Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless communication and radar systems, enabling high-resolution sensing and high-throughput communication with shared spectrum and hardware. However, achieving a fine radar resolution often requires high-rate analog-to-digital converters (ADCs) and substantial storage, making it both expensive and impractical for many commercial applications. To address these challenges, this paper proposes an orthogonal time frequency space (OTFS)-based ISAC architecture that operates at reduced ADC sampling rates, yet preserves accurate radar estimation and supports simultaneous communication. The proposed architecture introduces pilot symbols directly in the delay-Doppler (DD) domain to leverage the transformation mapping between the DD and time-frequency (TF) domains to keep selected subcarriers active while others are inactive, allowing the radar receiver to exploit under-sampling aliasing and recover the original DD signal at much lower sampling rates. To further enhance the radar accuracy, we develop an iterative interference estimation and cancellation algorithm that mitigates data symbol interference. We propose a code-based spreading technique that distributes data across the DD domain to preserve the maximum unambiguous radar sensing range. For communication, we implement a complete transceiver pipeline optimized for reduced sampling rate system, including synchronization, channel estimation, and iterative data detection. Experimental results from a software-defined radio (SDR)-based testbed confirm that our method substantially lowers the required sampling rate without sacrificing radar sensing performance and ensures reliable communication.

A key challenge in integrated sensing and communications (ISAC) is the synthesis of waveforms that can modulate communication messages and achieve good sensing performance simultaneously. In ISAC systems, standard communication waveforms can be adapted for sensing, as the sensing receiver (co-located with the transmitter) has knowledge of the communication message and consequently the waveform. However, the randomness of communications may result in waveforms that have high sidelobes masking weak targets. Thus, it is desirable to refine communication waveforms to improve the sensing performance by reducing the integrated sidelobe levels (ISL). This is similar to the peak-to-average power ratio (PAPR) mitigation in orthogonal frequency division multiplexing (OFDM), in which the OFDM-modulated waveform needs to be refined to reduce the PAPR. In this paper, inspired by PAPR reduction algorithms in OFDM, we employ trellis shaping in OFDM-based ISAC systems to refine waveforms for specific sensing metrics using convolutional codes and Viterbi decoding. In such a scheme, the communication data is encoded and then mapped to the signaling constellation in different subcarriers, such that the time-domain sidelobes are reduced. An interesting observation is that sidelobe reduction in OFDM-based ISAC is dual to PAPR reduction in OFDM, thereby sharing a similar signaling structure. Numerical simulations and hardware software defined radio USRP experiments are carried out to demonstrate the effectiveness of the proposed trellis shaping approach.

Integrated sensing and communication (ISAC) promises high spectral and power efficiencies by sharing waveforms, spectrum, and hardware across sensing and data links. Yet commercial cellular networks struggle to deliver fine angular, range, and Doppler resolution due to limited aperture, bandwidth, and coherent observation time. In this paper, we propose a space-time-frequency synthetic ISAC architecture that fuses observations from distributed transmitters and receivers across time intervals and frequency bands. We develop a unified signal model for multistatic and monostatic configurations, derive Cramer-Rao lower bounds (CRLBs) for the estimations of position and velocity. The analysis shows how spatial diversity, multiband operation, and observation scheduling impact the Fisher information. We also compare the estimation performance between a concentrated maximum likelihood estimator (MLE) and a two stage information fusion (TSIF) method that first estimates per-path delay and radial speed and then fuses them by solving a weighted nonlinear least-squares problem via the Gauss-Newton algorithm. Numerical results show that MLE approaches the CRLB in the high signal-to-noise ratio (SNR) regime, while the two stage method remains competitive at moderate to high SNR but degrades at low SNR. A central finding is that fully synthesized network processing is essential, as estimations by individual base stations (BSs) followed by fusion are consistently inferior and unstable at low SNR. This framework offers a practical guidance for upgrading existing communication infrastructure into dense sensing networks.

The technology of integrated sensing and communications (ISAC) is among the six pillars of 6G wireless networks. Practical ISAC systems face the challenge of limited sampling rate of analog-digital-converter (ADC), particularly when the communications share the large bandwidth used by the radar sensing. Motivated by the popular frequency modulation continuous wave (FMCW) radar, it is proposed in this paper that a Shannon bandwidth matching the ADC sampling rate is used for the modulations of communication messages and sensing information (e.g., the range of target), while all available Fourier bandwidth assures the sensing performance. This principle is implemented using chirping-based orthogonal frequency division multiplexing (OFDM) for ISAC. The validity of the proposed principle and scheme is demonstrated using numerical simulations for monostatic sensing.

Millimeter-wave (mmWave) technology represents a promising avenue in integrated sensing and communication (ISAC), leveraging wide bandwidth to accommodate growing demands for high data-rate communication and high-resolution radar sensing. However, in non-line-of-sight (NLOS) scenarios, mmWave signals suffer from severe attenuation, and integrating precise radar sensing into bandwidth-limited communication systems remains an open problem. To address this, we propose an NLOS-ISAC system that jointly provides accurate target positioning and robust communication. Our approach synthesizes many narrowband signals into a virtual wideband radar via stepped-frequency techniques, eliminating the need for additional hardware. By fusing time-of-flight (ToF) measurements of multipath reflections with environmental maps, the system achieves submeter positioning accuracy for NLOS targets. Building on these position estimates, we then employ position-aware beamforming to significantly enhance the NLOS communication throughput. Extensive experimental results demonstrate that the proposed NLOS-ISAC system reliably achieves high-accuracy localization while improving data rates in challenging NLOS environments.

This work explores the integration of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Delay-Doppler Division Multiplexing (ODDM) within an Integrated Sensing and Communication (ISAC) framework. The proposed system leverages ODDM for high-mobility scenarios and NOMA for efficient resource utilization, thereby enabling enhanced trade-offs between communication and sensing. To achieve a balance between communication and sensing performance, an optimization problem is formulated to maximize the weighted sum of a sensing performance metric and communication throughput by jointly optimizing user grouping, power allocation, and beamforming, which are inherently coupled. To solve this problem efficiently, it is decomposed into three subproblems—user grouping, power allocation, and beamforming—which are then addressed iteratively to improve overall system performance. Simulation results validate the efficacy of the proposed framework under various mobility conditions, demonstrating improved sum-rate and sensing accuracy compared to Orthogonal Multiple Access (OMA) systems. This study offers valuable insights into advanced ISAC architectures, which are critically important for future 6G networks.

Interference mitigation is one of the key challenges in wireless communication networks. Traditional approaches are based on the MAC layer scheduling or multiuser detection in the physical layer, in which signals are separated using either orthogonal channels or distinct waveforms. In this paper, motivated by the recently emerged integrated sensing and communications (ISAC), an interference mitigation scheme, based on the distinct Doppler shifts of legitimate signal and interference, is proposed, in which the physical features of legitimate transmitter are obtained from the capability of sensing in ISAC. An alternative motivation is the clutter mitigation in radar systems, which excludes clutters reflected from undesired objects in the Doppler domain. Different interference mitigation schemes are discussed for modulations in the time, frequency and delay-Doppler domains, respectively. Numerical simulations are carried out to demonstrate the validity of the proposed schemes.

In integrated sensing and communication (ISAC) networks, the communication capability enables the collaborations for sensing among different ISAC nodes, thus improving the diversity of measurements and thus the performance of sensing. However, the inherent time and frequency synchronization errors prevent a clear image of sensed targets due to the blurriness incurred by the local time/frequency errors. The challenge can be mitigated using autofocus that exploits the target position common to all ISAC nodes for a calibration. In the third part of this paper, the autofocus is accomplished by the methodology of robot rendezvous, where each position estimation is considered as a robot and each ISAC node adjusts its local timing to drive the positions for a rendezvous leading to a joint estimation and time synchronization. The proposed algorithm is demonstrated using numerical simulations.

Integrated sensing and communications (ISAC) is a promising feature in 6 G networks. It is envisioned to enhance spectral efficiency and provide sensing and communication services that meet the stringent requirements of future applications. However, it also poses new security and privacy concerns by giving malicious attackers access to new information about the network. In this work, we focus on the sensing privacy of a monostatic ISAC system by investigating the capability of a sensing eavesdropper (EVE) with an unknown location, acting as a passive bistatic radar (PBR) to gain access to user location information. We then propose a joint transmit and artificial noise (AN) beamforming optimization problem to degrade EVE's performance. Finally, we propose an iterative algorithm to solve the proposed optimization problem and evaluate its performance.

Integrated Sensing and Communication (ISAC) is emerging as a promising technique for future communication and radar systems. A high-resolution estimation is essential for radar applications, necessitating large bandwidth. However, a large bandwidth requires dedicated Analog-to-Digital Converters (ADCs) with high sampling rates and substantial storage capacities to fully capture waveform signals, making it prohibitively expensive and impractical for many commercial systems. Therefore, it is crucial to develop methods for radar estimation with reduced sampling rates. In this paper, we propose a method to achieve accurate radar estimations for Orthogonal Time Frequency Space (OTFS)-based ISAC systems with reduced sampling rates. Specifically, we design pilot symbols in the delay-Doppler domain to ensure that the pilots in the time-frequency (TF) domain occupy specific subcarriers, leaving the remaining ones inactive. By leveraging the aliasing effect caused by undersampling, the receiver captures all aliased active subcarrier information with a reduced ADC sampling rate, enabling the restoration of the original delay-Doppler signal. Finally, we introduce an iterative estimation and interference cancellation algorithm to mitigate the interference from data symbols and provide accurate estimation results. Extensive experiments demonstrate that our method effectively reduces the sampling rate without compromising the ISAC performance.

Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising waveform for next-generation wireless communications. OTFS demonstrates robust non-fading properties even under doubly-dispersive conditions, making it a preferred modulation scheme in complex scenarios involving high mobility. Despite its advantages, developing a low-complexity multiaccess OTFS protocol for environments with a large number of user terminals (UTs) and high mobility remains challenging due to severe multiuser interference (MUI) and rapidly changing channel states. In this paper, we propose a low-complexity algorithm to find a suboptimal solution for the joint resource block and power allocation problem in multiple access OTFS systems. Specifically, we decompose the resource block allocation and power allocation into two sub-problems. First, we perform the resource block allocation algorithm by considering the channel conditions. Then, we employ a two-stage water-filling algorithm to achieve a suboptimal power allocation. Numerical results indicate that our proposed resource allocation scheme achieves higher sum-rate than existing schemes in both 8-user and 16-user systems while reducing the complexity from exponential to linear in terms of the number of users. This makes our approach practical even in high mobility and larger user scenarios.

Integrated Sensing and Communications (ISAC) are expected to be one of the six pillars of 6G wireless networks. It integrates both functions in the same waveform, thus substantially improving the system efficiency of bandwidth and power. A significant trade-off exists between communications and sensing in ISAC, in terms of the sensitivity of waveform to the environment (or equivalently, of electromagnetic (EM) field to scatterers): on the one hand, if the EM field is sensitive to the scatterers, the field measurement at the sensing receiver will be more distinct with respect to different scenarios of scatterers, thus benefiting the purpose of sensing; on the other hand, more sensitivity to the environment brings the transmit waveforms (as communication codewords) more confusions, thus impairing the performance of communications. In this paper, the sensitivities of EM field to scatterers and transmit waveforms are analyzed for communications and sensing, respectively. The corresponding performance trade-off is quantified for evaluating the performance of ISAC.

The technique of Integrated Sensing and Communications (ISAC) is expected to be one of the six pillars of 6G wireless communication systems. It integrates communications and sensing in the same waveform, thus substantially reducing the consumption of bandwidth. When multiple antennas are used for both ISAC transceiver and communication receiver, new techniques of waveform synthesis are needed for the multiple-input-multiple-output (MIMO) ISAC. In this paper, a generalized ambiguity function (AF) is proposed for the MIMO ISAC, based on which the trade-off between communications and sensing in MIMO ISAC is quantified. Numerical simulations are used to demonstrate the validity of the proposed generalized AF and quantify the fundamental trade-off in MIMO ISAC.

Orthogonal Time Frequency Space (OTFS) modulation emerges as a promising waveform for next generation wireless communications. The efficacy of OTFS, in both communication and sensing realms, is critically dependent on the design of its frame structure. This study delves into the design and optimization of a pilot-symbol-aided OTFS frame, with an emphasis on enhancing spectrum efficiency, minimizing the Peak-to-Average Power Ratio (PAPR), and reducing bit error rate (BER). Specifically, we first analyze the impact of specific channel characteristics on BER. Subsequently, we engage in a detailed exploration of the interplay between frame parameters and performance metrics, namely the spectrum efficiency, PAPR, and BER. We eventually propose an optimization framework for embedded-pilot OTFS frames, aimed at attaining optimal performance across these metrics. Through both simulations and experiments, we demonstrate that the optimized OTFS frame architecture offers significant improvements in BER and our optimization framework provides profound insights into determining the optimal frame parameters tailored to specific use cases, with an emphasis on varying priorities.

Joint communications and sensing (JCS) provides an effective approach to enhance the spectral efficiency of wireless systems. When integrating these historically independent functions in the same waveform, both communication and sensing may suffer from performance degradation, thus resulting in a trade-off between their performances. A fundamental question is how to obtain bounds for the communication- sensing trade-off in JCS. In this paper, a geometric approach is adopted, namely evaluating the volume of a feasible waveform set given the tolerable performance degradation of sensing and then bounding the number of possible communication codewords using the sphere packing methodology. In particular, mathematical tools in high-dimensional geometry are leveraged for the volume calculation in the first of this paper. Applications for concrete sensing performance metrics will be left to the second part of the paper.

