Accurate estimation of channel state information (CSI) is crucial to realize the full potential of massive multiple-input multiple-output (MIMO) communication systems. Despite the high accuracy of existing CSI estimators, the high computational complexity makes them impractical for real-world massive MIMO systems. So, developing a highly accurate and low-complexity estimator has been a long-standing challenge in the field of massive MIMO. In this paper, we present a channel estimation scheme that significantly reduces computational complexity while achieving high accuracy. Firstly, we design a two-stage training protocol whereby only part of the transmit and receive antennas are activated for signal emission or reception. Secondly, we apply the low-complexity Least-Square (LS) estimator to acquire two sub-blocks of the channel matrix. Relying on the inherent low-rank property of channel matrix, the complete channel matrix is reconstructed via a randomized matrix approximation technique. Thirdly, we consider three different power allocation schemes to further optimize the pilot power in 2-stage training process. The theoretical bounds of estimation error for our CSI estimator are derived, and the optimal power allocation strategy is thus obtained by minimizing this error bound. Numerical simulations are provided to demonstrate our proposed method. As shown, the theoretical error bound is tight, and the optimal power allocation can achieve the substantial gain. Our CSI estimator incurs the same complexity as the popular LS estimator, whilst the estimation accuracy is improved by ~5 dB, which has the great promise to new-generation massive MIMO communications.

In recent years, vehicular communications have attached great interests in both academy and industry for its potential of promoting safety and autonomous driving. Unlike classical communication scenarios, in vehicular communications the optimal resource allocation must be accomplished in a real-time manner, in order to maximally reduce the response delay. This presents a substantial challenge for current machine learning based intelligent resource optimization methods which may be sample inefficient, especially when the problem space becomes extremely huge. In this study, we develop a fast reinforcement learning (RL) framework for the real-time resource optimization of vehicular communications, whereby the transmitting power and the accessing frequency channels need to be jointly allocated. The main concept of our new method is that it incorporates a sample efficient structured exploration mechanism in the action space, which firstly ignores the local exploitation but focuses on a randomized global exploration. Thus, our exploration-first method, in contrast to classical exploitation-first RL, can reconstruct the coarse-grained global landscape of a huge Q-table from only the few samples. This learned prior knowledge would remarkably accelerate the convergence of subsequent incremental learning process, by concentrating on the identified attentional subspace of the Q-table. As demonstrated by numerical results, our new method would reduce the time complexity or the response delay by around 10 folds. As such, our fast RL method would have the great potential to such challenging optimization problems whereby the acquisition of massive training samples is time demanding, which hence provides the great promise to the emerging vehicular networks.

In this article, we study a previously overlooked work of designing the constellation for concentration shift keying (CSK) in diffusion-based molecular communication (MC) systems, considering the adverse effects of signal-dependent noise and intersymbol interference (ISI). Against this background, the conventionally used uniform constellation strategy is no longer the optimal choice for CSK. Considering the discrete nature of messenger molecules, the constellation design of CSK is essentially a nonlinear integer programming problem. However, due to the nonconvexity of the goal function, general convex optimization methods cannot be easily employed. To solve the aforementioned problem, a novel CSK strategy with a brute-force search algorithm is proposed to obtain the optimal constellation numerically. Moreover, a low-complexity search algorithm that provides new insight into reducing the complexity of MC systems is further presented. Specifically, we theoretically derive the optimal threshold and employ a maximum likelihood detector that effectively minimizes the average symbol error rate (SER) by leveraging the knowledge of ISI at the receiver. Finally, we can obtain a fitted equation for the nearly optimal constellation points under specific channel parameters, attaining the SER performance similar to the optimal one, while the search complexity is much lowered.

To glean the benefits offered by massive multi-input multi-output (MIMO) systems, channel state information must be accurately acquired. Despite the high accuracy, the computational complexity of classical linear minimum mean squared error (MMSE) estimator becomes prohibitively high in the context of massive MIMO, while the other low-complexity methods degrade the estimation accuracy seriously. In this paper, we develop a novel rank-1 subspace channel estimator to approximate the maximum likelihood (ML) estimator, which outperforms the linear MMSE estimator, but incurs a surprisingly low computational complexity. Our method first acquires the highly accurate angle-of-arrival (AoA) information via a constructed space-embedding matrix and the rank-1 subspace method. Then, it adopts thepost-receptionbeamforming to acquire the unbiased estimate of channel gains. Furthermore, a fast method is designed to implement our new estimator. Theoretical analysis shows that the extra gain achieved by our method over the linear MMSE estimator grows according to the rule of O(log10M), while its computational complexity islinearlyscalable to the number of antennasM. Numerical simulations also validate the theoretical results. Our new method substantially extends the accuracy-complexity region and constitutes a promising channel estimation solution to the emerging massive MIMO communications.

