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Millimeter-wave rotary-wing (RW) unmanned aerial vehicle (UAV) air-to-ground (A2G) links face unpredictable Doppler effect arising from the inevitable wobbling of RW UAV. Moreover, the time-varying channel characteristics during transmission lead to inaccurate channel estimation, which in turn results in the deteriorated bit error probability performance of the UAV A2G link. This paper studies the impact of mechanical wobbling on the Doppler effect of the millimeter-wave wireless channel between a hovering RW UAV and a ground node. Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link. Numerical results show that different RW UAV wobbling patterns impact the amplitude and the frequency of ACF oscillation in the millimeter-wave RW UAV A2G link. For UAV wobbling, the temporal ACF decreases quickly and the impact of the Doppler effect is significant on the millimeter-wave A2G link. [ABSTRACT FROM AUTHOR]

The use of antenna arrays is indispensable in radar and multiple-input multiple-output (MIMO) wireless communications areas, as it permits the treatment of wavefronts in the spatial domain, enabling important applications such as beamforming and target detection. Spatial information is commonly given in terms of spatial correlation functions (SCFs), but its representation in terms of angular domain, such as the power azimuth spectrums (PASs), is sometimes more convenient. Therefore, transforming from SCF to PAS, and vice versa, is often required. This is commonly done by employing so-called beamformers, where the Bartlett beamformer (BB) is the most commonly utilized. However, the use of beamformers alone is not enough for retrieving PAS information from a given SCF. Particularly, this paper shows that a non-linear operation is missing in relating SCFs to PASs as provided by current beamformers. The paper also shows the mathematical expressions that correctly relate SCFs to PASs and wavenumber spectrums (WNSs) for the general case of systems with antenna arrays. The developed theory results in two new beamformers, which, corroborated by simulation results, solve any previous inconsistency in estimating PASs from SCFs. [ABSTRACT FROM AUTHOR]

In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users’ channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis. [ABSTRACT FROM AUTHOR]

In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of an intelligent omni-surface (IOS) assisted downlink non-orthogonal multiple access (NOMA) network with phase quantization errors and channel estimation errors, where the channels related to the IOS are spatially correlated. First, upper bounds on the average achievable rates of the two users are derived. Then, channel hardening is shown to occur in the proposed system, based on which we derive approximations of the average achievable rates of the two users. The analytical results illustrate that the proposed upper bound and approximation on the average achievable rates are asymptotically equivalent in the number of elements. Furthermore, it is proved that the asymptotic equivalence also holds for the average achievable rates with correlated and uncorrelated channels. Additionally, we extend the analysis by evaluating the average achievable rates for IOS assisted orthogonal multiple access (OMA) and IOS assisted multi-user NOMA scenarios. Simulation results corroborate the theoretical analysis and demonstrate that: i) low-precision elements with only two-bit phase adjustment can achieve the performance close to the ideal continuous phase shifting scheme; ii) The average achievable rates with correlated channels and uncorrelated channels are asymptotically equivalent in the number of elements; iii) IOS-assisted NOMA does not always perform better than OMA due to the reconfigurability of IOS in different time slots. [ABSTRACT FROM AUTHOR]