The technology of joint communications and sensing (JCS) is expected to prevail in 6G wireless networks. There exists a performance tradeoff between the functions of communications and sensing in JCS. One effective approach to analyze the tradeoff in JCS is to consider the level sets of a given performance metric of sensing as the signaling space of communication codewords. Then, performance bounds can be obtained for communications using the approach of sphere packing. The principle for generic sensing performance metric has been studied in the first part of this paper. In the second part of this paper, the concrete cases of sensing performance metrics, namely the signal-to-noise ratio (SNR) and integrated sidelobe level (ISL), are studied. The problems are turned into the volume evaluation for the intersection of a (elliptic) sphere (the quadratic approximation of the level set) and a hyperplane (the constraint on the total transmit power). They are solved by using the theory of Fourier-transform-based volume evaluation of convex sets. It is found that the optimal waveform is not unique, thus providing free lunch (although not plenty of) for communications. Another finding is that the communication data rate increases logarithmically with respect to the sensing performance metric degradation.

In joint communications and sensing (JCS), which is expected to be a distinguishing feature in 6G wireless networks, the major challenge is how to integrate the two distinct functions in the same waveform. In this paper, the waveform synthesis in JCS is studied$W$in the wideband regime, since a large bandwidth can simplify the analysis and is justified by the employment of millimeter wave or higher frequency band. The wideband analysis of sensing is a counterpart of the existing studies on wideband communications. It is proposed that the phase of the signal frequency spectrum is used for modulating communication messages while the magnitude spectrum is optimized for the sensing performance, whose theoretical foundation stems from the wideband analysis. The proposed wideband JCS scheme is demonstrated using numerical simulations.

This paper tries to raise awareness of security and privacy issues associated with Integrated Sensing and Communications (ISAC). In the context of ISAC, sensing (or radar) and communication functionalities can work synergically, i.e., the two functionalities can benefit each other, which is both exciting and worrisome. Our concern is that ISAC exposes increased vulnerability and faces threats we never experienced before, while it offers tremendous opportunity. In the ISAC system not only can an attacker wirelessly sniff data transmitted by legitimate users, but can also locate them passively or actively via illuminating. For instance, sensing-assisted eavesdropping in ISAC is much easier than traditional eavesdropping. With the availability of precise sensing at sub-centimeter accuracy, an attacker gains much more information than just a MAC address. The stolen information can include location, speed and ambient condition, and may be in the format of images. Consequently, attacks can be more targeted and effective. In addition, as sensing becomes ubiquitous and collaborative sensing becomes easier, it is harder to preserve privacy and to manage the trustworthiness of a large number of participants. We introduce a secure ISAC (S-ISAC) framework and propose a number of solutions. In contrast to some information-theoretic works on ISAC security, we emphasize practical countermeasures from an ISAC-network perspective. We also propose a number of research topics that need to be addressed before the ISAC concept is fully adopted by the industry.

We investigate a vision-aided integrated sensing and communications (ISAC) system comprising a transmitter, a receiver and a vision sensor (such as a camera) co-located with the receiver. The vision-aided ISAC system uses the vision sensor to sense the environment and share the vision data with the receiver. The receiver decodes the transmitted message using the received signal and the vision data. Even though this vision data may not completely determine the channel impulse response, some information about the environment, such as whether the transmitter is visible from the receiver, could be potentially useful for decoding. The objective of this paper is to understand the value of such information, termed channel state knowledge, using an information-theoretic formalism. We examine three scenarios in which the vision sensor provides different amounts of channel state knowledge to the receiver: perfect, imperfect, and none. Further, we analyze the mutual information for the vision-aided ISAC system using joint and sequential processing approaches and demonstrate that the system with the joint processing of vision data and communication signals has higher mutual information. This analysis provides crucial insights into the performance limits of vision-aided ISAC systems.

In the first part of this paper, an interference elimination scheme is proposed for communication networks, based on the signal propagation time offset and pulse compression. In the second part of this paper, the proposed scheme is analyzed and compared with existing schemes. First, the condition of interference isolation and elimination is derived, based on which the bandwidth requirement for the proposed scheme is obtained. Then, the proposed scheme is compared with spread spectrum based interference mitigation using distinct waveforms. It is found that they indeed have similar signaling structures and rely on the sequence autocorrelation and cross-correlation, respectively. Other interference management schemes, such as orthogonal transmission and coding for combating interference, are compared with the proposed scheme. Finally, numerical results and conclusions are given.

In joint communications and sensing (JCS), which is expected to be a prominent feature of 6G wireless networks, the major challenge is how to integrate both functions of communications and sensing in the same waveform. In this paper, a watermarking-based scheme is proposed, where the communication message is considered as watermark, hidden in the host waveform that is dedicatedly designed for radar sensing. Moreover, the watermarking is based on dithered quantization, which is optimized in order to minimize the distortion to the original host waveform. The mitigation of interference in JCS networks is also discussed. Numerical simulations are carried out to demonstrate the proposed watermarking JCS scheme.

In various cyber physical systems (CPSs), communications and sensing are conducted simultaneously. Therefore, the mechanism of joint communications and sensing (JCS) is envisioned to integrate both functions in the same waveform, frequency band and hardware. It is expected to be one of the major features of 6G wireless communication networks. A major challenge to the design and analysis of JCS is a unified framework that incorporates the distinct functions of communications and sensing. In the first par of this paper, the framework of broadcast channel that has been intensively studied in data communications and information theory is adopted for JCS, in which communication and sensing signals are broadcast to the concrete communication users and virtual sensing users. Such a broadcast channel framework benefits the applications of existing multiplexing schemes, such as dirty paper coding (DPC) or frequency division multiplexing (FDM). Based on the framework, the feasible performance region bound is derived, based on the broadcast-multiaccess duality. The design of dedicated sensing signal is studied for the scenarios of communication-first (or sensing-first) priority, based on the ambiguity function (AF) of radar sensing. The proposed scheme is numerically demonstrated using typical short-range communication and sensing setups. The scheme based on superposition coding will be discussed in the second part of this paper.

The technology of joint communications and sensing (JCS) integrates both functions in the same waveform and thus the same frequency band. It is expected to be a distinguishing feature in 6G wireless networks. A major challenge to JCS is how to seamlessly integrate the two historically distinct functions of communications and radar sensing. In the second part of this paper, a framework of superposition coding, motivated by the similarity to broadcast channels, is proposed for the functional multiplexing in JCS, which is motivated by the studies on broadcast channels in data communications. In this framework, communications and sensing are considered as genuine and virtual users, respectively. Sensing is considered as the bottom user in the layered structure of superposition coding; thus a sensing waveform is generated according to a certain criterion of sensing, which plays the role of cloud in superposition coding. Then, the communication message is superimposed on top of the cloud. Different superposition schemes are proposed, each corresponding to one type of mathematical operation on vectors in linear spaces. Moreover, the waveform diversity recently proposed in the radar community, which prepares a set of waveforms for handling the variance of environment, is taken into account. The cases of sensing waveform known/unknown to the communication receiver are discussed. The performance of the proposed JCS schemes is demonstrated using numerical simulations.

In joint communications and sensing (JCS), the two different functions need to be integrated in the same waveform. For the purpose of sensing, the waveform is expected to filter out clutters and enhance the reflected signal from the target. Therefore, the transmitted waveforms can be considered as a set of filters, which reshape the signal in the dimensions of space, time and frequency. To accomplish the data communications, information needs to be modulated into the set of filters, as codewords located on the filter manifold. In the first part of this paper, the spatial-time-frequency filtering is studied for the performance of sensing, thus disclosing the signal structure into which the communication information can be embedded, in a superposition coding manner. The detailed coding scheme for information embedding will be studied in the second part of this paper.

In joint communications and sensing (JCS), the same waveform delivers data to communication receiver in the forward propagation of electromagnetic (EM) wave and fetches information of significant reflectors (radar targets) in the backward propagation. For both communications and sensing, there are pressing needs to identify the significant reflectors, for both communication channel estimation and radar target sensing, which share common information of the reflector positions and velocities. In this paper, the JCS transceiver and communication receiver are considered as a bi-static radar, which can exchange information via the communication links (data channel from the JCS transceiver to the communication receiver and reverse control channel from the the communication receiver to the JCS transceiver). Due to the common information, the target sensing in JCS is carried out in a turbo manner, through the mechanism of message passing and within the framework of Kalman filtering for the dynamics of reflectors. The validity of the proposed turbo sensing scheme is demonstrated by numerical simulations.

Integrated sensing and communications (ISAC) is a promising technology to integrate both functions in the same waveform, and is expected to be a feature in 6G wireless communication networks. One effective approach to ISAC is leveraging existing communication signals (such as orthogonal frequency division multiplexing (OFDM) signals) to sense the environment. Since there are many base stations in densely deployed cellular networks, they can form a massive sensing network, in which some transmit for illumination while others receive and collect data for imaging the illuminated area. In this paper, such imaging algorithms are discussed. Further, the critical challenge of time synchronization errors is addressed by using the technique of autofocus. The performance conflict and corresponding trade-off between communications and sensing in ISAC are discussed qualitatively.

Communications and radar are expected to be integrated in the same waveform and hardware platform. Mobile joint communications-radar (JCR) networks, e.g., being used in autonomous driving systems, are subject to the severe challenge of mutual interference. When frequency modulation continuous waveform (FMCW) signal is used, its inherent robustness is leveraged to combat the interference. Beyond this capability, various approaches combating the JCR interference are proposed, including the interference detection, elimination and diversity. Moreover, the interference can be leveraged to obtain extra sensing information of the environment. Simulations and experiments are carried out to demonstrate the proposed interference management approaches in JCR networks.

An increasing number of distributed energy resources (DERs), such as rooftop photovoltaic (PV), electric vehicles (EVs), and distributed energy storage, are being integrated into the distribution systems. The rise of DERs has come hand-in-hand with large amounts of data generated and explosive growth in data collection, communication, and control devices. In addition, a massive number of consumers are involved in the interaction with the power grid to provide flexibility. Electricity consumers, power networks, and communication networks are three main parts of the distribution systems, which are deeply coupled. In this sense, smart distribution systems can be essentially viewed as cyber–physical–social systems. So far, extensive works have been conducted on the intersection of cyber, physical, and social aspects in distribution systems. These works involve two or three of the cyber, physical, and social aspects. Having a better understanding of how the three aspects are coupled can help to better model, monitor, control, and operate future smart distribution systems. In this regard, this article provides a comprehensive review of the coupling relationships among the cyber, physical, and social aspects of distribution systems. Remarkably, several emerging topics that challenge future cyber–physical–social distribution systems, including applications of 5G communication, the impact of COVID-19, and data privacy issues, are discussed. This article also envisions several future research directions or challenges regarding cyber–physical–social distribution systems.

In joint communications and sensing (JCS), which is expected to be among the major features of 6G wireless networks, the functions of communications and sensing are accomplished by the same JCS transmitter, possibly sharing the same waveform and thus the same frequency band and transmit power. In JCS networks, scheduling in the MAC layer is needed to balance the tasks of delivering data packets (particularly when there are multiple hops) and the radar target sensing, given limited wireless resources. In this paper, a unified framework is proposed, by considering virtual queues, in which virtual information bits for sensing that characterize the uncertainties of the mobile radar targets are backlogged if sensing is not carried out, or are cleared if significant resource is dedicated to sensing. The entropy reduction by sensing and entropy accumulation due to random perturbations to the radar target motions are quantified using the framework of Kalman filtering. Then the back pressure scheduling is employed for both the real communication queues and virtual sensing queues. Both JCS schemes of simple time-division multiplexing (TDM) and more flexible coding-spreading multiplexing (CSM) are taken into account. Numerical simulations are carried out to demonstrate the performance of the proposed algorithms, with the baseline of round-robin scheduling.

In integrated sensing and communications (ISAC), both functions are tightly integrated using the same spectrum, waveform and hardware. Analogy is identified between ISAC and broadcast channel in communications, since ISAC transmitter broadcasts signals for both communication receivers and sensing, despite the functional overlap between communications and sensing. Although the communication signal can also be used for sensing, the intrinsic randomness of communications impairs the reliability of sensing. Therefore, a dedicated and deterministic sensing waveform is added, in order to improve the sensing performance. To remove the interference of the dedicated sensing waveform to communication receivers, dirty paper coding is employed, which exploits precoding for interference cancellation. Meanwhile, the trade-off between communications and sensing is identified. Numerical results are used to demonstrate the proposed ISAC scheme and the performance trade-off.

In joint communications and sensing (JCS), the same electromagnetic (EM) wave is used for both functions, in the forward (incident) and backward (scattered) channels, respectively. The communication channel is determined by scatterers (also as sensing targets), thus coupling the performances of communications and sensing. More scattering targets generate more degrees of freedom (DoFs) for communications, thus improving the channel capacity given sufficient antennas. Meanwhile, they also result in more sensing errors due to the increasing complexity of EM field. The complexity analysis of EM field, in terms of DoFs, is applied to analyze the trade-off between communications and sensing, as the complexity of scattering targets increases. Numerical results are calculated to demonstrate the performance trade-off between communications and sensing.

Joint communications and sensing integrate both functions in the same waveform, in order to improve the efficiency of spectrum access. Compared with existing studies on linearly superimposed communication and sensing waveforms, a nonlinear and inseparable integration of both functions can leverage the compatibility of communications and radar. In this paper, given a set of waveforms, the performances of communications and radar sensing are analyzed in the frequency spectrum domain, based on which the trade-off, characterized by the Pareto front, between the two functions is derived. Numerical simulations are carried out to evaluate the performances and characterize the trade-off. The results show that the conflict between communications and sensing is marginal, given the setup in the paper.