Abstract Accurate channel state information (CSI) is extremely crucial to realize high‐accurate hybrid precoding, in millimeter wave communication systems. In order to improve the CSI estimation performance, several traditional channel estimators have been developed by exploiting the sparse or low‐rank property, whilst they bring huge channel training overhead and computational complexity. In this work, based on jointly sparse and low‐rank property of massive multiple input multiple output (MIMO) channel, one non‐convex mmWave channel estimation problem is formulated and a novel scheme to acquire one accurate CSI estimation result with greatly reduced training overhead is proposed. Specifically, the non‐convex problem is reformulated as two simple sub‐problems, by exploiting the alternating direction method of multipliers (ADMM) technique. Based on the low‐rank characteristic, one fast gradient descent matrix completion algorithm is developed to accurately solve the first sub‐problem. On this basis, the compressed sensing (CS) technique to acquire the accurate CSI estimation matrix is further utilized. Numerical simulation validates that the method could achieve the much higher channel estimation accuracy, yet only incurs the lower overhead compared with the traditional scheme.

Molecular communication is attractive to the emerging nano-scale communication systems. Traditionally, a detector recovers the information from only the concentration of single messenger molecule, while ignoring the variation of multiple participants in biochemical reaction. In this paper, we propose a non-coherent signal detector, by fully exploiting this ubiquitous biochemical diversity property of multiple reacting molecules. After extracting the channel state information (CSI) independent non-coherent features of received signals, the dynamical transient characteristics of messenger, reactant and product molecules are all utilized to implement the diversity detection, thus formulating a functional single-input multiple-output (SIMO) system via reaction-diffusion communication that has been barely considered before. We design both hard and soft combination strategies to attain the potential diversity gain arise from the dynamical co-variation of participants. Theoretical analysis and numerical simulations are provided to demonstrate the advantages of our detector. Compared with conventional detectors that use only single messenger molecule, the bit error rate (BER) of is substantially reduced. Moreover, the BER performances of our non-coherent detector are even better than coherent maximum a posteriori (MAP) detector that requires accurate CSI estimation, which confirms the dramatic diversity gain provided by our detector. It would have great potentials in reliable nano-scale communications.

Federated learning (FL) enables multiple local clients to collaboratively train a global model, which can reduce privacy leakage by sharing model parameters instead of private datasets. However, recent works have revealed that gradient-based data reconstruction attacks, e.g., deep leakage from gradients (DLG), improved DLG (iDLG), and inverting gradients (IG), may still reveal private information by exploiting model parameters from a local client. Current privacy-preserving FL strategies, e.g., differential privacy or gradient compression, can handle such attacks to some extent, but seriously sacrifice their model accuracy. In this work, we propose a novel privacy-preserving FL method, named privacy-encoded FL (PEFL), to combat such data reconstruction attacks without degrading the model performance. The key concept of PEFL is that each large weight matrix of the neural network model is decomposed into multiple cascading sub-matrices, which thus establishes a novel privacy-encoded mechanism by introducing an entangling nonlinear mapping between model gradients and raw data. As such, multiple sub-matrices are directly trained in parallel at the local clients, but only partial sub-matrices are reported to a global server, which suffices to reconstruct the global model whilst increasing the complexity of the coupling between the model parameters and raw data. We provide a detailed analysis of the accuracy, security, and complexity of our method. As shown, it breaks the limit of classical defensive methods, by significantly reducing the risk of data reconstruction attacks yet not degrading the model performance. Compared to classical defenses, the proposed PEFL decreases the peak-signal-to-noise ratio (PSNR) between the reconstructed data and the raw data by ~20dB, without sacrificing the test accuracy. As a new paradigm for privacy-preserving FL, our proposed method has great potential in privacy-demanding learning applications.

The development of reconfigurable intelligent surfaces (RIS) has recently advanced the research of physical layer security (PLS). Beneficial impacts of RIS include but are not limited to offering a new degree-of-freedom (DoF) for key-less PLS optimization, and increasing channel randomness for physical layer secret key generation (PL-SKG). However, there is a lack of research studying how adversarial RIS can be used to attack and obtain legitimate secret keys generated by PL-SKG. In this work, we show an Eve-controlled adversarial RIS (Eve-RIS), by inserting into the legitimate channel a random and reciprocal channel, can partially reconstruct the secret keys from the legitimate PL-SKG process. To operationalize this concept, we design Eve-RIS schemes against two PL-SKG techniques used: (i) the CSI-based PL-SKG, and (ii) the two-way cross multiplication based PL-SKG. The channel probing at Eve-RIS is realized by compressed sensing designs with a small number of radio-frequency (RF) chains. Then, the optimal RIS phase is obtained by maximizing the Eve-RIS inserted deceiving channel. Our analysis and results show that even with a passive RIS, our proposed Eve-RIS can achieve a high key match rate with legitimate users, and is resistant to most of the current defensive approaches. This means the novel Eve-RIS provides a new eavesdropping threat on PL-SKG, which can spur new research areas to counter adversarial RIS attacks.