Channel estimation is a challenging issue for millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) in the future sixth generation (6G) wireless systems, where the conventional estimation schemes may fail to track the fast varying channels, especially in high-speed mobile scenarios. In this paper, a novel tensor-based uplink channel estimation scheme is proposed for multi-user MIMO (MU-MIMO) systems over time-varying channels. In the proposed scheme, a low-overhead pilot transmission scheme is designed to track the varying channel. The received uplink signal at the base station (BS) is formulated as a third-order tensor which admits a CANDECOMP/PARAFAC (CP) model. The CP decomposition issue is then solved using blind matrix decomposition, in which the special structures of signals in the time dimension and the matrix subspace are utilized. By exploiting low-rank structure of the signal tensor, the channel parameters (angles of arrival/departure, path gains, and Doppler shifts) are estimated from the factor matrices. Moreover, the proposed scheme is theoretically analyzed and it is guaranteed with low pilot overhead. Simulation results verify that the proposed scheme can outperform the compressed sensing (CS) based scheme and the iteration-based scheme in terms of accuracy and stability. The uniqueness of the proposed scheme is also verified in our simulation. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) frequency spectrum offers orders of magnitude greater spectrum to mitigate the severe spectrum shortage in conventional cellular bands. To overcome the high propagation loss in the mmWave band, massive multiple-input and multiple-output (MIMO) can be adopted at both transmitter and receiver to provide large beamforming gains. At the same time, hybrid architecture is applied to reduce the huge power consumption caused by devices operating at radio frequency (RF). However, because of the hybrid architecture and large number of antennas, it is hard to obtain the channel state information (CSI) which is crucial for obtaining desirable beamforming gains. Off-grid error and sparsity pattern (SP) estimation error are two main limiting factors of the performance of most existing compressive sensing (CS) based channel estimation (CE) algorithms. Off-grid error presents when the true angle does not lie on the discretized angle grid of mmWave channel in the spatial domain. In this paper, we first propose a fast Bayesian matching pursuit method with ‘virtual sparsity’ to improve the accuracy of SP estimation and name it as the improved Bayesian matching pursuit (IBMP). Then an enhanced algorithm, named off-grid IBMP (OG-IBMP), is developed to mitigate the off-grid problem, followed by a theoretical analysis of OG-IBMP. This method iteratively updates the selected grid points and updates the corresponding parameters based on the maximum a posteriori (MAP) criterion. Numerical simulations are performed to validate our theoretical analysis and evaluate the performance of the proposed method. Compared to other existing methods, the results show that our proposed OG-IBMP algorithm greatly reduces the off-grid error and significantly enhances the accuracy of the SP estimation with low computational complexity. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is a passive antenna array composed of a large number of reflecting elements. In a RIS-assisted communication system, it is a challenging task to acquire accurate channel state information (CSI). In this paper, we propose a semi-blind channel estimation method for a RIS-assisted massive multiple-input single-output (MISO) system. Assuming a Gaussian priori on the data symbols, an iterative expectation maximization (EM)-based algorithm is developed to obtain the maximum likelihood (ML) estimate of the cascaded RIS channel. Different from existing pilot-based methods, the proposed semi-blind channel estimation method exploits the data symbols for channel estimation enhancement and only a fraction of full-pilot signaling overhead is required. Simulation results verify that the proposed method achieves significant improvement in the accuracy of the channel estimation while it noticeably reduces the training pilot overhead. [ABSTRACT FROM AUTHOR]

The efficiency of link adaptation in wireless communications relies greatly on the accuracy of channel knowledge and transmission mode selection. In this paper, a novel deep learning based link adaptation framework is proposed for the orthogonal frequency-division multiplexing (OFDM) systems with compressed-sensing-assisted index modulation, termed as OFDM-CSIM, communicating over millimeter-wave (mmWave) channels. To achieve link adaptation, a novel multi-layer sparse Bayesian learning (SBL) algorithm is proposed for accurately and instantaneously providing the required channel state information. Meanwhile, a deep neural networks (DNN)-assisted adaptive modulation algorithm is proposed to choose the best possible transmission mode to maximize the achievable throughput. Simulation results show that the proposed multi-layer SBL algorithm enables more accurate channel estimation than the conventional techniques. The DNN-based adaptive modulator is capable of achieving a higher throughput than the learning-assisted solution based on the $k$ nearest neighbor ($k$ -NN) algorithm, and also the classic average signal-to-noise ratio (SNR)-based solutions. Moreover, analysis shows that both the multi-layer SBL algorithm and the DNN-assisted adaptive modulator achieve better performance than their respective conventional counterparts while at a significantly lower computational complexity cost. [ABSTRACT FROM AUTHOR]

Due to the combined effects of different acoustic propagation, reflection patterns in different time-scale, time-varying underwater acoustic (UWA) channel generally exhibits hybrid sparsity, i.e., contains not only rapidly time-varying elements, but also stationary or slowly time-varying ones. While the classic sparse reconstruction algorithms such as orthogonal matching pursuit (OMP) generally do not take it into account, the existence of static multipath will unavoidably hinder the tracking of rapid time variations. In this paper, a sequential adaptive observation length orthogonal matching pursuit (SAOLOMP) approach is proposed to sequentially explore rapidly time-varying sparsity in a two-step sequential manner. Specifically, the static components are firstly separated via simultaneous orthogonal matching pursuit (SOMP) among continuous measurements. Upon the residual error of the SOMP, the measurement-wise OMP is applied for estimating the remained dynamic components. As the variation of the SOMP residual error indicates how rapidly the remained dynamic multipath components vary, the observation length of the OMP is adjusted according to the gradient of residual error to adapt to rapidly time-varying components. Finally, both types of sparse components are summed up to obtain the whole channel response. Numerical simulations as well as experimental results from field UWA communication data show that the proposed algorithm exhibits better performance in exploiting rapidly time-varying sparsity than the state-of-the-art compressive methods do under UWA channel. [ABSTRACT FROM AUTHOR]