In modern cellular communication networks, higher density of base stations and higher frequency bands will be employed. If being reflected by targets in the environment, the scattered communication signal also brings back the information of the targets, in addition to the communication messages, to the receivers. In this paper, it is proposed to leverage the reflected communication signals to reconstruct the image of the illuminated region. Due to the analogy to traditional synthetic aperture radar (SAR), the principle of spotlight SAR, namely the tomography via Fourier transformation, is adopted with necessary improvements. Numerical simulations are carried out to demonstrate the proposed algorithms.

In joint communications and sensing (JCS), the two functions are integrated into the same waveform and hardware, which substantially reduces the consumptions of bandwidth and power. However, there exist conflicts of interests between communications and sensing, when they share the same resource. One major conflict is the uncertainty of waveform that is necessary to communications but harmful for sensing. In this paper, the conflict and trade-off in the waveform uncertainty are analyzed for orthogonal frequency division multiplexing (OFDM) waveforms and wide sense stationary (WSS) waveforms, in terms of data transmission rate of communications and integrated sidelobe level (ISL) for sensing. It is shown by numerical results that high-order quadrature amplitude modulation (QAM) results in higher ISL (thus lower capability of target identification), while a single-sided Gaussian-distributed power spectral density (PSD) in WSS waveforms achieves the optimal trade-off in the waveform uncertainty.

Knowledge distillation (KD) has been identified as an effective knowledge transfer approach. By learning from the outputs of a pre-trained, over-parameterized teacher network, a compact student network can be trained efficiently to achieve superior performance. Although KD has gained substantial successes, exposure to pre-trained models usually causes potential risks of intellectual property leaks. From a model stealing attacker’s perspective, one can easily mimic the model functionality via KD, resulting in huge financial loss. In this paper, we propose a novel adversarial training framework called semantic nasty teacher, which prevents the teacher model from being copied by the attacker. In specific, we disentangle the semantic relationship in the output logits when training the teacher model, which is the key to success in KD. Experiment results show that neural networks trained with our approach only sacrifices little performance while canceling out the probability of KD-based model stealing.

The technique of joint communications and sensing (JCS) integrates both functions in the same waveform, thus reusing the frequency spectrum, transmit power and hardware. The two functions need to be multiplexed, while they also benefit each other. In this paper, the function multiplexing of JCS is in the frequency domain, leveraging the orthogonal subcarriers in orthogonal frequency division multiplexing (OFDM). To exploit the mutual benefits of subcarriers dedicated to communications and sensing, the signals over the communication subcarriers are leveraged for the function of sensing. The signals over the dedicated sensing subcarriers are made adaptive to the communication signals, in order to optimize the integrated sidelobe levels (ISLs) of the overall waveform and improve the sensing performance. Since the signals over the dedicated sensing subcarriers are functions of the communication signals, due to the adaptive waveform synthesis, they provide redundancies for the communication signals and improve the reliability of data transmission. The proposed JCS scheme is demonstrated by numerical results.

Joint communication and sensing (JCS) is a promising technology that shares the same waveform for both the functions of communications and sensing, in order to improve the efficiencies of frequency spectrum and power. In this paper, the approach of reusing communication waveforms for sensing is studied. From the viewpoint of frequency domain, the communication signals provide bandwidth for radar ranging; from the viewpoint of time domain, the random information symbols in the communication signal offer timing information via the autocorrelation function. Both cases of single-carrier and multi-carrier communication signals are studied for embedding the sensing function. The trade-off between communication and sensing performances is studied.

This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available.

Interferometry is a powerful tool for estimating the incident angle of electromagnetic (EM) waves by calculating the correlation of received signals at different antennas. Motivated by very-long-baseline interfereometry (VLBI) in radio astronomy, an interferometry based sensing scheme is proposed as integrated sensing and communications (ISAC). It reuses the communication signal from base stations (BSs), similarly to passive radars, which improves the sensing precision and spectrum efficiency. Different from the almost-perfect synchronization in VLBI realized by atomic clocks, the synchronization in BSs of cellular commu-nication networks (usually based on GPS signals) could have significant errors. Therefore, algorithms for compensating for synchronization errors in both time and frequency are proposed. Numerical simulations demonstrate that the proposed algorithms can substantially alleviate the synchronization errors.

Joint communications ad radar (JCR) is a technology to leverage the same waveform for simultaneous data transmission and radar sensing, which is expected to be efficient in bandwidth and power. A theoretical framework is proposed to study JCR, based on the electromagnetic field analysis. Given a simple model of communications and radar sensing, the electromagnetic field is described in terms of forward and scattering Green functions. For radar sensing, the imaging error is derived based on the Fisher operator and the eigen-decomposition of the integral transformations with Green function kernels. For data transmission, the channel capacity is analyzed based on a similar eigen-decomposition. The trade-off between the imaging error of radar and the channel capacity of data transmission is obtained based on the theoretical analysis and numerical computations.

Communication is of key importance for the control of unmanned aerial vehicle (UAV) networks. From the viewpoint of statistical mechanics, it plays the role of bond and correlation in the ensemble of particles. A statistical mechanics model is set for UAV networks with limited communication ranges, including potentials and stochastic evolutions. Based on the framework, an equation describing the behavior of UAV network, in terms of canonical thermodynamics metrics, is derived using the technique of cluster expansion. The critical temperature, characterizing the phase transition (between unstable and stable), is derived in explicit expression. Numerical simulation shows that the statistical mechanics analysis provides an operable estimation on the stability condition of UAV networks, in terms of communication range. In particular, it is shown that the critical temperature (the power of random perturbation) follows a power law in terms of the communication range, given the model adopted in this paper.

Joint communications and radar (JCR), which use the same waveform for both functions, provide an efficient scheme of spectrum access and find various applications in practice such as autonomous driving. A convenient signaling framework is the orthogonal frequency-division multiplexing and multi-in-multi-out (OFDM-MIMO) structure, which corresponds to the spatial-frequency spectrum resulted from high-dimensional Fourier transform. The key challenge of JCR is how to resolve the interest conflict between communications and radar sensing, when they share the same waveform in an inseparable manner. The corresponding trade-off is formulated as constrained optimization problems for the cases of analog and digital beamformings. Numerical results show that the proposed schemes are effective in the spatial-frequency spectrum management and achieve good performance trade-offs between communications and radar sensing.

Ample evidence has confirmed the importance of information in security. While much research on security game has assumed the attackers' limited capabilities to obtain target information, few studies consider the possibility that the information can be acquired from a data broker, not to mention exploring the attackers' profit-seeking behaviors in the shrouded underground society. This paper studies the role of information in the security problem when the target information is sold by a data broker to multiple attackers. We formulate a novel multi-stage game model to characterize both the cooperative and competitive interactions of the data broker and attackers. The attackers' competition with correlated purchasing and attacking decisions is modeled as a two-stage stochastic model, and the bargaining process between the data broker and the attackers is analyzed in a Stackelberg game. The study contributes to the literature by exploring the behaviors of the attackers with labor specialization, and providing quantitative measures of information value from an economic perspective. The proposed frameworks characterize both the attackers' competitive equilibrium solutions and the data broker's pricing strategies under different market parameters. We also show how factors such as the quality of information, the heterogeneity in attackers' utilities, and their cooperative purchasing strategy would have an impact on the results.

The unmanned aerial vehicle (UAV) network offers unprecedented flexibility in the future wireless communication systems. In this paper, a novel cell coverage model is proposed from the perspective of practical system implementations. At first, several UAV millimeter wave communication experiments are implemented. Then experiment results identify the inevitable impact of UAV cell coverage due to the wind-caused attitude perturbation. Furthermore, an equivalent coverage model is derived based on the experimental observation. Finally, the simulations reveal the relationship between the cell coverage and wind perturbation.

Frequency-modulated continuous-wave (FMCW) is widely used in radar systems to measure range and speed due to its low cost, outstanding reliability and full automation. In this paper, a novel framework is proposed to enable the communication functionality of millimeter wave (mmWave) FMCW radar. The symbol is modulated by the frequency slope of chirp pulse, when matched filter based approach is used to demodulate the information symbol. Theoretical analysis is provided for the upper bound of the symbol error rate by introducing the Fresnel integral. Based on the measurements of our mmWave FMCW radar test bed, a detailed simulation is carried out to demonstrate the performance of the proposed framework.

Millimeter wave (mmWave) has found substantial applications in wireless communications and sensing systems. However, mmWave communications and sensing are studied and designed separately. In this paper, it is proposed to leverage the mmWave communication infrastructure in 5G cellular systems for the purpose of sensing, with marginal extra cost, due to its large number of antennas, small wavelength and expected deployment of cloud radio access. In this scheme, a certain region/object is illuminated by transmitting base stations, and its image is reconstructed by using the reflected/scattered signals intercepted by receiving base stations. The sensing algorithms are proposed in this paper, for both 2- dimensional and 3-dimensional sources, and then evaluated using theoretical analysis and numerical simulations. It is shown that the proposed scheme can reconstruct the image with reasonable precisions given typical scenarios of 5G systems.

Millimeter wave (mmWave) mobile network is the cornerstone for capacity enhancement and low latency transmission in the next generation of wireless communications. To compensate the blockage and intermittent connections, multiconnectivity can be taken as an essential component for a robust and reliable link. However, the complicated transfer mechanism for unsent and buffered data, due to handoff or blocked link in mmWave mobile network, will nullify the benefits of increasing link numbers. Taking advantage of properties of rateless erasure code, a fountain code based multi-connectivity mechanism is proposed to not only increase the link reliability, but also simplify the backhaul data offloading. As the problem arises in mmWave channels, the signal strength in a multi-link channel is measured with a 60GHz mmWave testbed. The proposed multi-connectivity system is verified with these measurements. The simulation demonstrates the efficiency and robustness of our system design.

Mobile communications in the millimeter wave band is expected to dramatically increase the network capacity due to the large bandwidth. However, the high propagation loss requires compensation from antenna gains to meet the link budget. Thus, beam-based cell coverage is a critical component in the next generation of wireless communication. The appropriate beam selection induces a unique challenge, when the mobile device is roaming within one cell. In this paper, we provide a thorough analysis of sensor-aided beam updating approach at the user equipment side, which is validated through experimental data. By decoupling the translation and rotation in various motions, we can further improve the tracking accuracy and reduce the mismatch by 50%. Finally, the overhead simulations are compared based on real sensor measurements given different typical scenarios. Experiment and simulation show that the proposed beam tracking mechanism could dramatically reduce the overhead of beam alignment.

Directional transmission is a prominent feature of millimeter wave communication systems, due to its near-optical propagation and the capability of beam forming. When the narrow beam provides high signal-to-noise ratio (SNR) due to the focus of power, it also implicitly hides bonus information for other purposes. One of the possible applications is the landscape detection by leveraging the reflection of beams: when a mobile user moves, the incident angle of beam may be changed; if it is non-line-of-sight (NLOS) beam, which can be determined due to the locations of base stations, the reflecting surface (building, constructions, trees, et al) can be estimated from the trajectory of the mobile user, the location of the base station, and the record of incident angles. This is in principle similar to the synthetic aperture radar (SAR). In this paper, the ideal case of infinitesimally narrow beam (as a straight line) is first studied. The estimation for the smooth curve of reflector is obtained. Then, the analysis is relaxed to the practical case of limited but nonzero beam width. The uncertainty in the direction is tamed by statistical modeling and filtering. Experiments in a 60GHz millimeter wave testbed are carried out to collect measurements and verify the proposed algorithms.

Self-organized networks (SONs) have been studied for many years, and have attracted many researchers due to their substantial applications. Although their performance in the lower frequency band networks (sub-6 GHz band) has been well studied, there are only sparse studies on SON in higher frequency bands, such as the millimeter wave (mmWave) band ranges between 28 GHz and 300 GHz. mmWave frequencies have attracted much attention recently because of their unique features, and being an important part of the next generation of wireless communications namely (5G). In this paper, we suggest a new routing protocol for mobile ad-hoc networks that use mmWave by utilizing its directional antennas, limited transmission range, and the random beamforming technique. The main goal of the proposed protocol is to increase the network throughput, reduce the delay, and mitigate the effect of the interference of concurrent communicating nodes. Mathematical analysis of the network per hop achievable throughput, expected interference, and average delay per hop has been carried out. Multiple realistic simulations scenarios have been conducted to confirm the analytical results and prove the advantages of the proposed protocol.

Millimeter wave (mmWave) provides an enormous spectrum for future broadband cellular communications. The corresponding challenging characteristics of radio propagation, and the use of highly directional transmission and the dense deployment, lead to more complex and critical resource allocation problems than in traditional cellular systems, where the channels are better tamed. In this paper, we study the joint power control and user association problem in heterogeneous networks (HetNets) by considering the dynamics of links as a Markov Decision Process (MDP). A reinforcement learning framework is proposed to study the problem. The large state/action space is handled by decomposing the large scale problem into multiple local problems, based on the topology of mmWave HetNet, which is motivated by the celebrated belief propagation (BP) algorithm in probabilistic graphical models. The decomposed problem is solved with a distributed message passing method and accelerated by the prior knowledge of the mmWave dynamics. Two categories of learning frameworks are proposed for time sensitive and power sensitive conditions. The real-world measurements in the 60GHz band are collected and used in the simulation of the proposed framework.