Due to the public nature of wireless environments, legitimate communication systems are generally under the threat of Pilot Spoofing Attack (PSA). The malicious user may manipulate channel estimation by emitting the same pilot information, leading to the biased estimation of Channel State Information (CSI) and the degraded secrecy capacity. Although various methods have been proposed for combating PSA, the ubiquitous mobility of legitimate and malicious users introduces complex time-varying characteristics, making previous static PSA detection methods less attractive. In this paper, we present a novel location-awareness dynamical PSA detection mechanism. To model the complex dynamical behaviors, a Random Finite Set (RFS) is formulated to jointly describe the mobile positions and uncertain attack status. On this basis, we design a joint PSA detection and user localization algorithm relying on the sequential Bayesian inference. As such, the inherent correlations in mobile patterns can be exploited to effectively enhance PSA detection probability and localization accuracy. Numerical simulation results validate that the new method significantly improves PSA detection and CSI estimation accuracy compared with state-of-the-art counterparts, therefore the information leakage problem is greatly alleviated. Our new approach thus has the great potential in the emerging mobile scenarios by effectively enhancing the physical-layer secured transmissions.

With significantly dynamic characteristics of the new aerial users, the emerging cellular-enabled unmanned aerial vehicle (UAV) communication paradigm raises great challenges to current research of UAV applications. As far as the robust channel allocation is concerned, the high mobility of UAV nodes and the unexpected disturbance of external environment would render most existing methods which rely on definite information and are vulnerable to dynamic environment, become less attractive or even invalid. In this paper, we particularly investigate a cellular-enabled mesh UAV network exploiting partially overlapping channels (POCs), and propose a distributed fuzzy space based learning scheme for POCs allocation to combat the dynamic environment. Rather than the perfect channel state information (CSI) assumption, the dynamic and uncertain CSI of UAVs is characterized by fuzzy number. On this basis, the allocation process can be implemented in a mapped fuzzy space. Integrating fuzzy-logic and game based learning, we formulate the problem of POCs assignment as a fuzzy payoffs game (FPG), and demonstrate the existence of fuzzy Nash equilibrium for our designed FPG. Then, with the derived priority vector in the fuzzy space, the equilibrium solution can be achieved by the proposed algorithm. Numerical simulations demonstrate the advantages of our new scheme.

In recent robotic applications, a critical need is to simultaneously detect communication (emission state) and estimate its trajectory. Whilst wireless sensor observations are useful, they are often uncertain due to the stochastic communication bursts and robot mobility. Over-sampling the information environment can incur excessive radio interference and energy usage. Therefore, one challenge is how to improve the efficiency of sensing under sparse and dynamic information, and make accurate inference on the robot's location. Here, we design a novel mixed detection and estimation (MDE) scheme to enhance both the accuracy and the efficiency by exploiting the mobility pattern correlations. Relying on a Markov state-space model, dynamic behaviors of robot's communication state and movement are formulated. A two-stage sequential Bayesian scheme, premised on random finite set (RFS), is developed to detect and estimate the involved unknown states. Specifically, in order to counteract the probability likelihood disappearance (caused by no information emission) and improve robustness to ambient noise, a sequential pre-filtering technique is designed, which can refine local observations and thereby significantly improve the accuracy of the system. We validate the proposed MDE scheme via both theoretical analysis and numerical simulations, demonstrating it would improve both the detection and estimation accuracy and efficiency.

To support large-scale unmanned aerial vehicle (UAV) networks with both payload communication (PC) packets and control and non-payload communication (CNPC) packets, millimeter wave (mmWave) communication is convinced to be a promising solution. However, efficient user association and spectrum allocation are still challenging in mmWave-enabled UAV networks considering that the network state is dynamic and the status information of each UAV is incomplete. In this paper, we investigate a joint UAV association and spectrum allocation problem under a hybrid mmWave sharing paradigm, where PC packets are transmitted over both licensed and pooled bands in a shared manner to achieve high throughput and CNPC packets are transmitted over licensed band in an exclusive manner to guarantee high reliability. To this end, we introduce a strategic form game that can characterize network stochastic states and individual partial observations to reformulate the problem, and then propose a counterfactual regret minimization scheme to achieve its correlated equilibrium (CE). Benefited from the updating mechanism on randomobservation-actionpairs, the designed scheme can converge to the corresponding CE solutions for the two types of packets with partial observation. Finally, our simulation results demonstrate the superior performance of the proposed scheme over the baseline schemes.

Subspace methods, e.g., multiple signal classification algorithm (MUSIC), show great promise to high-resolution environment sensing in the 6G-enabled mobile Internet of Things (IoT), e.g., the emerging unmanned systems. Existing schemes, aiming to simplify the computational 1-D search of the MUSIC pseudospectrum, unfortunately have still an unaffordable complexity or the compromised accuracy, especially when the millimeter-wave massive multiple-input–multiple-output (MIMO) radar is considered. In this work, we address the fast and accurate estimation of the high-resolution pseudospectrum in massive MIMO radars. To enable real-time automotive sensing, we first formulate this computational procedure as one matrix product problem, which is then solved by leveraging randomized matrix sketching techniques. To be specific, we compute the large matrix productapproximatelyby the product of two small matrices abstracted via random sampling. To minimize the approximation error, we further design another sampling, pruning, and recomputing (SaPRe) algorithm, which refines the approximated results and thus attains the exact pseudospectrum. Finally, the theoretical analysis and numerical simulations are provided to validate the proposed methods. Our fast approaches dramatically reduce the time complexity and simultaneously attain the accurate Direction-of-Arrival (DoA) estimation, which have the great potential to real time and high-resolution automotive sensing with massive MIMO radars.