In connected vehicular networks, it is vital to have vehicular nodes that are capable of sensing surrounding environments and exchanging messages with each other for automating and coordinating purposes. Towards this end, integrated sensing and communication (ISAC), combining both sensing and communication systems to jointly utilize their resources and pursue mutual benefits, emerges as a new cost-effective solution. In ISAC, the hardware and spectrum co-sharing leads to a fundamental tradeoff between sensing and communication performance, which is not well understood except for very simple cases with the same sensing and channel states, and perfect channel state information at the receiver (CSIR). In this paper, a general point-to-point ISAC model is proposed to account for the scenarios where the sensing state is different from but correlated with the channel state, and the CSIR is not necessarily perfect. For the model considered, the optimal tradeoff is characterized by a capacity-distortion function that quantifies the best communication rate for a given sensing distortion constraint requirement. An iterative algorithm is proposed to compute such tradeoff, and a few non-trivial examples are constructed to demonstrate the benefits of ISAC as compared to the separation-based approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a reference-free artificial noise (AN) waveform design and cancellation scheme for single-input-single-output (SISO) wireless secure transmission. First, a system structure with the aid of AN is presented, where we provide a specific method extracting the sign information of each symbol from the desired signal (DS) to form AN at the transmitter. Then, we notice that the legitimate receiver enable to reconstruct the AN from the received signal by almost the same method as the transmitter without AN reference, which reduces the system complexity in practice. Subsequently, the AN cancellation performance and the secrecy capacity at the receiver are analyzed, and their upper bounds are also derived. Finally, simulation results confirm that the proposed reference-free AN design and cancellation scheme can effectively suppress the AN at legitimate receiver, while degrades the signal to noise ratio (SNR) at eavesdropper seriously, which provides impressive secure performance in terms of secrecy capacity. [ABSTRACT FROM AUTHOR]

Recently, research on the design of relay structure and optimal precoding matrix in massive multiple-input multiple-output (MIMO) relay systems has been proposed in many works assuming that partial or all of the channel state information (CSI) is known. However, in the practical communication systems, the CSI is unknown and needs to be estimated. In particular, channel estimation is more difficult when the relay adopts a hybrid precoding structure. In this paper, we propose an efficient channel estimation scheme based on Tucker-2 tensor model for a multi-way massive MIMO hybrid relay system. The proposed algorithm provides all the CSI involved in the communication links at each user end. Compared with traditional channel estimation algorithms, the proposed algorithm has better estimation performance and requires few iterations to achieve convergence. In addition, we derive the analytical expression of the Cramér-Rao lower bound (CRB) for reference. Simulation results verify the effectiveness of the proposed Tucker-2-based channel estimation approach. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) and cell-free (CF) massive multiple-input multiple-output (MIMO) are two promising technologies for realizing beyond-fifth generation (5G) networks. In this paper, we study the uplink spectral efficiency (SE) of a practical spatially correlated RISs-aided CF massive MIMO system over Rician fading channels. Specifically, we derive the closed-form expression for characterizing the uplink SE of the system, which shows that increasing the number of RIS elements can improve the system performance. Moreover, the results unveil that the spatial correlation of RIS has a significant impact on the system while leading to the best performance gain at a half wavelength spacing. Finally, the accuracy of our analytical results are verified by Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme. [ABSTRACT FROM AUTHOR]