Millimeter wave (MMwave) in wireless communications has tremendous frequency bandwidth compared with traditional cellular and WLAN bands. As a promising technique of MMwave, multiple- input-multiple-output (MIMO) techniques, especially beamforming, could greatly improve the system capacity and compensate for the additional path loss in MMwave. However, a perfect beam alignment between the transmitter and receiver needs fine tuning and is vulnerable to different factors such as mobile device movement. In this paper, the widely available motion sensors in mobile devices are employed to mitigate the angle misalignment between the MMwave transmitter and receiver. A new beam alignment mechanism is proposed to reduce the system overhead, given the device attitude information. Experiment and simulation show that the proposed beam tracking mechanism could dramatically reduce the overhead with a minimum computational cost.

Millimeter wave is a promising technique in the next generation of cellular communications due to its large bandwidth and thus unprecedentedly high data transmission rate. In this paper, the blocking effect of millimeter wave is studied through a series of experiments. Then, the blocking of millimeter wave communications is applied to track the movement of the blocking object. Two object tracking schemes via the blocking effect, using two different criteria, are proposed. A walking trajectory model is proposed and analyzed to evaluate the performance of the proposed tracking algorithm analytically. The performance of the proposed schemes is tested by simulations on true trajectories based on GPS measurement.

A prominent feature of millimeter wave communications is the blockage effect: an object intruding the line-of-sight (LOS) of a communication link can block the millimeter wave signal. While the blockage incurs outage to the communications, it also brings information related to the blocking object. In this paper, both short-term and long- term trajectory estimations are studied for the object(s) blocking the millimeter wave communication link(s). For the short-term trajectory, experiment has revealed signal strength oscillations before and after the blockage, due to the reflection at the blocking object and the interference with LOS signal. It is shown that the short-term trajectory, as well as the speed, of the moving object can be estimated from the samples of oscillations. For the long-term trajectory, the tracking of multiple objects is carried out using the blockage events on multiple millimeter wave communication links. The trajectory estimation is converted to the problem of object recognition, which is solved by exploiting the geometric intuition. Numerical and experimental results show that the proposed short-term and long-term trajectory estimations achieve excellent performances.

In this paper, the impact of user mobility on the performance of random beamforming (RBF) is studied for possible practical scenarios of millimeter wave (mmWave) broadcast downlink systems. Both mathematical modeling and simulation scenarios' results were investigated to estimate the upper bound of the degradation in the overall system throughput that results from the mobility of users in a small cells systems. Different possible factors that can affect the throughput are taken into consideration, which include the user location, direction of movement, speed, and the beam angle. Simulation results show that for all the typical mobility cases (walking, running, biking, and riding a car), the degradation in performance resulting from user mobility is marginal, while the Base Station (BS) will be able to deliver at least one message after each beamforming training period with high probability exceeding 99.99% theoretically and more than 96% in the worst case simulations.

Motivation: Cell movement in the early phase of Caenorhabditis elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulatory networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images. Results: We present a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C.elegans. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, agent-based modeling that uses greedy algorithms. We tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the superficial left-right asymmetry. In the first case, the model results suggested that Cpaaa's intercalation is an active directional cell movement caused by the continuous effects from a longer distance (farther than the length of two adjacent cells), as opposed to a passive movement caused by neighbor cell movements. In the second case, a leader-follower mechanism well explained the collective cell movement pattern in the asymmetry rearrangement. These results showed that our approach to introduce deep reinforcement learning into agent-based modeling can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to explore the large datasets generated by live imaging. Availability and implementation: Source code is available at https://github.com/zwang84/drl4cellmovement. Supplementary information: Supplementary data are available at Bioinformatics online.

This paper proposes a distributed multiagent reinforcement learning framework for resource allocation problems in heterogeneous wireless networks (HetNet). The base station, pico transmitters and user equipments collaborate to improve the system throughput and energy efficiency by handling the inter-cell interference based on the local observable environment. Utilizing the sparsity and tree structure of connection graph of the network, the collaboration is carried out through a distributed calculation of the optimal action via the message passing method. The tradeoff between the system throughput and the energy efficiency is addressed by a weighting parameter in the reward function. Simulation results show that the proposed framework outperforms the traditional inter-cell interference coordination method and improves overall performance.

In cyber physical systems (CPSs) such as smart grids, feedback control confines the system state around the desired one. To monitor the system state, communications are needed to convey the system observations. Entropy is used to bridge the control and communications, since the control action results in a low entropy system. Meanwhile communications arise from positive entropy (i.e., uncertainty), and a lower entropy information source requires less communications for description. However, it is not clear whether the control action always reduces the entropy (thus communications). Hence, two types of entropy reductions, namely reduction in time and reduction when compared with open loop control, are studied. Sufficient conditions for entropy reduction and increase are derived, respectively. Numerical results show that the feedback control reduces the entropy, thus the communication requirement, in typical setups of smart grids. However, there also exist situations in which the feedback control increases entropy, thus demanding more communications.

Millimeter wave (mmWave) has triggered lots of new technologies for the next generation of wireless networks. However, learning the utility and the statistics of spectrum becomes difficult due to the directional antenna beams in mmWave networks. Due to the spatial correlation of spectrum, the efficiency of learning the spectrum can be improved by letting secondary users collaborate and exchange information. In this paper, the collaborative spectrum sensing in mmWave networks is studied by leveraging the similarity between the collaborative learning for spectrum and the recommendation system of electronic commerce such as Amazon. The technique of user-based collaborative filtering is applied. Real data is collected in the 60GHz band by capturing the data stream of the Dell WiGig DOCK and TP-Link WiGig Router. Numerical simulation shows that the proposed strategy can significantly improve the performance of spectrum sensing, especially for easing the false sensing caused by the rotations of antennas. It provides insight for the future study on the potential of spatial reuse in the mmWave band.

The millimeter wave (mmWave) enables high-dimensional antenna arrays for higher gains via beam-forming and spatial multiplexing, which can improve the area spectral efficiency through concurrent transmissions, but also causes stronger channel interference among nodes in multiuser mmWave networks by the spatially focused power. In this paper, we consider the power gain of directional mmWave antenna as a special type of fading due to the randomness of the angle between the interferer and the interfered node. By comparing with small scale fading in traditional omni- directional wireless networks, we firstly show that given some conditions the interference expectation and variance are more severe in mmWave networks in a bounded region than in traditional omni-directional wireless networks, and this observation is further explained by the black body phenomenon in mmWave networks, namely more power will be radiated to the inner side of the deployment region. To alleviate the challenge of interference, a channel management scheme is proposed and several strategies are revisited to reduce the interference and enhance the utilization of available channels.The performance of the proposed algorithm is demonstrated by numerical simulations.

As millimeter wave is becoming the fundamental signaling technology of the physical layer standard in the next generation cellular network, it also brings about many questions and challenges. Not all the existing theories and methods for traditional wireless communications can apply directly to millimeter wave network because of the adoption of directional antenna, blockage effect, and the unprecedentedly large bandwidth. Among them, spectrum sensing is one of the open challenges, for the purpose of dynamic spectrum access in the millimeter wave band. In this paper, we propose a data driven sensing technique based on the mean recurrence time of random process to efficiently detect the change in the primary user (PU) activities, which can tolerate small fluctuations in the distribution. The proposed spectrum sensing works well without a priori knowledge of the PUs, and does not take the assumption of independent and identically distributed observations on the PUs. It can also serve as a general framework for change detection in other areas.

State-of-the-art sensing methods mostly exploit spectrum holes (SHs) in conventional frequency, time, and geography dimensions, which can hardly satisfy the increasing throughput demand of CR networks. Meanwhile, the rapid development of multi-antenna technology makes the terminal obtain the angle recognition capability. Motivated by this, this paper analyzes SHs from the angle/space domain and design a spatial sector based sensing-access scheme. In this case, the SH can be regarded as a kind of particle in spatial-temporal dimension and thus the spatial-temporal uncertainty principle (STUP) is discovered, which reveals an interesting constraint phenomenon between spatial and temporal resolutions. Based on STUP, we propose a novel spatial-temporal resolution tradeoff (STRT) scheme, whose objective is to identify the optimal spatial resolution size to maximize the throughput of CR networks. Different from the conventional temporal domain sensing-throughput tradeoff problem, we study the spatial-temporal cross-dimension optimization, thus fully exploiting SHs' spatial diversity to achieve a better performance. In addition, a fast search algorithm is proposed to track the optimal spatial resolution at an exponential convergence rate. Simulation results verify the efficiency of the proposed tradeoff scheme and search algorithm.

Combined with a quickest detection algorithm for error propagation prevention in joint channel decoding and state estimation, a decoding procedure based on Pearl's belief propagation is proposed to exploit the redundancy of system states in time domain for cyber-physical systems. By applying the proposed scheme to a linear model of electric generator dynamic system, numerical simulations have demonstrated that a better performance of state estimation will be obtained by the joint channel decoding and state estimation, and the error propagation can be successfully eliminated by the error propagation detection algorithm during the state estimation process.

The state-of-the-art eigenvalue-based spectrum sensing methods only consider the partial information of eigenvalues, such as the maximum, minimum, and mean values to make detection, which does not make full use of the eigenvalues to catch correlation. In this paper, we focus on all the eigenvalues of sample covariance matrix in multi-antenna cognitive radio networks and propose eigenvalue weighting-based detection schemes. According to the Neyman–Pearson criterion, the globally optimal weighting solution is the likelihood ratio test (LRT). Hence, we analyze and derive the eigenvalue-based LRT (E-LRT). Utilizing the random matrix theory, a simple closed-form expression for the E-LRT is obtained, which is exactly the optimal eigenvalue weighting scheme. Although the E-LRT is optimal, it is infeasible in practice due to its dependence on the knowledge of primary users and noise powers. Hence, we further analyze suboptimal methods and design maximum likelihood estimation-based approximation weighting approach. Under the approach, both semi-blind (only the noise power is known) and totally-blind methods are correspondingly proposed. In addition, the theoretical performance analysis of these proposed methods are provided. Simulation results are presented to verify the efficiency of the proposed algorithms.

Cognitive radio is one of the enabling technologies considered for the next generation communication systems for many mission-critical applications. In cognitive radio systems, cooperative spectrum sensing is one of the key techniques that can improve reliability and agility. In this paper, a framework that integrates quickest detection and belief propagation is applied to the cooperative spectrum sensing, where the primary user activities are heterogeneous in the space and dynamic in the time. The performance of the proposed scheme is analyzed mathematically. Using numerical simulations, detection performance measured by false alarm rate and average detection delay is obtained for different setups. The results show that the proposed scheme achieves better receiver operational curves than traditional detection method.

The compromise of confidential information will lead to catastrophic disaster during the operation and control in the next generation of power grid. With more advanced yet vulnerable techniques integrated, the system is more exposed to malicious attackers compared with isolated old systems. In this paper, we investigate the privacy issues in the optimal control of load-frequency with information exchange in deregulated power systems. We focus on the problem to stabilize the system while sharing limited information, thus maintaining a proper level of privacy, based on the technique of function decomposition. We prove the existence of an upper bound for the approximation of decomposable functions, which provides privacy protection for each participant in a competitive environment. Then we examine the security and feasibility of different possible mechanisms to achieves an approximate decomposability. Numeral simulation shows the approximated control law obtained from the privacy preserving information exchange for power systems could stabilize the system with barely noticeable performance degradation.

Device-to-Device (D2D) communications are considered as one of the key technologies for 5G wireless communication systems. In this paper, a frequency reuse scheme, in which multiple D2D links share spectrum resources with multiple cellular links simultaneously, is studied. Two novel concepts are introduced, namely the D2D pool and the cellular pool. D2D links in the D2D pool reuse uplink resource blocks occupied by the cellular links in the corresponding cellular pool. To maximize the reuse of spectrum, an algorithm for searching maximum D2D pool is proposed. Moreover, an algorithm for solving the problem of resource competition between different D2D pools is proposed. Simulation results are provided to show that the proposed scheme can well support D2D communications in cellular networks and significantly improve the data rate of D2D links.

Communication is a bottleneck for many distributed machine learning problems with large data sets. One effective approach is to exchange the set of good classifiers among the learners. The key challenge in such an approach is how to represent the set of good classifiers, which can be viewed as a source coding problem in communication systems. A nonuniform sampling framework is proposed for this source coding problem. A metric that describes the quality of the sampled data for the target hypothesis space is proposed. An optimization problem is formulated by minimizing the upper bound of the proposed metric, and an efficient weight based algorithm is provided to solve the optimization problem. Numerical simulations on synthetic and real world data set show that the learning performance based on the proposed sampling framework is close to the global optimum with high confidence, while requiring less samples than baseline random sampling algorithms.

The paper studies the source coding (quantization) for the feedback of discrete-time and continuous-state cyber-physical systems (CPSs). It is formulated as a sequential coding optimization problem. The goal is to find a deterministic policy, as a series of mappings from the historical state information to the quantization strategy for the current feedback state. In particular, an optimization problem is formulated and then solved by the Bellman's Equation in dynamic programming (DP). A more practical solution is proposed by leveraging the approximate dynamic programming (ADP). The performance of the proposed strategy is examined for both scalar and vector dynamic systems, which shows that the designed policy can significantly outperform constant quantization strategies in the CPSs.

In modern cognitive ratio systems, the spectrum is becoming increasingly crowded and expensive; thus spectrum sensing becomes more important than ever before. Traditional spectrum sensing assumes Gaussian noise (or of other given distributions) in general. However when secondary users (SUs) have no prior information about the measurement distributions, the spectrum sensing schemes assuming given distribution forms (even if the parameters are assumed to be unknown) no longer apply. In this paper we propose a universal quickest change detection scheme based on density ratio estimation for spectrum sensing by detecting the sudden change of spectrum (e.g., the emergence of primary user), where neither the pre- change nor post-change distribution (even the distribution forms) is known to SUs, thus achieving robustness to complex spectrum environment.