The advanced 6G Technology benefits the Internet of Things (IoT) in various applications. As one essential application scenario, smart grid (SG) incorporates communication and management techniques and promises an efficient and intelligent power system, whereby cognitive radio (CR) is believed to be an essential tool for better resource utilization in power generation and delivering processes. In the CR-assisted IoT in SG scenarios, channel detection (CD) will play an essential role to accurately sense the available channel resource. However, for SG scenarios, high-accuracy CD may become a challenging task in complex power supply environments with unexpected impulsive noise (IN) and channel fading, which will significantly affect the signal statistical property. To address this problem, we propose a novel CD mechanism in the context of the wireless environment with IN and random channel fading. To be specific, taking the wireless channel status, IN and time-variant fading into account, a novel quaternary hypothesis testing model (QHTM) is formulated to describe the detection task, and by which a new dynamic state-space model (DSM) is developed to capture the dynamical behavior of the CD system. On this basis, a joint detection and estimation algorithm based on Bayesian statistical inference is devised to accomplish the CD task. Benefiting from the jointposterioridistribution estimation procedure, our algorithm can not only accurately detect the unknown channel status, but also estimate the real-time channel state information (CSI), thereby eliminating their effects on the detection performance. Numerical simulation results validate the proposed CD mechanism.

Emerging cellular‐enabled unmanned aerial vehicles (UAVs) communication poses both opportunities and challenges to the current research of UAV applications. On the one hand, the advanced cellular technologies and authentication mechanisms make significant performance improvements of reliability, security, coverage, and throughput of UAVs possible. On the other hand, the considerably dynamic characteristics of the new aerial users bring some complicated and open issues to the future heterogeneous network. To throw some light on this field, the authors study the joint power allocation and channel reuse problem of uplink transmission in a cellular‐enabled UAVs network with full consideration of the rapid channel variations resulting from high mobility of UAVs. Given the diverse requirements of terrestrial cellular users (CUs) and UAVs, i.e. large capacity for CU links and ultra‐reliability for UAV links, they formulate the problem as optimising the uplink capacities of CUs with reliable transmission constraint of UAVs. By decoupling the intricate problem into two sub‐problems, a joint resource allocation algorithm is proposed, which relies only on the statistical information of dynamic channels to implement, hence is robust. Finally, numerical results are provided to corroborate the anticipated performances of the proposed scheme.

This study considers a downlink power consumption problem for unmanned aerial vehicles (UAVs)‐assisted wireless communications, in which UAVs are used as aerial base stations to provide service for the ground users and equipped with a directional antenna of fixed beamwidth. Moreover, the on‐board circuit power of UAV is taking into consideration. The authors derive a closed‐form expression for the optimal flying altitude and number of UAVs by minimising the total power consumption under the users' rate requirements in the given coverage area. The numerical simulation and theoretical results show that the optimal flying altitude of UAVs depends on the beamwidth of the directional antenna at UAVs, the on‐board circuit power of UAVs, and the rate constraint of each user.

In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channel is unknown, existing coherent schemes (e.g., the state-of-the-art maximum a posteriori, MAP) have to pursue complex channel estimation and ISI mitigation techniques, which will result in either high computational complexity, or poor estimation accuracy that will hinder the detection performance. In this paper, we develop a novel high-dimensional non-coherent detection scheme for molecular signals. We achieve this in a higher-dimensional metric space by combining different non-coherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space, at the expense of higher complexity on computing the multivariate posterior densities. The realization of this high-dimensional non-coherent scheme is resorting to the Parzen window technique based probabilistic neural network (Parzen-PNN), given its ability to approximate the multivariate posterior densities by taking the previous detection results into a channel-independent Gaussian Parzen window, thereby avoiding the complex channel estimations. The complexity of the posterior computation is shared by the parallel implementation of the Parzen-PNN. Numerical simulations demonstrate that our proposed scheme can gain 10dB in SNR given a fixed BER as 10-4, in comparison with other state-of-the-art methods.

Deep neural network are one of the most powerful model for machine learning, which can learn the underlying patterns automatically from a large amount of data. So it can be extensively used in more and more Internet-of-Things (IoT) applications. However, the training of deep models is difficult, suffering from overfitting and gradient vanishing problem. Besides, the large amount of parameters and multiplication operations make it impractical for most deep learning models to directly execute on target hardware. In this paper, we propose a method of gradually pruning the weakly connected weights to improve the traditional stochastic gradient descent. And we adopt a reinforcement learning method called learning automata to find the weakly connected weights on account of its strong policy-making ability in stochastic and nonstationary environment. Our proposed method can learn a more effective and sparsely connected architecture during training from the initially fully connected neural networks. The experiments on MNIST show that our method have stronger power to defeat overfitting and can get better generalization performance on test set. Meanwhile, the thin and sparsely connected model we get can be more suitable for IoT applications.

Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this paper, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing.