Although LoRa modulation can achieve low-power and long-range transmission, its performance is significantly affected by fading channels. On the other hand, its low data rate limits the capacity. To mitigate these disadvantages, a new reconfigurable intelligent surface (RIS) assisted LoRa uplink system is proposed in this paper, where the reflection coefficients of the RIS are generated by utilizing an $M$ -ary differential phase shift keying modulator. Moreover, a low-complexity non-coherent receiver is designed for the proposed system. The new system is able to inherit the non-coherent detection of the conventional LoRa system and also transmit additional information of $\log _{2}{M}$ bits while reducing the impact of the fading channels. Furthermore, the bit-error-rate, symbol-error-rate and throughput expressions of the system are derived in closed form. Theoretical and simulated results show that the system has better performance than the conventional LoRa system and single-relay LoRa system. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) and millimeter wave (mmWave) communications are two major technologies to enhance the spectrum efficiency (SE). Considering that the SE improvement in the CRNs is limited due to the interference temperature imposed on the primary user (PU), and the severe path loss and high directivity in mmWave communications make it vulnerable to blockage events, we introduce an intelligent reflecting surface (IRS) into mmWave CRNs. Due to the estimation mismatch and the passivity of Eavesdroppers (Eves), perfect channel state information (CSI) of wiretap links is challenging to obtain, which promotes our research on robust secure beamforming (BF) design in the IRS-assisted mmWave CRNs. This paper considers the collaborate scenario of Eves, which allows us to investigate the BF design in the harsh eavesdropping environment. Specifically, by using a uniform linear array (ULA) at the cognitive base station (CBS) and a uniform planar array (UPA) at the IRS, and supposing that imperfect CSIs of angle-of-departures for wiretap links are known, we formulate a constrained problem to maximize the worst-case achievable secrecy rate (ASR) of the secondary user (SU) by jointly designing the transmit BF at the CBS and reflect BF at the IRS. To solve the non-convex problem with coupled variables, an efficient alternating optimization algorithm is proposed. As for the transmit BF at the CBS, we propose a heuristic robust transmit BF algorithm to attain the BF vectors analytically. As for the reflect BF at the IRS, by means of an auxiliary variable, we transform the non-convex problem into a semi-definite programming problem with rank-1 constraint, which is handled with the help of an iterative penalty function, and then obtain the optimal reflect BF through CVX. Finally, simulation results indicate that the ASR performance of our proposed algorithm has a small gap with that of the optimal solution with perfect CSI compared with the other benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a two-hop simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system with an energy-limited relay node. The relay node harvests energy based on the radio-frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol and fully uses the harvested energy to precode and forward the information to the destination node. The non-linear energy harvesting models are considered at the relay node. With the consideration of the channel estimation error, the joint optimization of the TS factor, source and relay precoding matrices is proposed with robustness against the channel state information (CSI) mismatch to maximize the mutual information (MI) between the source and destination nodes. The optimal structure for the source and relay precoding matrices is derived to simplify the transceiver optimization problem. Numerical simulations show that the system performance provided by the proposed algorithms with robustness is better than the non-robust algorithm. [ABSTRACT FROM AUTHOR]

To extend the transmission range governed by limited transmit power, collaborative beamforming (CB) can be employed with a virtual antenna array (VAA) constructed by an unmanned aerial vehicle (UAV) swarm. CB techniques combined with physical layer security (PLS) algorithms are known to better secure transmissions of sensitive information in the presence of eavesdropping attacks, compared to co-located antenna-based schemes. In particular, analog collaborative beamforming (ACB) with random VAA topologies, which is suitable for UAV networks due to its lower signaling/channel estimation overhead compared to the digital CB, can provide a significantly higher secrecy rate by creating noise-like signals at eavesdroppers. However, the increase in energy consumption to improve secrecy rate had been ignored in the existing ACB-based PLS schemes, which is of paramount importance considering the limited onboard energy of UAVs. Thus, motivated by such limitation, in this paper, we examine the security energy efficiency of the ACB-based PLS technique, where a group of UAVs (or VAA elements) randomly change their locations in a distributed manner to randomize the array factor of the VAA, i.e., a stochastic VAA. The proposed stochastic VAA outperforms the existing static VAA in terms of secrecy energy efficiency as verified by analytic and numerical results. Further, the impact of phase errors of the proposed scheme is also presented, which indicates the significance of the accurate phase estimation. [ABSTRACT FROM AUTHOR]