In the coming era of smart grids, the merging of advanced information infrastructure and power system intrigues the concern of information exchanging and privacy. Here we investigate the optimal distributed control issue with information exchanging in a deregulated power system, in which all utility companies are competitors and collaborators at the same time. The objective is to design a distributed approximate controller that stabilizes the system load frequency through network without manifesting the original system parameters and states. To this end, we derive the existence of an upper bound for the approximation of separable functions, which extends the application of Leontief's Theorem. This proof is then applied to design a distributed control strategy in the context of load frequency control with the awareness of information privacy. Simulation demonstrates that the approximate control law obtained for a two-area system stabilizes the system with negligible sacrifice on the performance of control.

Traditional quickest change detection, which detects distribution changes in random processes, requires full or partial knowledge of pre-change or post-change distributions of samples. In practice, it is possible that the prior information is unavailable, which prohibits the direct application of existing algorithms such as the cumulative sum (CUSUM) algorithm. In this paper, data driven quickest detection is studied, with only the assumption of stationary ergodic (not necessary i.i.d. or Markovian) processes. To fully exploit existing algorithms within the probabilistic framework, the theory of algorithmic complexity (a.k.a. Kolmogorov complexity) is applied to bridge the observed samples and unknown probability distributions. In particular, data compression algorithms such as Lempel-Ziv algorithm are used to measure the unconditional and conditional algorithm complexities. Numerical simulations are carried out to demonstrate the validity of the proposed algorithms.

Previous work on network steganography mainly focused on which field(s) in the cover packet should be used to embed secret information, while largely ignoring how to generate the cover packet in the first place to make it hard to detect. As a result, the cover packet itself may raise suspicions which would defeat the purpose of network steganography. In this paper, we propose a novel traffic behavior learning scheme that can be leveraged to construct hard-to-detect cover packets even if the network traffic is analyzed at a deeper level (i.e., traffic patterns or behaviors). An unsupervised learning method based on the topic model is adopted to discover network traffic behavior. Previously captured network traces in a given network environment are used to train the topic model to learn traffic behavior. The results serve as reference to generate typical network traffic in any given environment and adapt to the network even if it is dynamically changing. In addition to traffic behavior, our model is able to capture network nodes' behavior. This is useful in that both traffic information (what packets to mimic) and node information (which nodes to mimic) can be taken into account when crafting cover packets. Our extensive simulation results show the effectiveness of the proposed scheme learning traffic behavior, indicating that the scheme can potentially be applied to other applications where traffic behavior is needed, besides network steganography.

In order to understand how to operate the physical dynamics of cyber physical systems (CPSs), we study the control of entropy (or equivalently uncertainty) via communications in CPSs. We consider the controller as the Maxwell's demon that decimates the system entropy. Due to the second law of thermodynamics, the system entropy cannot be spontaneously decreased. Therefore, to reduce the system entropy, the controller needs external information communicated from sensors. For a finite state physical system in CPS, we derive upper and lower bounds for the communication requirements. The optimal designs of message mechanism and control policy are proposed.

The emergence of smart meters has enabled the new energy efficiency services in an automatic fashion. With the information and communication technology, the smart meters are devised to gather and communicate the information of electricity suppliers and residential electricity consumers to ameliorate the efficiency of power distribution as well as the sustainability of the power resources. Due to the enormous amount of electricity consumers, the analysis of the big data produced by the smart meters is a crucial challenge faced by the electricity companies and researchers. In this paper, we analyze the big data based on the smart meter readings collected in the Houston area. The statistical properties of the data is investigated such that the behaviors of the consumers can be better understood. Moreover, the kernel PCA analysis and non-parametric clustering of the data gives a comprehensive guidance on what are the potential clusters of the customers and how to allocate the power more efficiently.

Communication is an important issue in cyber physical systems (CPS), which conveys information from sensor(s) to controller(s) in order to control the physical dynamics. When there are multiple sensors receiving correlated observations, it is important to design an efficient distributed source coding scheme. Although distributed source coding has been widely studied in the context of pure data communication networks, such as the information theoretic argument (such as Slepian-Wolf coding schemes) and practical codes design (such as the nested lattice based ones), there are many new challenges in the context of CPS, e.g., the cost function for the purpose of control. In this paper, we propose schemes of lattice based quantization, efficient coloring coding scheme and adaptation to physical dynamics. Numerical simulations with application in smart grids demonstrate that the proposed distributed source coding scheme can efficiently suppress the information source.

In this paper, we study the quality-of-service (QoS) support for realtime traffic in cognitive radio (CR) networks when spectrum availability and quality is not known a priori. A resource-constrained CR relies on sequential channel sensing and probing to resolve spectrum uncertainty and search for good transmission opportunities in real time. We are interested in maximizing the effective throughput the CR can achieve with a desired confidence (success probability) under a spectrum access delay constraint. This quantity can be interpreted as the QoS-compliant (e.g., min-rate and delay) capacity of the CR link under the uncertain spectrum environment. The optimization is formulated as a finite-horizon optimal stopping problem under the objective of maximizing a given percentile of the rate of return at the stopping time. This formulation cannot be directly solved by classical optimal stopping theory, because the latter only supports a mean-reward objective function. A novel transformation is developed to convert the problem into solving a series of sub problems, each of which optimizes a transformed mean-reward of the original problem and therefore can be solved using classical optimal stopping method. We prove the monotonicity of the sub problems, based on which we develop a fast algorithm to efficiently find the unique solution to the original problem. To account for different MAC mechanisms used in practice, our analysis considers both non-recall and recall channel decision strategies. Extensive simulations are performed to verify the effectiveness and significance of the optimization. We show that significant gains (e.g., over 30%) on the QoS-compliant capacity can be achieved by the proposed algorithm when compared with the counterparts.

A key feature of cognitive radio network is the intelligence of secondary users who can collaborate to improve the system performance. The collaboration in terms of channel recommendation is studied in this paper. The recommendation mechanism results in dynamics of the channel preferences of secondary users, thus causing a behavior propagation in a social network. For cognitive radio networks having a grid topology, the ergodicity of the dynamics is studied using the model of interacting particles in nonequilibrium statistical mechanics. For networks having a grid topology or being randomly deployed, mean field descriptions using ordinary differential equation are used to explicitly describe the dynamics of behavior propagation. The analytic results are demonstrated by numerical simulations.

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.

A futures market is proposed for spectrum market in order to manage the financial risk in spectrum trade and discovering future price. The similarities between the spectrum market and electricity market are pointed out. The model of spot market price in the spectrum market is discussed using a set of real activity measurements of cellular base stations. The characteristics of price are analyzed using Black-Scholes model. Based on the models, the option pricing strategies, which determine the price of futures options, and hedging policies, which determine the investment in the futures markets in order to reduce the financial risk, are discussed analytically. Both strategies are tested using the measurement data.

A reverse transmission mechanism for smart grid is introduced, in which the transmission robustness of ad-hoc net-segment is guaranteed. The goal is to address the deadlock issue caused by the broken down of the targeted tower , and to reduce transmission power consumption. Performance analysis of the new mechanism is presented, in which the configuration of different number of electrical towers is discussed. Upon examination of the simulation results, we conclude that the proposed mechanism provides lower latency and power consumption compared to the traditional transmission mechanism, and guarantees the transmission robustness for the smart grid.

In this paper, we study the quality-of-service (QoS) support for realtime traffic in cognitive radio (CR) networks when spectrum availability and quality is not known a priori. A resource-constrained CR relies on sequential channel sensing and probing to resolve spectrum uncertainty and search for good transmission opportunities in real time. We are interested in maximizing the effective throughput the CR can achieve with a desired confidence (success probability) under a delay constraint. This quantity can be interpreted as the QoS-compliant capacity of the CR link under the uncertain spectrum environment. The optimization is formulated as a finite-horizon optimal stopping problem under the objective of maximizing a given percentile of the rate of return at the stopping time. This formulation cannot be directly solved by classical optimal stopping theory, because the latter only supports a mean-reward objective function. A novel transformation is developed to convert the problem into solving a series of sub problems, each of which optimizes a transformed mean-reward of the original problem and therefore can be solved using classical optimal stopping method. We prove the monotonicity of the sub problems, based on which we develop a fast algorithm to efficiently find the unique solution to the original problem. Extensive simulations are performed to verify the effectiveness and significance of the optimization. We show that significant gains (e.g., over 30%) on the QoS-compliant capacity can be achieved by the proposed algorithm when compared with the counterparts.

We propose a dynamic spectrum access scheme where secondary users cooperatively recommend "goodâ channels to each other and access accordingly. We formulate the problem as an average reward-based Markov decision process. We show the existence of the optimal stationary spectrum access policy and explore its structure properties in two asymptotic cases. Since the action space of the Markov decision process is continuous, it is difficult to find the optimal policy by simply discretizing the action space and use the policy iteration, value iteration, or Q-learning methods. Instead, we propose a new algorithm based on the model reference adaptive search method and prove its convergence to the optimal policy. Numerical results show that the proposed algorithms achieve up to 18 and 100 percent performance improvement than the static channel recommendation scheme in homogeneous and heterogeneous channel environments, respectively, and is more robust to channel dynamics.

Cognitive radio (CR) networks have attracted many attentions recently, while the security issues are not fully studied yet. In this paper, we propose a new and powerful network layer attack, routing-toward-primary-user (RPU) attack in CR networks. In this attack, malicious nodes intentionally route a large amount of packets toward the primary users (PUs), aiming to cause interference to the PUs and to increase delay in the data transmission among the secondary users. In the RPU attack, it is difficult to detect the malicious nodes since the malicious nodes may claim that those nodes, to which they forward the packets, behave dishonestly and cause problems in the data transmission. To defend against this attack without introducing high complexity, we develop a defense strategy using belief propagation. First, an initial route is found from the source to the destination. Each node keeps a table recording the feedbacks from the other nodes on the route, exchanges feedback information and computes beliefs. Finally, the source node can detect the malicious nodes based on the final belief values. Simulation results show that the proposed defense strategy against the RPU attack is effective and efficient in terms of significant reduction in the delay and interference caused by the RPU attack.

In contrast to traditional communication networks, the data traffic of cognitive radio network is interrupted by primary users. To evaluate the impact of primary user activities, the transient data traffic dynamics of cognitive radio networks, after being interrupted by primary users, are studied. The Onsager's regression approach, a powerful principle in non-equilibrium statistical mechanics, is applied. The autocorrelation function in the equilibrium state is used to study the time evolution of system deviation from the equilibrium state, according to the principle of Onsager's regression. Analytic approximations have been derived for the autocorrelation function for both generic and fluid approximation cases. Numerical simulations are carried out for a Jackson network model. The validity of the Onsager's regression is verified. The derived approximation of the correlation function is demonstrated to match the true value at the beginning of the relaxation. A scheduling algorithm for fast recovery of equilibrium is also proposed and demonstrated by numerical results.

The design of communication infrastructure in cyber physical system (CPS) is of key importance since it conveys information from sensors to controllers. Different from traditional scheduling algorithms designed for pure data communication networks, the scheduling in CPS is for the purpose of optimizing the physical dynamics and should be designed with the awareness of the dynamics. In this paper, the scheduling problem in CPS is fitted into the framework of hybrid systems, in which different selections of communication links correspond to different dynamics modes. Both centralized and distributed scheduling algorithms are discussed. The proposed algorithms are also applied in the background of voltage control of distributed energy generations (DEGs) in microgrids. Numerical simulations show that the proposed framework and algorithms achieve good performance.

When GPS devices are widely integrated into smart phones, researchers stand a big chance of collecting massive location information, that is necessary in studying users' moving behavior and predicting the next location of the users. Once the next location of a user can be determined, it can serve as input for many applications, such as location based service, scheduling users access in a mobile network or even home automation. One important task in predicting the next location is to identify typical users' moving patterns. In this paper, we propose a novel method to extract the patterns using deep learning. Experiment results show significant performance improvement of the proposed method compared to the classical principal component analysis method.

A key feature of cognitive radio network is the intelligence of secondary users which can collaborate to improve the performance. The collaboration in terms of channel recommendation is studied. Via recommendations, the channel preferences of secondary users become dynamic. The corresponding behavior dynamics of secondary users are studied. Particularly, the ergodicity of the dynamical progress is studied using the model of interacting particles in nonequilibrium statistical mechanics, which is important for predicting the long term dynamics of the cognitive radio network. A mean field description using ordinary differential equation for the dynamics is also used to explicitly describe the behavior dynamics. A Jackson network with lattice topology is simulated to demonstrate the conclusions.

The performance of cognitive radio networks is fundamentally determined by the availability of spectrum resources. Detailed measurement campaigns are needed to collect the spectrum occupancy data to obtain a deeper understanding of the spectrum usage characteristics in cognitive radio networks. This approach, however, is usually inefficient due to the ignorance of the spatial, temporal and spectral correlations of spectrum occupancies, and unpractical because of the geographical and hardware limitations of the cognitive radio nodes. In this paper, we apply the theory of random fields to model the spatial-temporal correlated spectrum usage data, using the two dimensional Ising model and the Metropolis-Hastings algorithm respectively. To efficiently obtain the spectrum occupancy, we adopt the matrix completion technique that leverages the low-rank structure of the data matrix to recover the original data from limited measurements. Simulation results validate effectiveness of the proposed algorithm.