Despite the great promises in next-generation wireless communications, dynamic spectrum access (DSA) remains still as a major challenge in uncertain environments, e.g., varying unknown channels. Existing popular schemes, i.e. potential game approaches, rely on the definite reward and a greedy strategy, which become unfortunately invalid in such varying and uncertain scenarios. In this paper, we propose a robust fuzzy-game approach to combat inherent channel uncertainties. Rather than the definite reward, we first project the decision space to another fuzzy-logical space and thereby characterize the varying uncertain information with the fuzzy numbers. Thus, the sensitiveness to random fluctuation in definite rewards would be alleviated. On this basis, we formulate DSA in uncertain channels as a centralized fuzzy payoffs game (FPG). We then develop a novel fuzzy-learning algorithm to achieve the optimal network throughput even in face of uncertain information, with which the network controller executes the decision making of sharing users by exploiting the fuzzy-logic method. Numerical results are finally provided to validate our new FPG scheme. Although uncertain environments render existing crisp-game approaches invalid, our new algorithm can converge after tens of iterations (even in fast-varying conditions), thereby permitting reliable shared accessing and improved throughput, which is of great significance to next- generation communications operated in dynamical and uncertain environments.

Molecular communications convey information via diffusion propagation. The inherent long-tail channel response causes severe inter-symbol interference, which may seriously degrade signal detection performances. Traditional linear signal detection techniques, unfortunately, require both high complexity and a high signal-to-noise (SNR) ratio to operate. In this paper, we proposed a new non-linear signal processing paradigm inspired by the biological systems that achieves low-complexity signal detection even in low SNR regimes. First, we introduce a stochastic resonance inspired non-linear filtering scheme for molecular communications, and show that it significantly improves the output SNR by transforming the noise energy into useful signals. Second, we design a novel non-coherent detector by exploiting the transient features of molecular signaling, which are independent of channel response and involves only lowcomplexity linear summation operations. Numerical simulations show that this new scheme can improve the detection performance remarkably (approx. 7dB gain), even when compared against linearly optimal coherent methods. This is one of the first attempts to demodulate molecular signals from an entirely biological point of view, and the designed non-linear noncoherent paradigm will provide significant potential to the design and future implementation of nano-systems in noisy biological environments.

We investigate the global throughput maximization of distributed spectrum reusing (DSR) in cognitive radio (CR) network, which reaches the two-dimensional spectrum multiplexing. Most previous works only consider the temporal-domain accessing, which greatly underutilize the spectrum resources. In this paper, we propose a new temporal-spatial spectrum reusing scheme by fully exploiting the location information of devices, where multiple users can access one channel simultaneously. In distributed applications, the global information will be unavailable, and therefore a non-cooperative game is formulated. It is proved as an exact potential game (EPG), which has at least one pure strategy Nash equilibrium (NE). Then, an improved decentralized reinforcement learning (RL) algorithm is developed to achieve the NE points. The network performance is evaluated by computer simulations.

Molecular communications via diffusion (MCvD) represents a relatively new area of wireless data transfer with especially attractive characteristics for nanoscale applications. Due to the nature of diffusive propagation, one of the key challenges is to mitigate inter-symbol interference (ISI) that results from the long tail of channel response. Traditional coherent detectors rely on accurate channel estimations and incur a high computational complexity. Both of these constraints make coherent detection unrealistic for MCvD systems. In this paper, we propose a low-complexity and noncoherent signal detector, which exploits essentially the local convexity of the diffusive channel response. A threshold estimation mechanism is proposed to detect signals blindly, which can also adapt to channel variations. Compared to other noncoherent detectors, the proposed algorithm is capable of operating at high data rates and suppressing ISI from a large number of previous symbols. Numerical results demonstrate that not only is the ISI effectively suppressed, but the complexity is also reduced by only requiring summation operations. As a result, the proposed noncoherent scheme will provide the necessary potential to low-complexity molecular communications, especially for nanoscale applications with a limited computation and energy budget.

In this paper, a new detection framework, namely, deep sensing (DS), is proposed for 5G spectrum sharing, which is designed to proactively recover some informative states associated with realistic cognitive links (e.g., fading gains), except for detecting the occupancy of primary-band. Relying on a dynamic state-space approach, a unified mathematical model is formulated. The Bernoulli random finite set (BRFS) is exploited to theoretically characterize the complex DS procedures. A Bernoulli filter algorithm is suggested to recursively estimate unknown PU states accompanying related link information, which is further implemented by particle filtering. The proposed DS algorithm is applied to detect primary users over more challenging time-varying fading channels. Numerical simulations validate the new scheme. Spectrum sensing can be effectively implemented by estimating time-varying fading gains jointly.