In multi-cell massive multiple-input multiple-output (MIMO) systems, channel estimation is deteriorated by pilot contamination and the effects of pilot contamination become more severe due to hardware impairments. In this paper, we propose a joint pilot design and channel estimation based on deep residual learning in order to mitigate the effects of pilot contamination under the consideration of hardware impairments. We first investigate a conventional linear minimum mean square error (LMMSE) based channel estimator to suppress the interference caused by pilot contamination. After that, a deep learning based pilot design is proposed to minimize the mean square error (MSE) of LMMSE channel estimation, which is utilized to the joint pilot design and channel estimator for transfer learning approach. For the channel estimator, we use a deep residual learning which extracts the features of interference caused by pilot contamination and eliminates them to estimate the channel information. Simulation results demonstrate that the proposed joint pilot design and channel estimation method can effectively reduce the effect of pilot contamination as well as outperforms the conventional approach in multi-cell massive MIMO scenarios. Moreover, the joint pilot design and channel estimation method using transfer learning further enhances the estimation performance when the prior knowledge of pilot contamination cannot be exploited. [ABSTRACT FROM AUTHOR]

The terahertz (THz) waves with enormous bandwidth can be used along with the existing sub-6GHz and millimeter wave (mmWave) bands to achieve the ever evolving ecosystem of applications that need to be supported by the modern wireless networks. This paper investigates a user-centric dynamic base station (BS) clustering design for a hybrid network where THz, mmWave, and sub-6GHz BSs coexist. Invoking the proposed clustering model, the BS cooperation within each band is made possible by considering long term channel variations and all the surrounding BSs within a cluster with tunable size. A typical user is associated with the best BS cluster, from either a sub-6GHz, mmWave or THz tier based on the maximum signal-to-interference-plus-noise-ratio (SINR) criterion or the maximum rate criterion. Using tools from stochastic geometry, we assess the performance of the proposed user-centric hybrid system in terms of SINR and rate coverage performances, while accounting for: band specific propagation models, directional beamfroming, and BSs random locations. The accuracy of the analytical results is validated with Monte-Carlo simulations. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz BSs improves the rate significantly but may degrade the coverage performance. Thus, with carefully planned networks, enabling user-centric BS cooperation for hybrid wireless systems can achieve ultra-high rates while maintaining sufficient coverage in sixth-generation (6G) networks. [ABSTRACT FROM AUTHOR]

Partially impaired antenna array is one of the challenges for estimating the accurate directions of the signals impinging upon antenna arrays. It causes random loss of the signal information, resulting in the poor direction of arrival (DOA) estimation performance. In this paper, we propose an efficient DOA estimation method under partially impaired antenna array elements, which work randomly. We first devise a strategy to detect the positions of randomly lost signal information corresponding to partially impaired antenna array elements and then formulate the problem of received signal recovery as low rank matrix completion. Afterwards, we utilize the recursive least squares (RLS) with the nulling antenna array for DOA estimation due to its fast convergence and low mean square error properties and place the nulls in the directions of received signals by adjusting the weight vectors adaptively. The reciprocal of the antenna array defines the output spatial spectrum, and the directions of the signals are estimated by finding the high peaks. We also investigate the mean and mean square performance of the RLS after recovering and de-noising the received signal. Simulation results show that the proposed method converges to optimal solution fast and outperforms the existing DOA estimation methods. [ABSTRACT FROM AUTHOR]

Channel predictions are important to achieve high spectral efficiency for high-mobility vehicles. Channel extrapolation, used by many prediction methods, suffers from a limited prediction horizon in difficult radio environments. The predictor antenna (PA) concept provides the prediction horizons required for efficient transmission to fast-moving vehicles by measuring the channel ahead of time with an extra antenna placed on the vehicle. This paper presents a general framework that addresses the practical signal processing challenges of the PA concept. It is adaptable to a vast variety of vehicular deployment, mobility, and communication scenarios. A new theoretical prediction normalized mean-squared error (NMSE) expression is derived based on the presented framework. The framework is demonstrated by applying it to extensive channel measurements and comparing the PA predictions to Kalman-based channel predictions and outdated channel estimates. By studying the impact of vehicular velocity and radio environment on the prediction performance, it is shown that PA prediction is weaker at low velocities, where Kalman prediction methods are sufficient, but is uncontested at high velocities in environments without a dominating path. At high velocities in dominating path environments, the Kalman predictor provides usable predictions, but it is still outperformed by the PA predictions. [ABSTRACT FROM AUTHOR]