An efficient WiMAX-based multiple access scheme for the state updating in voltage control is introduced. The scheme, called sample-contention scheme, addresses the problem of optimally bringing deviated voltage to a predefined reference level with minimum communication resources by taking advantage of the sparseness of voltage disturbance. An n-sample interval is defined, in which randomly selected voltage monitors broadcast their voltage states and help other unselected ones prioritize transmissions of voltage-state-report using the Maximum Likelihood Estimation (MLE). By analyzing the successful transmission in contention access interval (m-contend), a contention based multiple access is devised. Numerical simulation results show that the sample-contention scheme can reduce the control cost by 20%, compared with the simple round-robin scheduling scheme.

For reducing the channel switching frequency due to uncertain and frequent primary users' interruptions, we design a nonparametric channel allocation algorithm which can effectively manage the spectrum resources in distributed cognitive radio networks and serve for delay-sensitive data traffics. In the proposed algorithm, an infinite Gaussian mixture model is used to get the posterior decision from the feature spaces of the channel idle duration and the primary user's frequency channel, while the collapsed Gibbs sampling is applied for fast convergence. Experimental and simulation results demonstrate that the proposed channel allocation algorithm outperforms the random channel allocation method and the idle probability based channel allocation algorithm in terms of throughput and packet drops.

In cyber physical systems, communication is needed for conveying sensor observations to controllers; thus, the design of the communication sub-system is of key importance for the stabilization of system dynamics. In this paper, multicast routing is studied for networking of decentralized sensors and controllers. The challenges of uncertain destinations and multiple routing modes, which are significantly different from traditional data networks, are addressed by employing the theories of hybrid systems and linear matrix inequalities, thus forming a novel framework for studying the communication sub-system in cyber physical systems. Both cases of neglible delay and non-negligible delay are discussed. The proposed framework is then applied in the context of voltage control in smart grid. Numerical simulations using a 4-bus power grid model show that the proposed framework and algorithm can effectively stabilize cyber physical systems.

Cognitive radio (CR) is a promising technology for future wireless spectrum allocation to improve the usage of the licensed bands. However, CR wireless networks are susceptible to various attacks and cannot offer efficient security. Primary user emulation (PUE) is one of the most serious attacks for CR networks, which can significantly increase the spectrum access failure probability. In this paper, we propose a defense strategy against the PUE attack in CR networks using belief propagation, which avoids the deployment of additional sensor networks and expensive hardware in the networks used in the existing literatures. In our proposed approach, each secondary user calculates the local function and the compatibility function, computes the messages, exchanges messages with the neighboring users, and calculates the beliefs until convergence. Then, the PUE attacker will be detected, and all the secondary users in the network will be notified in a broadcast way about the characteristics of the attacker's signal. Therefore, all SUs can avoid the PUE attacker's primary emulation signal in the future. Simulation results show that our proposed approach converges quickly, and is effective to detect the PUE attacker.

Cognitive radio is a very popular research area in the communication community as cost and bandwidth can be saved by sensing the available licensed spectrum for unlicensed users. This paves the way for the application of cognitive radio in control systems over wireless communication links. A typical control system comprises a sensor interconnected with an actuator, a plant and controller. In this paper, it is assumed that the sensor and estimator communicate through a cognitive radio link. The state estimator needs to adjust to this new communication media as it is affected by interruptions from primary users, resulting in packet losses. The cognitive radio link is modeled as multiple semi-Markov processes each of which can capture the channel dynamics. Two different cases are considered. The first case assumes that acknowledgement of packet arrivals is available at the estimator while the second case does not. For the first case, sufficient conditions are derived for the stability of the peak covariance process which guarantees the stability of the estimator covariance. For the second case, an estimator is designed. Numerical examples are given to show the performance of the proposed estimators. Several applications of the proposed work are also discussed.

The data traffic in cognitive radio subjected to the interruptions of primary users is studied. By incorporating the essential features of cognitive radio traffic, the traffic is modeled as a group of differential equations, similarly to that of vehicle traffics in highway. The stability of the traffic dynamics is analyzed and the corresponding implications on the cognitive radio design are discussed. Particularly, the small and temporary perturbations of primary user interruption may cause long term traffic jam. The traffic jam is essentially caused by the overreaction of traffic control and chain action of followers. Numerical simulations are used to demonstrate the conclusions of the analysis.

The performance of cognitive radio is determined by the spectrum occupancy, which is correlated in space, time and frequency. In this paper, the theory of random fields is applied to model the spatially correlated spectrum occupancy. The entire cognitive radio networks are modeled as a plenary grid or a straight line. Two dimensional and one dimensional Ising models are applied to model the joint distributions of spectrum occupancies, respectively. The phase transition in the network connectivity is studied using the random field model. The average delay and jitter are analyzed using the Ising models. Numerical simulations show that the Ising model can effectively predict the connectivity, average delay and jitter.

The reliability of traffic flows is a serious challenge to cognitive radio networks due to the interruptions from primary users. A backup path scheme is proposed to protect the secondary user traffic flows against the interruptions from primary users. In such a scheme, the traffic flow can be switched to a backup path if the working path is interrupted by primary users. When primary users leave, the traffic flow will be switched back to the working path. The problem of selecting backup paths is formulated as an integer programming problem. Given a backup path, a statistical rule is proposed for switching the traffic flow to the backup path, based on the Bayesian decision framework. The switching rule is implemented on a USRP GNU Radio node based testbed, which consists of a multi-hop cognitive radio network and a primary user. The hardware experiment shows that the system cost, defined as a weighted sum of throughput loss and average packet delay, is reduced by around 50% in a typical setup, compared with the scenario of no backup paths.

In this paper, the problem of spectrum situation reconstruction is considered for large cognitive radio ad hoc networks. The major challenge of such problem lies in the fact that only very limited measurements of spectrum occupancy may be obtained by the cognitive users for a certain location at any given time slot. This is due to both the hardware limitations as well as the tradeoff between spectrum sensing and data throughput of the cognitive users. By representing the spectrum sensing results across the network as an image, we formulate the problem of spectrum situation reconstruction as an image recovery problem. The method of total variation inpainting is applied to solve the problem with low recovery error. The proposed method takes advantage of the correlations in multiple dimensions and the simulation results demonstrate the effectiveness of the proposed scheme.

To address the security and performance challenges, a novel smart grid communication system based on the frequency-hopping (FH) technology is investigated in this paper. A new FH sequence set with various levels of Hamming cross-correlation values is proposed for this system in order to endow the power users various qualities of services (QoSs). By using the real measurement, we propose a statistic model of the data collected from power grid. Based on the proposed FH sequence set and the data model, the analytic expression of the bit error rate (BER) of such a system with binary frequency shift keying (BFSK) modulation is derived under slow Rayleigh fading channel model. This expression firstly reveals the general relation among the BER, the properties of deterministic FH sequence and other system parameters and provides the guidance for the sequence design in the view of the system performance. The simulation results which are performed by using the real measurement data coincide with the analytical conclusions.

Energy efficiency is always an essential concern in modern wireless communication systems. Energy harvesting is a promising technique and can substantially extend the lifetime of power-critical wireless systems such as wireless sensors. Unlike the traditional battery powered system, the system utilizing energy harvesting usually has a smaller battery or merely a super-capacitor. Rather than the limitation on the maximum energy capacity, it is much more critical to take considerations on the energy harvesting rate when an optimal transmission strategy is proposed for such wireless communication systems. In this paper, we study the above issues, and develop an innovative framework considering the characteristics of the wireless communication systems with energy harvesting. The balance between the available resources such as harvested energy and the output such as successful packets is optimized. The simulation results show that our proposed framework works better in all scenarios compared with the baseline strategy.

The communication for the control of distributed energy generation (DEG) in microgrid is discussed. Due to the requirement of realtime transmission, weak or no explicit channel coding is used for the message of system state. To protect the reliability of the uncoded or weakly encoded messages, the system dynamics are considered as a 'nature encoding' similar to convolution code, due to its redundancy in time. For systems with or without explicit channel coding, two decoding procedures based on Kalman filtering and Pearl's Belief Propagation, in a similar manner to Turbo processing in traditional data communication systems, are proposed. Numerical simulations have demonstrated the validity of the schemes, using a linear model of electric generator dynamic system.

The social behavior in cognitive radio networks is studied using analysis tools in social networks. A recommendation system is proposed for cognitive radio, thus incurring the channel preference propagation in the corresponding random geometric network. A mean field based ordinary differential equation is used to describe the dynamics of the channel preference propagation in cognitive radio networks. The conditional distribution of random geometric graph is studied. The convergence and the steady state of the mean field equation are discussed. Numerical simulations are used to demonstrate the properties uncovered by the analysis.

Smart grid, equipped with modern communication infrastructures, is subject to possible cyber attacks. Particularly, false report attacks which replace the sensor reports with fraud ones may cause the instability of the whole power grid or even result in a large area blackout. In this paper, a trustiness system is introduced to the controller, who computes the trustiness of different sensors by comparing its prediction, obtained from Kalman filtering, on the system state with the reports from sensor. The trustiness mechanism is discussed and analyzed for the Linear Quadratic Regulation (LQR) controller. Numerical simulations show that the trustiness system can effectively combat the cyber attacks to smart grid.

Cognitive radio (CR) is a promising technology for future wireless spectrum allocation to improve the usage of the licensed bands. However, CR wireless networks are susceptible to various attacks and cannot offer efficient security. Primary user emulation (PUE) is one of the discovered attacks for CR networks, which can significantly increase the spectrum access failure probability. In this paper, we propose a defense strategy against the PUE attack in CR networks using belief propagation (BP), which avoids the deployment of additional sensor networks and expensive hardware in the networks used in existing literatures. In our proposed approach, each secondary user calculates the local functions based on location information, computes the messages, exchanges messages with the neighboring users, and calculates the beliefs until convergence. Then, the PUE attacker will be detected according to the mean of the final beliefs. After that, all the secondary users in the network will be notified in a broadcast way about the characteristics of the attacker's signal, and avoid the attacker's primary emulation signal in the future. Simulation results show that our proposed technique converges quickly, and is effective to detect the PUE attacker.

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmission requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.

The defense against the Primary User Emulation Attack (PUE) is studied in the scenario of unknown channel statistics (coined blind dogfight in spectrum). The algorithm of the adversarial bandit problem is adapted to the context of blind dogfight. Both cases of complete and partial information about the rewards of different channels are analyzed. Performance bounds are obtained subject to arbitrary channel statistics and attack policy. Several attack strategies, namely uniformly random, selectively random and maximal interception attacks, are discussed. The validity of the defense strategy is then demonstrated by numerical simulation results.

The main challenge to cognitive radio is the emergence of primary users, which can be considered as the service interruptions in a queuing system. The service interruption can incur significant delays for secondary users' data packets which are considered as secondary customers. Therefore, a secondary customer needs to decide whether to join the queue or leave for other means of transmission. It is shown that the individually optimal strategy for joining the queue is characterized by a threshold of queue length. When the current queue length is above this threshold, the secondary customer should leave; otherwise it should join the queue. The socially optimal threshold of queue length is also obtained and is numerically shown to be smaller than the individually optimal one, which implies that the individually optimal strategy does not yield the socially optimal one. To bridge the gap between the individually and socially optimal strategies, a pricing mechanism is proposed to toll the service of each secondary customer, thus equalizing the two optimal strategies. When the channel statistics are unknown, an online learning procedure, based on the Kiefer-Wolfowitz algorithm, is proposed. The proposed algorithms are then demonstrated using numerical simulations.

Wideband spectrum sensing in heterogenous cognitive radio networks has two significant challenges to tackle. One is the spectrum acquisition in the wideband scenario due to the limited sampling capability; the other is how to collaborate among the secondary users. Compressed spectrum sensing provides a powerful approach to acquire wideband signal. Moreover, most cooperative spectrum sensing methods assume that all the secondary users experience the same occupancy of primary users, which may be infeasible in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose a probabilistic graphical model to represent and fuse multi-prior information from one hop neighboring secondary users. Belief propagation (BP) is used for the statistical inference of the spectrum occupancy. Numerical simulation results demonstrate that the proposed BP based cooperative compressed spectrum sensing can effectively achieve cooperation in heterogenous environments and improve performance of compressed spectrum sensing under a low sampling rate and low signal-to-noise ratio (SNR), compared with the other distributed cooperative compressed sensing methods.

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.

There are many key problems of decision making related to spectrum occupancies in cognitive radio networks. It is known that there exist correlations of spectrum occupancies in time, space and frequency, which facilitates the decision making problems. A uniform framework, utilizing graphical models and tools, is proposed to integrate the spectrum correlations and decision making problems. Bayesian networks are used to model the probabilistic dependencies of spectrum occupancies. The statistical inference over the Bayesian network is carried out for spectrum sensing. Influence diagrams are used for cross-layer decision makings by integrating the inference result of the Bayesian network model of spectrum and the QoS requirement from upper layers. A simulated scenario of primary user network is used to generate the spectrum activities, based on which the proposed graphical framework is demonstrated to improve the performance of cognitive radio networks.

In smart grid, a home appliance can adjust its power consumption level according to the realtime power price obtained from communication channels. Most studies on smart grid do not consider the cost of communications which cannot be ignored in many situations. Therefore, the total cost in smart grid should be jointly optimized with the communication cost. In this paper, a probabilistic mechanism of locational margin price (LMP) is applied and a model for the stochastic evolution of the underlying load which determines the power price is proposed. Based on this framework of power price, the problem of determining when to inquire the power price is formulated as a Markov decision process and the corresponding elements, namely the action space, system state and reward function, are defined. Dynamic programming is then applied to obtain the optimal strategy. A simpler myopic approach is proposed by comparing the cost of communications and the penalty incurred by using the old value of power price. Numerical results show the significant performance gain of the optimal strategy of price inquiry, as well as the near-optimality of the myopic approach.