Spectrum sensing, as one of the most important aspects, plays a crucial role on mitigating interference of secondary users (SU) in cognitive radio. However, moving primary user (PU) will sharply decrease the stability of observable information by considerably deteriorating the sensing performance. In this paper, a new joint estimating scheme is proposed for tracking PU proactively and detecting the occupation of primary band meanwhile. In view of both PU's state and its location, the united mathematical model based dynamic state-space model (DSM) is established in the new scheme. On this basis, a Bernoulli filter algorithm is suggested to jointly estimate the PU's state and its location recursively, which is further implemented by Particle filtering. Furthermore, an adaptive horizon expanding method is subtly designed to deal with loss of tracking resulting from the intermittent disappearance of PU's state. Experimental simulations demonstrate that, aiming at mobile PU, sensing performance of the new scheme is apparently better than other traditional methods, and the estimated PU's location may be further utilized by resource allocation of cognitive network.

Interference alignment (IA) is a novel technique to achieve the optimal degree of freedom of wireless communication systems through efficient interference management. In a large size multi‐cell network, IA over a full connected model requires the impractical number of transmit/receive antennas. Moreover, extensive channel state information is delivered over the backhaul between different base‐stations. For realistic scenarios with limited quantity of transceiver antennas, such a full IA scheme may even become infeasible. In this study, by exploiting the heterogeneous path losses, the authors propose a novel partial IA scheme to enhance the throughput of multi‐cell networks, which requires relatively small amounts of antennas and hence can be practically implemented. They first formulate the partial IA problem in terms of a mixed integer bi‐level non‐linear optimal program. Then, they decompose the problem into two sub‐problems to reduce the computation complexity and, furthermore, introduce two algorithms for interference links selection. It is shown that, in a 19 hexagonal wrap‐around‐cell layout, their proposed algorithm outperforms a standard multi‐user multi‐input–multi‐output technique with far less transmit antennas. The present scheme is therefore of great promise to practical applications.

Spectrum sensing based dynamic spectrum sharing is one of the key innovative techniques in future 5G communications. When realistic mobile scenarios are concerned, the location of primary user (PU) is of great significance to reliable spectrum detections and cognitive network enhancements. Given the dynamic disappearance of its emission signals, the passive locations tracking of PU, nevertheless, remains dramatically different from existing positioning problems. In this investigation, a new joint estimation paradigm, namely deep sensing, is proposed for such challenging spectrum and location awareness applications. A major advantage of this new sensing scheme is that the mutual interruption between the two unknown quantities is fully considered and, therefore, the PU's emission state is identified by estimating its moving positions jointly. Taking both PU's unknown states and its evolving positions into account, a unified mathematical model is formulated relying on a dynamic state-space approach. To implement the new sensing framework, a random finite set (RFS) based Bernoulli filtering algorithm is then suggested to recursively estimate unknown PU states accompanying its time-varying locations. Meanwhile, the sequential importance sampling is used to approximate intractable posterior densities numerically. Furthermore, an adaptive horizon expanding mechanism is specially designed to avoid the mis-tracking aroused by the intermittent disappearance of PU. Experimental simulations demonstrate that, even with mobile PUs, spectrum sensing can be realized effectively by tracking its locations incessantly. The location information, as an extra gift, may be utilized by cognitive performance optimizations.

In this paper, spectrum sensing is investigated and a new detection framework, namely, deep sensing (DS), is proposed for more challenging scenarios of future dynamic spectrum sharing. In contrast to existing methods, the DS scheme is designed to proactively recover and exploit some other informative states associated with realistic cognitive links (e.g., fading gains), except detecting the occupancy of primary-band. A unified mathematical model, relying on the dynamic state-space approach, is formulated, in which the Bernoulli random finite set (RFS) is further exploited to theoretically characterize complex DS procedures. A Bernoulli filter algorithm is suggested to recursively estimate unknown PU states accompanying related link information, which is implemented by particle filtering based on numerical approximations. The proposed DS algorithm is applied to detect primary users under time-varying fading channel, which may increase the observation uncertainty and, therefore, deteriorate the sensing performance. With this new framework, the time-varying fading gain, modeled as a stochastic discrete-state Markov chain (DSMC), is estimated along with unknown PU states. Simulations demonstrate that, by exploiting the underlying dynamic fading property, the sensing performance will surpass other traditional schemes. The DS scheme may be conveniently generalized to other applications, which will promote sensing performance and provides a new paradigm for next-generation spectrum sharing.

Cluster identification of ultra-wideband (UWB) propagations is of great significance to the parameter extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e., the decaying amplitude and the time of arrivals, are organically combined and fully explored in the suggested cluster identification algorithm. Each resolvable trajectory component is first projected onto a 2-D amplitude-time plane and further modeled as a virtual ant-agent, which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameter configuration is analyzed, and a group of numerical performance metrics is derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses can be accurately extracted.

For the emerging 5G millimeter-wave communications, the nonlinearity is inevitable due to RF power amplifiers of the enormous bandwidth operating in extremely high frequency, which, in collusion with frequency-selective propagations, may pose great challenges to signal detections. In contrast to classical schemes, which calibrate nonlinear distortions in transmitters, we suggest a nonlinear equalization algorithm, with which the multipath channel and unknown symbols contaminated by nonlinear distortions and multipath interferences are estimated in receiver-ends. Attributed to the nonlinearity and marginal integration, the involved posterior density is analytically intractable and, unfortunately, most existing linear equalization schemes may become invalid. To solve this problem, the Monte-Carlo sequential importance sampling based particle filtering is suggested, and the non-analytical distribution is approximated numerically by a group of random measures with the evolving probability-mass. By applying the Taylor's series expansion technique, a local-linearization observation model is further constructed to facilitate the practical design of a sequential detector. Thus, the unknown symbols are detected recursively as new observations arrive. Simulation results validate the proposed joint detection scheme. By excluding transmitting pre-distortion of high complexity, the presented algorithm is specially designed for the receiver-end, which provides a promising framework to nonlinear equalization and signal detection in millimeter-wave communications.

