume the time for data transmission and thus bring down the throughput. In this paper, we investigate two multiple weight vector(MWV) opportunistic beamforming schemes tailored for fast fading and slow fading scenarios respectively and we derive tight upper bounds of the data rates for both schemes. To maximize the upper bounds, we obtain the optimal numbers of weight vectors to be broadcast. Simulation results demonstrate the validity of our theoretical analysis. Based on these results, we obtain some basic guidelines for MWV design problem. For the MWV scheme of fast Rayleigh fading scenario, we claim that (1) the faster the fading is, the less weight vectors are desired; (2) the more users there are, the less weight vectors are desired. For the MWV scheme of slow Rayleigh fading scenario, we have the conclusion that a small number of weight vectors are good enough to achieve a desirable performance.

In cognitive radio (CR) contexts, the available spectrum holes between the CR transmitter and receiver may be dynamically varying. Therefore, an important issue for CR application is the coordination of available spectrum at the transmitter and receiver before and during the process of information transmission. In this paper, we address this problem by proposing a signaling mechanism. Our scheme includes both the initial link establishment algorithm and dynamic link maintenance mechanism. We also propose the interleaved OFDM-based transform domain communication system as a promising candidate for the initial access signaling transmission by comparing with currently available modulation schemes. Simulation results of the proposed access link establishment mechanism are also presented to evaluate its feasibility

Reliable detection of available spectrum is the foundation of cognitive radio technology. To improve the detection probability under sustainable false alarm rate, a distributed spectrum sensing scheme has been proposed. In this paper, we propose a new decision combination scheme, in which the credibility of local spectrum sensing is taken into account in the final decision at central access point and Dempster-Shafer's evidence theory is adopted to combine different sensing decisions from each cognitive user. Simulation results show that significant improvement in detection probability as well as reduction in false alarm rate is achieved by our proposal

The orthogonal frequency division multiplexing (OFDM) systems that exploit Alamouti transmit diversity technique can be divided into two categories, i.e. Alamouti space-time block-coded OFDM (STBC-OFDM) and space-frequency block-coded OFDM (SFBC-OFDM). In time-varying multipath Rayleigh fading channels, these systems suffer from both time and frequency selectivity of channels. Recently, several detection schemes have been proposed to improve the bit error rate (BER) performance of these systems. In this paper, the achievable average capacities of different detectors are investigated. Based on the probability density function (pdf) of the effective instantaneous signal-to-noise ratio (SNR) obtained by each detector, the analytical expressions of achievable average capacities for those systems with different detectors are derived. Numerical results are also presented to give some insights

Traditional irregular low-density parity-check (IR-LDPC) codes are constructed based on the irregular bipartite graphs with carefully chosen degree patterns on both sides and computer searching is usually applied, which leads complexity to be relatively high. In this paper, we introduce the concept of V-matrix which is a matrix composed of vectors and propose a new approach to construct the IR-LDPC codes. By introducing some special operations, we can obtain the desired sparse parity-check matrix for IR-LDPC codes design. Because computer searching is avoided, the complexity is efficiently reduced. Computer simulation results show that our approach can achieve good tradeoff between complexity and performance