Smart grid is an emerging technology which is able to control the power load via price signaling. The communication between the power supplier and power customers is a key issue in smart grid. Performance degradation like delay or outage may cause significant impact on the stability of the pricing based control and thus the reward of smart grid. Therefore, a QoS mechanism is proposed for the communication system in smart grid, which incorporates the derivation of QoS requirement and applies QoS routing in the communication network. For deriving the QoS requirement, the dynamics of power load and the load-price mapping are studied. The corresponding impacts of different QoS metrics like delay are analyzed. Then, the QoS is derived via an optimization problem that maximizes the total revenue. Based on the derived QoS requirement, a simple greedy QoS routing algorithm is proposed for the requirement of high speed routing in smart grid. It is also proven that the proposed greedy algorithm is a K-approximation. Numerical simulation shows that the proposed mechanism and algorithm can effectively derive and secure the communication QoS in smart grid.

The existing research on security issues in cognitive radio networks mainly focuses on attack and defense in individual network layers. However, the attackers do not necessarily restrict themselves within the boundaries of network layers. In this paper, we design cross-layer attack strategies that can largely increase the attackers' power or reducing their risk of being detected. As a case study, we investigate the coordinated report-false-sensing data attack (PHY layer) and small-back-off-window attack (MAC layer). Furthermore, we propose a trust-based cross-layer defense framework that relies on abnormal detection in individual layers and cross-layer trust fusion. Simulation results demonstrate that the proposed defense framework can significantly reduce the maximum damage caused by attackers.

Energy efficiency is increasingly important for wireless communication systems. An optimization policy for cognitive radio communication systems is derived by employing dynamic programming, thus improving the energy efficiency via drowsy transmission. An alternative Q-learning approach is introduced when the environment model is unknown. Numerical simulation results demonstrate that our proposed optimization is effective. Significant performance gain is achieved especially when the traffic load of secondary user is small, which reduces the energy consumption up to 50%.

In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem. Results of numerical simulations that validated the effectiveness and robustness of the proposed approach are presented. In particular, in noiseless cases, when number of primary user is small, exact detection was obtained with no more than 8% of the complete sensing information, whilst as number of primary user increases, to achieve a detection rate of 95.55%, the required information percentage was merely 16.8%.

Control channel design is an important issue in cognitive radio systems. One potential approach is to use licensed frequency bands and opportunistic spectrum access for conveying control messages. Multiple frequency bands should be reserved to meet the requirement of high reliability for control channel. However, the availabilities of these bands may be asymmetric at the transmitter and receiver. A bad design of control channel may cause deadlock, e.g. the transmitter keeps transmitting at one band which is unavailable for the receiver. Therefore, band synchronization for control channel is studied to avoid deadlock and achieve reliable connection. The band synchronization is considered as a two-party collaboration game and is then studied using game theory. For one-stage case, conditions of Bayesian equilibrium are proposed and explicit expressions are obtained for two-band case. For multiple-stage case, a heuristic algorithm is proposed. Numerical results show that the band synchronization algorithms can achieve good synchronization probability and thus yield a reliable control channel.

In wideband cognitive radio networks having multiple channels, it improves the efficiency of spectrum access to predict the states of unsensed channels at different locations. On considering the channel states as a matrix, whose rows are the indices of locations and columns are the indices of channels, the prediction of channel states is essentially a matrix completion, i.e. reconstructing the whole matrix by using a few known entries. Due to the challenges of high dimensionality and decentralization, as well as the a priori information about the similarity between adjacent channels or locations, the traditional matrix completion approaches, like singular value decomposition (SVD) and nuclear norm optimization, are inefficient. In this paper, we propose to apply the framework of Belief Propagation (BP) for the matrix completion. Both centralized and decentralized versions are introduced. Numerical simulation results show that the proposed BP framework can effectively predict the channel states and thus efficiently orient the spectrum sensing of secondary users. Numerical simulations have shown that, for a scenario with 20000 frequency-space pairs, we can achieve a reconstruction error rate less than 20% (5%) with sampling rate 1% (15%). It is also demonstrated that our proposed algorithm significantly outperforms the general-purpose SVD based matrix completion algorithm.

Primary user emulation (PUE) attack is a serious threat to cognitive radio systems. A passive anti-PUE approach, similar to the random frequency hopping in traditional anti-jamming schemes, is proposed. The defenders randomly choose channels to sense and avoid the PUE attack while the strategy of the attacker is arbitrary. Channel statistics like availability probabilities are assumed unknown to both the attacker and defenders. The environment of each defender, including the attacker, channels and other defenders, is unknown to the defender and is then modeled as an adversarial multi-armed bandit. The technique of adversarial bandit problem is applied in the defenders' strategies to alleviate the problem of unknown environment. Lower bounds of performance for defenders, subject to several typical attack strategies, are derived for single defender case. Numerical simulations demonstrated the performance of the proposed attack avoidance scheme for both the single defender and multiple defender cases.

A Space-Time Turbo Bayesian Compressed Sensing (STTBCS) algorithm is proposed for Ultra-Wideband (UWB) systems in this paper. Based on the sparsity of UWB signals, the STTBCS algorithm provides an efficient approach for integrating spatial and temporal redundancies. A space-time structure is also designed for exploiting and transferring the spatial and temporal \emph{a priori} information for signal reconstructions in the framework of Bayesian Compressed Sensing (BCS). Simulation results using experimental UWB echo signals demonstrate that our STTBCS algorithm achieves good performance for UWB systems, compared with the traditional BCS and multitask BCS algorithms.

The security issue in collaborative sensing in cognitive radio networks can be modeled as attackers and secondary users in a jamming and anti-jamming scenario. In this paper, we introduce a stochastic zero-sum game model to study the strategies. Primary users, secondary users and jammers are the three types of agents in the system. The primary users dictate the system states and their transitions while the secondary users and jammers behave non-cooperatively to achieve their goals independently under different system environment. Our Markovian game model captures not only the zero-sum interactions between secondary users and the jammers but also the dynamics of the system. Our results indicate that the secondary users can enhance their security level or increase their long-term payoff by either improving their sensing capabilities to confuse the jammer with the choice or choosing to communicate under states where the available channels are less prone to jamming. In the numerical experiments, we point out that the payoff of the secondary users increases with the number of available jamming-free channels and is eventually limited by the behavior of primary users.

Cyclostationary feature is incorporated into the framework of quickest spectrum sensing, which is based on the technique of quickest change detection, in cognitive radio systems. Different from detecting the change in a sequence of independent random variables, e.g. energy detection, the property change of random process with correlated observations should be detected in the context of cyclostationary feature based spectrum sensing. Motivated by traditional cumulative sum (CUSUM) test in quickest change detection, a multi-thread competition algorithm for detecting the change in cyclostationary feature is proposed to tackle the difficulty of correlated observations. A thread truncation approach is proposed to reduce the required amount of memory and computation. Numerical results show that the cyclostationary feature based quickest spectrum sensing significantly outperforms the traditional block detection based spectrum sensing scheme.

Resource (power and bandwidth) allocation is an important issue in orthogonal frequency division multiple access (OFDMA) systems. For multi-cell systems, the interference across different cells makes the optimization of resource allocation difficult. Large system analysis is applied for multi-cell OFDMA systems with the fairness constraint of equal grade of service (EGOS). An interference function is defined to model the intercell interference. Variational analysis is used to compute the optimal profile of transmit power and bandwidth. The optimal resource allocation is then computed using numerical simulations.

Cognitive radio technique is applied to Vehicular Ad hoc Networks (VANET) to increase frequency bandwidth. Spectrum sensing for opportunistic spectrum access is carried out collaboratively among neighboring vehicles. For the collaborative spectrum sensing, Belief Propagation (BP) is applied to tackle the distributed observations and to exploit redundancies in both space and time. The corresponding performance is analyzed for a three-vehicle case and is demonstrated using numerical simulations.

Collaborative spectrum sensing is an important technique to combat many random factors in cognitive radio systems. However, it is vulnerable to attacks like false reports of spectrum situation. Particularly, a hit-and-run attack policy is proposed in this paper, which cannot be be mitigated by traditional approaches of attacker detection in cognitive radio systems. Motivated by the system of points for penalizing reckless drivers in DMV, a point based attacker detection algorithm is proposed, whose effectiveness is demonstrated by numerical simulations.

In a heterogeneous cognitive radio system, different secondary users may suffer from different activities of primary user, thus making a joint cooperative spectrum sensing difficult. However, there typically exists strong correlation among secondary users located close to each other, which can substantially benefit the spectrum sensing. To leverage the correlation, belief propagation (BP) is applied for cooperative spectrum sensing in heterogeneous cognitive radio systems. Simplifications and assumptions are proposed to facilitate the message computing and passing in BP. Numerical results show that the BP based cooperative spectrum sensing can significantly improve the performance over single-user spectrum sensing using only a few iterations. Simulation also shows that the performance is insensitive to the knowledge of mutual distance between secondary users, as well as the number of bits used to quantize beliefs, the communication range and packet loss.

A cognitive radio system is modeled as a queue with a server subject to interruptions from primary users. Each customer (packet) arriving at the queue has choices of balking or joining the queue. The individual decision of each customer is optimized based on the reward for the completion of service and the penalty for the waiting time (delay). A socially optimal strategy is also found to maximize the average reward of all customers. Numerical results show that these two strategies, individual and social, generally do not coincide, i.e. an individually optimal strategy does not imply social optimality. To oblige the coincidence of the two optimal strategies, an admission fee to the queue is proposed and the optimal price is found by numerical approaches. To tackle the problem of unknown environments, a learning procedure is proposed to learn the pricing strategy without explicitly modeling the primary users' behavior.

The game of collaborative spectrum sensing is discussed for cognitive radio systems, in which an honest secondary user collaborates with an unclassified secondary user. The honest secondary user needs to determine its trustiness to the collaborator while the collaborator, if being malicious, needs to find the optimal tradeoff between causing more damage to the honest secondary user and hiding its own type. The theory of signaling game is applied to find the equilibrium of the game. Numerical results are provided to illustrate the equilibrium strategies of both secondary users. An interesting observation is that the attacker is unable to attack in the Bayesian equilibrium in certain situations.

Competitive spectrum access is studied for cognitive radio networks. Based on the assumption of rational secondary users, the spectrum access is modeled as a graphical game, in which the payoff of a secondary user is dependent on only other secondary users that can cause significant interference. The Nash equilibrium in the graphical game is computed by minimizing the sum of regrets. To alleviate the local knowledge of payoffs (each secondary user knows only its own payoff for different channels), a subgradient based iterative algorithm is applied by exchanging information across different secondary users. When information exchange is not available, learning for spectrum access is carried out by employing stochastic approximation (more specifically, the Kiefer-Wolfowitz algorithm). The convergence of both situations is demonstrated by numerical simulations.

The problem of Compressed Sensing (CS) based multiple access is studied under the assumption of random data traffic. In many multiple access systems, e.g. Wireless Sensor Networks (WSNs), data arrival is random due to the bursty data traffic for every transmitter. Following the recently developed Compressive Sensing methodology, the technique of compressing the transmitter identities into data transmissions is proposed, such that it is unnecessary for a transmitter to inform the base station its identity and its request to transmit. The proposed compressed multiple access scheme identifies transmitters and recovers data symbols jointly. Numerical simulations demonstrate that, compared with traditional multiple access approaches like carrier sense multiple access (CSMA), the proposed CS based scheme achieves better expectation and variance of packet delays when the traffic load is not too small.

Quickest detection is applied in spectrum sensing in cognitive radio systems when multiple secondary users collaborate with limited communication time slots. When the transmissions of sensing results are not coordinated to avoid confliction, random broadcast is used to exchange information. A necessary condition for the optimal broadcast probability, as a function of the log likelihood ratio of local observation, is obtained using variational analysis. To alleviate the difficulty of computing the optimal broadcast probability, a simple threshold broadcast scheme is proposed. Simulation shows that the proposed threshold broadcast scheme can achieve substantial performance gain (less than 60% in detection delay for the same false alarm rate) over schemes of random broadcast without regulation and single-user spectrum sensing.

Collaborative spectrum sensing is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and then exclude the attacker's report for spectrum sensing. Many existing attacker-detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian attacker detection. However, in practical cognitive radio systems the data fusion center typically does not know the attacker's strategy. To alleviate the problem of the unknown strategy of attacker(s), an abnormality-detection approach, based on the abnormality detection in data mining, is proposed. The performance of the attacker detection in the single-attacker scenario is analyzed explicitly. For the case in which the attacker does not know the reports of honest secondary users (called independent attack), it is shown that the attacker can always be detected as the number of spectrum sensing rounds tends to infinity. For the case in which the attacker knows all the reports of other secondary users, based on which the attacker sends its report (called dependent attack), an approach for the attacker to perfectly avoid being detected is found, provided that the attacker has perfect information about the miss-detection and false-alarm probabilities. This motivates cognitive radio networks to protect the reports of secondary users. The performance of attacker detection in the general case of multiple attackers is demonstrated using numerical simulations.