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For the emerging 60GHz millimeter-wave communications, the nonlinearity is usually inevitable due to RF power amplifiers operating in the ultra-high frequency and enormous bandwidth, which, in collusion with frequency-selective propagations, poses great challenges to signal detections. In contrast to classical schemes calibrating nonlinear distortions in transmitters, a blind detection algorithm is presented in this investigation, with which both the multipath response and symbols contaminated by nonlinear distortions and multipath interferences are estimated in receiver-ends. The Monte-Carlo sequential importance sampling based particle filtering is used, and the non-analytical distribution is approximated numerically by a group of random measures with evolving weights. By applying the Taylor's series expansion techniques, a local linearization model is further constructed to facilitate the practical design of a sequential detector. Simulation results validate the proposed blind detection scheme. By excluding the transmitting predistorter with complex computations and implementations, the presented algorithm provides a promising signal detection framework in 60GHz systems.

As the application scope of cognitive radio grows continuously, time‐variant flat fading (TVFF) channels become common in practical spectrum sensing scenarios. Unfortunately, most existing spectrum sensing methods which are designed for time‐invariant propagation channels could hardly obtain good performance when they operate in realistic TVFF channels. To combat this difficulty, in this investigation the authors design a promising spectrum sensing method. Firstly, a novel dynamic state‐space model is proposed in which a two‐state Markov chain is employed to abstract the evolution of primary user states and a finite‐state Markov channel model is utilised to characterise the TVFF channel. Secondly, based on the maximum a posteriori probability criteria and the particle filtering mechanic, a joint estimation algorithm of the time‐dependent fading channel gain and the state of primary user is presented. Experimental simulations verify the performance superiority of the authors presented joint detection scheme, which could be properly applied to spectrum sensing in realistic TVFF channels.

Cooperative spectrum sensing can overcome hidden terminal and shadowed fading, a novel strategy of improving the effectiveness of cooperative sensing, which is inspired by cross‐correlation matrix (CCM) estimation and a linear weighted algorithm, is presented in this study. Namely, the values of channels gain ratio and signal‐to‐noise ratio are estimated by means of the CCM which is composed of the sampled signals and, therefore, the selection of sensing nodes is achieved by linearly weighting the spectrum sensing data. The subsequent data fusion strategy includes two modes, that is, the full fusion mode in which all sensing nodes data are linearly weighted, and the partial fusion mode where only part of sensing nodes data are linearly weighted. A major advantage of this scheme is that these two modes are realised by only exploiting the sampled signals of sensing nodes without any prior knowledge of primary user. Simulations verify the effectiveness of the proposed CCM estimation and the data fusion strategy.

Most of the existing spectrum sensing schemes utilize only the statistical property of fading channels, which unfortunately fails to cope with the time-varying fading channel that has disastrous effects on sensing performance. As a consequence, such sensing schemes may not be applicable to distributed cognitive radio networks. In this paper, we develop a promising spectrum sensing algorithm for time-variant flat-fading (TVFF) channels. We first formulate a dynamic state-space model (DSM) to characterize the evolution behaviors of two hidden states, i.e., the primary user (PU) state and the fading gain, by utilizing a two-state Markov process and another finite-state Markov chain, respectively. The summed energy, which serves as the observation of DSM, is employed for the ease of implementation. Relying on a Bayesian statistical inference framework, the sequential importance sampling based particle filtering is then exploited to numerically and recursively estimate the involved posterior probability, and thus, the PU state and the fading gain are jointly estimated in time. The estimations of two states are soft-outputs, which are successively refined with a designed iterative approach. Simulation results demonstrate that the new scheme can significantly improve the sensing performance in TVFF channels, which, in turn, provides particular promise to realistic applications.

ABSTRACT This article focuses on improving the system capacity of 60‐GHz wireless personal area networks (WPANs) and presents an effective time slot allocation scheme for spatial reuse, which combines the existing exclusive region (ER)‐based scheduling algorithm with simple power control. In the study, the concept of ER for concurrent transmissions is introduced first. Considering the large path loss in the millimeter‐wave band and the characteristics of typical indoor multipath channels, we generalize the signal propagation model as well as ER radius for 60‐GHz WPANs. Then, we propose an improved ER‐based scheduling algorithm, which employs power control instead of constant transmitting power. In this algorithm, devices are assumed to receive data at the receiver sensitivity, which refers to the minimum receiving power to ensure the reliability for communications. Implementation feasibility is provided within the current standards framework. Some more practical analysis based on the beamforming codebook specified by of the IEEE 802.15.3c standard is also developed. In addition, the impact of the antenna radiation efficiency and receiver sensitivity is discussed. Computer simulations validate the excellent performance of the proposed scheme, which can significantly improve the channel capacity under different antenna configurations. Copyright © 2012 John Wiley & Sons, Ltd.

The traditional spectrum sensing schemes can only utilize the statistical probability of fading channels, which may fail to deal with the time-varying fading gain. Thus, the performance of such sensing techniques will degrade dramatically and may even become inapplicable to distributed cognitive radio networks. In this investigation, we develop a promising spectrum sensing algorithm for time-variant flat-fading (TVFF) channels. Firstly, a promising dynamic state-space model (DSM) is established to thoroughly characterize the evolution behaviors of both primary user (PU) state and fading channels, by utilizing a two-state Markov process and the finite-states Markov chain (FSMC), respectively. Relying on an optimal Bayesian inference framework, the sequential importance sampling based particle filtering is then suggested to recursively estimate PUs state and fading gain jointly. Experimental simulations demonstrated that the new scheme can significantly improve the sensing performance in TVFF channels, which provides particular promise to realistic applications.

In this paper, we investigate the cluster identification of ultra-wideband (UWB) multipath propagations from a promising biological processing perspective. In the presented biological cluster extraction method, both the amplitude decay and time of arrival of UWB channel impulse response (CIR) are fully taken into considerations. Each resolvable multipath component is projected onto a two dimensional amplitude-time workspace, and then modeled as a virtual ant-agent. Thus, these ant-agents can move around in this 2-D space with a preference to the high local environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identification can be elegantly realized by the biological ant colony clustering (ACC) procedure. As the experimental simulations shown, the suggested algorithm can accurately and efficiently identify the involved multiple clusters in a completely automatic manner, which is of great importance to UWB channel modeling and parameters extractions.

In this article, we suggest an efficient beam switching technique for the emerging 60GHz wireless personal area networks. Given the pre-specified beam codebooks, the beam switching process, aiming to identify the best beam-pair for data transmissions, is formulated as a global optimization problem in a two-dimension plane that is formed by the potential beam pattern index. As the analytical gradient information of the objective reward function is practically unavailable, Rosenbrock numerical algorithm is properly adopted to implement beam searching, by implicitly approaching and exploiting the gradient descent direction through the numerical pattern-search mechanic. In order to enhance search performance, furthermore, a novel initialization process is presented to provide the feasible initial solution for Rosenbrock search. Inspired by the appealing conception of small-region dividing and conquering, this pre-search algorithm can efficiently reduce the search scope and hence improve the success probability. The developed beam switching technique, i.e. an initialization process followed by Rosenbrock search, exhibits a much lower complexity than the current state-of-the-art strategies. It is demonstrated from both theoretical analysis and numerical experiments that, compared with the existing popular methods, the required protocol overhead of the new beam-training procedure can be significantly reduced, accompanying the power consumption of 60GHz devices.

This paper presents a statistical characterization of Ultra-Wideband (UWB) channel for ship cabin environment based on measurements carried out in the frequency-domain and covered the frequency band of 6-9GHz. According to the results of measurement, it is observed that rays arrive in clusters, so we describe the multipath propagation with the classical S-V model. The main purpose is to study multipath propagation behavior, particularly the phenomenon of clustered multipath components. An efficient cluster automatic identification algorithm based on wavelet analysis is employed for clusters identification of the discrete UWB channel impulse responses (CIRs). Then, the statistical characterizations of multi-path components (MPCs) are examined. Finally, simulation results of the proposed statistical model are compared to measured data demonstrating reasonable agreement. The results are helpful to model the UWB propagation channel in such environment.

Modern communication engineering has brought forward impractical requirements on powerful computation engines as well as simple implementations. Apparently, the two aspects are contradicted in most realistic applications. Because of the dispersive multipath propagation in underwater acoustic channels, traditional coherent and adaptive receivers are computationally intensive and, hence, inapplicable to the large-scale underwater sensor networks. Inspired by quantum computing and nature intelligence that are incorporated with the concept of culture evolution, in this paper, we suggest a novel quantum memetic algorithm (QMA) built with more qualified problem-solving ability. Instead of classical gene representations, the quantum bit structure is employed by chromosomes to enhance the population diversity of genetic searching. The quantum gate rotating is then explored to update chromosomes in an efficiently parallel way. As a hybridization strategy, quantum-rotation-based local search is integrated in the lifetime learning to further refine individuals' performance and accelerate their convergence toward the global optimality. As a significant real-world application, we develop a noncoherent underwater signal receiver that is based on a QMA framework. From a pattern recognition aspect, the suggested detection scheme includes two sequential phases: Features extraction and pattern classification. Finally, the highly computational optimization problem is elegantly addressed by QMA. Providing favorable robustness to various parameter configurations, QMA can considerably reinforce the search performance and improve the underwater signal detection. It is demonstrated from numerical experiments that QMA is much superior to genetic algorithm (GA) in this high-dimensional optimization. Meanwhile, QMA shows remarkable advantages in search performance, even to the current state-of-the-art quantum-inspired GA and memetic algorithm.