In cognitive radio systems, primary user emulation (PUE) attack means that an attacker sends primary-user-like signals during the spectrum sensing period such that honest secondary users leave the corresponding channels, which causes a serious threat to cognitive radio systems. A passive anti-PUE approach, similar to the random frequency hopping in traditional anti-jamming schemes, is proposed and called dogfight in spectrum. In this scheme, the defenders randomly choose channels to sense and avoid the PUE attack. It is assumed that the channel statistics like availability probabilities are known; then the PUE attack and the random hopping are modeled as a zero-sum game between the attacker and defending secondary user(s). The Nash equilibrium of the game is found. The anti-jamming efficiency is also obtained. Numerical simulations demonstrated the performances of the proposed schemes for both the single defender, multiple defender and multiple round cases.

This paper proposes a high performance least square solver on FPGAs using the Cholesky decomposition method. Our design can be realized by iteratively adopting a single triangular linear equation solver for modified Cholesky decomposition and forward/backward substitutions. Good performance is achieved by optimizing the Cholesky factorization algorithms, reordering the computation and thus alleviating the data dependency. Dedicated hardware architecture for solving triangular linear equations is designed and implemented for different precision requirements. Compared to software on a Pentium 4, our design achieves a significant speedup.

Quickest detection is applied in the spectrum sensing of cognitive radio systems when multiple secondary users collaborate with limited communication time slots. When transmissions are not coordinated to avoid collisions, random broadcast is used to exchange information. A necessary condition for optimal broadcast probability, as a function of the log likelihood ratio of local observation, is obtained using variational analysis. To alleviate the difficulty of computing optimal broadcast probability, a simple threshold broadcast scheme is proposed. Simulation shows that the proposed threshold broadcast scheme can achieve substantial performance gain (less than 60% in detection delay for the same false alarm rate) over random broadcast without regulation, as well as the single-user spectrum sensing.

Primary user emulation attack in multichannel cognitive radio systems is discussed. An attacker is assumed to be able to send primary-user-like signals during spectrum sensing period and thus jam the secondary user sensing the corresponding channels. An anti-jamming approach for the secondary user is to do random hopping over the multiple channels. When different channels have different qualities (e.g. probabilities of being idle), the secondary user needs to find the optimal tradeoff between choosing good channels and avoiding the jamming of the attacker. The procedure of jamming and anti-jamming is coined dogfight in spectrum due to its nature of pursuit and evasion. For the one-stage case, the dogfight is modeled as a normal zero-sum game and the Nash equilibrium strategy (max-min point) is obtained. For the case of multi-stage game, the dogfight is essentially a stochastic game with partial observations and imperfect monitoring. The game is analyzed by fixing the strategy of the secondary user and finding the optimal attacking strategy of the attacker using the framework of partially observable Markov decision process (POMDP). The optimal strategies are numerically computed for both one-stage and multi-stage games.

The emerging biometric cryptography has gained significant interests for key management and privacy protection, but the previously proposed schemes using set metrics for fingerprints may either be too weak to offer enough security or suffer from the performance limitations. In this paper, a new fuzzy cryptographic technique without use of chaff data, Randomized Dissolvent Template (RDT), is proposed for biometric set modalities. The proposed technique is designed to dissolve the enrolled biometric set into a random secret resource, so as to construct robust secured templates by exploiting at least two resources of randomness. In this way, when one fingerprint is used for multiple applications, each time the additional information leakage by secured templates will not exceed the new introduced random information, so RDT is reusable. We thus provide two novel RDT-based constructions in practice: Fuzzy Reconciler using set difference threshold and Fuzzy Dissolver using set intersection threshold. Security analysis proves the new constructions have enough computational complexity for the required security properties, and implementations on FVC2002DB fingerprint database show that the proposed schemes can bring about better accuracy performance over current Fuzzy Vault and Fuzzy Extractor, thus are more promising for biometric-based security applications.

Collaborative spectrum sensing in cognitive radio networks has been proposed as an efficient way to improve the performance of primary users detection. In collaborative spectrum sensing schemes, secondary users are often assumed to be trustworthy. In practice, however, cognitive radio nodes can be compromised. Compromised secondary users can report false detection results and significantly degrade the performance of spectrum sensing. In this paper, we investigate the case that there are multiple malicious users in cognitive radio networks and the exact number of malicious users is unknown. An onion-peeling approach is proposed to defense against multiple untrustworthy secondary nodes. We calculate suspicious level of all nodes according to their reports. When the suspicious level of a node is beyond certain threshold, it will be considered as malicious and its report will be excluded in decision-making. We continue to calculate the suspicious level of remaining nodes until no malicious node can be found. Simulation results show that malicious nodes greatly degrade the performance of collaborative sensing, and the proposed scheme can efficiently detect malicious nodes. Compared with existing defense methods, the proposed scheme significantly improves the performance of primary user detection, measured by ROC curves, and captures the dynamic change in the behaviors of malicious users.

Quickest detection of an abrupt distribution change with an unknown time varying parameter is considered. A novel adaptive approach is proposed to tackle this problem, which is shown to outperform the celebrated Parallel CUSUM Test. Performance is evaluated through theoretical analysis and numerical simulations.

Spectrum sensing, which monitors the spectrum activity, is studied for cognitive radio systems using multiple frequency bands with non-negligible band switching time (blind period). Due to hardware limitation, it is assumed that only a subset of frequency bands can be monitored simultaneously. The problem of controlling the monitoring procedure is studied in the frameworks of dynamic programming (DP). System states and cost functions are defined. Cost-to-go functions for DP are derived, simplified and approximated, based on which control policies are derived. Numerical results are provided to demonstrate the proposed algorithms.

Energy efficiency has become increasingly important to mobile systems on which wireless interfaces are among the largest power consumers. While existing physical layer power optimization mostly focuses on improving the transmission efficiency, our recent work has showed that wireless interfaces can spend most of its time and energy in very short idle periods between transmitting two packets [9]. In this work, we present a physical layer optimization method, drowsy transmission, which explicitly considers the power cost of such idle periods in physical layer power optimization through joint power control/rate selection and power management. We provide a control theoretical formulation of the optimization problem and present a dynamic programming based solution and its approximation that is close form and practical. We further offer an on-line learning technique to cope with unknown channel and traffic. Using a power model from a commercial wireless network interface card, we demonstrate that drowsy transmission can reduce the energy per bit by 70% and 40% in comparison to power control/rate selection-based optimization and optimization with disjoint power control/rate selection and power management, respectively. Moreover, the achieved energy per bit is very close to the theoretical lower bound. Our evaluation shows that the proposed on-line learning technique can assess the channel and approach the performance under pre-known channel in as short as 200 ms. We also show that our optimization introduces negligible packet delays.

A feedback structure and signal recovery algorithm, orthogonal pruning pursuit, are proposed to apply sequential compressed sensing (CS) on ultra-wideband (UWB) communications. The prior knowledge is exploited by the feedback structure in time sequence UWB signals. The orthogonal pruning pursuit algorithm is utilized to reconstruct UWB echo signals by deleting zero entries to pursuit true non-zero elements. Numerical simulations show that using the proposed scheme, far fewer measurements are needed to achieve good performance compared with other CS algorithms for UWB communications. This saves substantial hardware resources in practical systems.

Resource allocation is an important issue in cognitive radio systems. It can be done by carrying out negotiation among secondary users. However, significant overhead may be incurred by the negotiation since the negotiation needs to be done frequently due to the rapid change of primary users' activity. In this paper, a channel selection scheme without negotiation is considered for multi-user and multi-channel cognitive radio systems. To avoid collision incurred by non-coordination, each secondary user learns how to select channels according to its experience. Multi-agent reinforcement leaning (MARL) is applied in the framework of Q-learning by considering opponent secondary users as a part of the environment. The dynamics of the Q-learning are illustrated using Metrick-Polak plot. A rigorous proof of the convergence of Q-learning is provided via the similarity between the Q-learning and Robinson-Monro algorithm, as well as the analysis of convergence of the corresponding ordinary differential equation (via Lyapunov function). Examples are illustrated and the performance of learning is evaluated by numerical simulations.

Uplink code-division multiple access (CDMA) multiple-input and multiple-output (MIMO) systems are considered in the large system limit within the assumptions of synchrony and frequency flat fading. The outage region maximizing the sum capacity of non-outage users is obtained, which extends the criterion of outage in single-input and single-output (SISO) CDMA systems.

Eigendecomposition of estimated covariance matrices is a basic signal processing technique arising in a number of applications, including direction-of-arrival estimation, power allocation in multiple-input/multiple-output (MIMO) transmission systems, and adaptive multiuser detection. This paper uses the theory of non-crossing partitions to develop explicit asymptotic expressions for the moments of the eigenvalues of estimated covariance matrices, in the large system asymptote as the vector dimension and the dimension of signal space both increase without bound, while their ratio remains finite and nonzero. The asymptotic eigenvalue distribution is also obtained from these eigenvalue moments and the Stieltjes transform, and is extended to first-order approximation in the large sample-size limit. Numerical simulations are used to demonstrate that these asymptotic results provide good approximations for finite systems of moderate size.

The spectral efficiency of equal-rate DS-CDMA systems with multiple transmit antennas is analyzed. Explicit expressions for the spectral efficiency are obtained in the low and high energy regions and numerical results are presented for the moderate energy region. The performance gains resulting from the use of multiple transmit antennas are discussed. An equation characterizing the diversity-multiplexing tradeoff in this situation is derived. A conclusion that adding more transmit antennas does not necessarily improve the performance with the constraint of equal rate is drawn

Power allocation and the resulting spectral efficiency are considered for DS-CDMA systems operating over fading channels with a fixed single quality of service (QoS) requirement. For systems both with and without channel state information at the transmitter, the decoding scheme of successive interference cancellation combined with minimum mean square error (SIC+MMSE) multiuser detection is shown to minimize the total transmitting power under certain conditions. Both upper and lower bounds on the required receive power are obtained for SIC+MMSE systems with channel state information at transmitters (CSIT) and an analytical expression for the required transmit power is obtained for SIC+MMSE systems without CSIT. The parameters of spectral efficiency in both low and high energy regions are obtained. The good performance of SIC+MMSE in non-elastic traffic systems is also demonstrated.

Power allocation results for equal-rate systems developed in Part I are extended to multiple-rate systems. For both systems with and without channel state information at the transmitter (CSIT), conditions for the optimality of successive interference cancellation combined with minimum mean square error multiuser detection (SIC+MMSE) are obtained. When CSIT is available, the optimal decoding order is discussed for optimal SIC+MMSE and two sub-optimal decoding schemes. Explicit expressions for the spectral efficiency parameters in the low or high energy regions are also obtained.

Communication through dispersive direct-sequence code-division multiple-access channels suffers from intersymbol and multiple-access interference, which can significantly impair performance. Joint maximum a posteriori probability equalization and multiuser detection with error control decoding can be used to mitigate this interference and to achieve the optimal bit-error rate. Unfortunately, such optimal detection typically requires prohibitive computational complexity. This problem is addressed in this paper through the development of a reduced state trellis search detection algorithm, based on decision feedback from channel decoders. The performance of this algorithm is analyzed in the large-system limit. This analysis and simulations show that this reduced complexity algorithm can exhibit near-optimal performance under moderate signal-to-noise ratio and attains larger system load capacity than parallel interference cancellation.

The paper considers the analysis of successive interference cancellation (SIC) based multirate DS-CDMA systems. Power allocation in the context of various detection and decoding schemes, including SIC-MMSE and group SIC (GSIC), is discussed. Optimal decoding order for these schemes is analyzed. Spectral efficiency in both non-fading and frequency flat fading channels is discussed by obtaining explicit expressions for minimum energy per bit and spectral slope, and the results are compared with a multi-code scheme.

For practical wireless DS-CDMA systems, channel estimation is imperfect due to noise and interference. The impact of channel estimation error on multiuser detection (MUD) is analyzed under the framework of the replica method. System performance is obtained in the large system limit for optimal MUD, linear MUD and turbo MUD, and is validated by the numerical results of finite systems.

In practical wireless communications, channel estimation is imperfect due to the effects of noise and interference. This paper analyzes the impact of imperfect channel estimation on a form of turbo multiuser detection based on linear minimum-mean-square-error (MMSE) detection and parallel interference cancellation. In particular, the variance of the noise caused by the channel estimation error is analyzed for full and reduced rank MMSE detectors and parallel interference cancellation in the large system limit.

Power allocation and spectral efficiency of multirate and single code (SC) direct-sequence code division multiple access (DS-CDMA) systems without channel state information at transmitters in frequency flat fading channels are discussed. An explicit expression is found for the optimal power allocation under some mild assumption. An interference cancelling and minimum-mean-square-error detection scheme is shown to achieve the optimal power consumption. Spectral efficiencies of both SC and multiple code (MC) schemes are obtained, and numerical results show that MC outperforms SC due to the former's utilization of user diversity.

This paper analyzes the iterative channel estimation and multiuser detection in multipath fading coded CDMA channels. The maximum likelihood criterion is used for channel estimation while the interference is cancelled with direct interference cancellation. The channel decoder output is fed back to enhance the performance of the channel estimator and interference canceller. The asymptotic system performance is analyzed, and conditions for convergence and uniqueness of fixed point are given.

Communications in dispersive multiuser CDMA channels suffer from both intersymbol and multiuser interference. MAP equalization and multiuser detection with error control coding can be used to combat this interference and achieve optimal performance. For reducing the high computation complexity of the optimal algorithm, a reduced-state trellis search, which exploits decision feedback from the decoder is proposed. Simulations show that this low complexity algorithm can obtain near-optimal performance under medium signal-to-noise ratio and has larger system load capacity than parallel interference cancellation.