In time-varying fading channels, channel coefficients are estimated using pilot symbols that are transmitted every coherence interval. For channels with high Doppler spread, the rapid channel variations over time will require considerable bandwidth for pilot transmission, leading to poor throughput. In this paper, we propose a novel receiver architecture using deep recurrent neural networks (RNNs) that learns the channel variations and thereby reduces the number of pilot symbols required for channel estimation. Specifically, we design and train an RNN to learn the correlation in the time-varying channel and predict the channel coefficients into the future with good accuracy over a wide range of Dopplers and signal-to-noise ratios (SNR). The proposed training methodology enables accurate channel prediction through the use of techniques such as teacher-force training, early-stop, and reduction of learning rate on plateau. Also, the robustness of prediction for different Dopplers and SNRs is achieved by adapting the number of predictions into the future based on the Doppler and SNR. Numerical results show that good bit error performance is achieved by the proposed receiver in time-varying fading channels. We also propose a data decision driven receiver architecture using RNNs that further reduces the pilot overhead while maintaining good bit error performance. [ABSTRACT FROM AUTHOR]

In this paper, pilot-based carrier frequency offset (CFO) estimation for spectrally efficient frequency division multiplexing (SEFDM) system is focused in the downlink. Firstly, the designed objective function for CFO estimation is proved to fit parabolic curve approximately. Then, a low-complexity CFO estimator is proposed by computing only four trials. Moreover, the Cramer Rao bound (CRB) of CFO estimation for SEFDM is derived. Considering the non-orthogonality of SEFDM, the novel pilot sequence is proposed for SEFDM via minimizing the derived CRB. Under the effects of timing offset and multi-path, simulation results demonstrate that the proposed pilot sequence for SEFDM outperforms traditional sequences. [ABSTRACT FROM AUTHOR]

In this paper, we study non-orthogonal multiple access (NOMA) user scheduling and resource allocation problem for a generic downlink single-cell multiple input and single output (MISO) millimeter wave (mmWave) system. The larger number of packed antennas and the highly directional property of mmWave communications enable directional beamforming to achieve spatial diversity. Toward this end, we consider two different hybrid precoding schemes which are based on orthogonal matching pursuit (OMP). Users are assigned into different clusters and the base station (BS) transmits superposed signals that share the same precoding vector. Moreover, both fixed number of users per cluster and dynamic number of users per cluster are investigated. We aim to jointly optimize the user clustering, service scheduling, and power allocation strategy, in maximizing the proportional fairness (PF) among the users and exploring the multiuser diversity and multiplexing gain. Since the formulated joint user clustering, scheduling and power allocation problem is a mixed integer non-convex optimization problem, we propose a two-fold methodology. First, we apply a hybrid precoding and user clustering scheme, where the hybrid precoder is constructed by singular vector division (SVD) or minimum mean square error (MMSE). Then, with the obtained result, we approximate the proportional fairness power allocation problem by a sequence of Geometric Programming (GP) problems which are solved iteratively. The proposed scheme strikes a balance between the spectral efficiency and service fairness. Results show that the proposed MISO-NOMA scheme which is based on MMSE hybrid precoder and the proposed user scheduling and power allocation strategy under proportional fairness metric can outperform various conventional MISO schemes. Furthermore, our proposed dynamic number of users per cluster scheme outperforms the fixed scheme and can be considered as an upper bound in several aspects, including spectral efficiency and fairness. [ABSTRACT FROM AUTHOR]

In-band full duplex communication requires precise self-interference cancellation (SIC) to successfully decode the received desired signal. The existing neural network (NN) based SIC scheme uses offline trained NN to estimate the non-linear component of received self-interference (SI) over a static SI channel without additional NN training. For the time-varying SI channel, the NN-aided SIC method needs to retrain the NN during in-band full duplex communication to adapt time-varying channels. However, NN training is not fast enough to be done during full duplex communication. Thus, the SIC performance degrades. In this paper, we propose a digital SIC scheme using channel robust NN which takes estimates of linear SI channel coefficients as an input. It was found that the NN could learn the static part of the non-linear behavior of the SI channel well. Our solution can adapt the time-varying SI channel with only the channel coefficient estimation of linear components and a pre-trained NN. The proposed scheme can successfully reduce the SI to the noise floor for both time-invariant and time-varying SI channels. [ABSTRACT FROM AUTHOR]

Ultra-reliable low latency communication (URLLC) requires the packet error rate to be on the order of $10^{-9}$ - $10^{-5}$. Determining the appropriate transmission rate to satisfy this ultra-reliability constraint requires deriving the statistics of the channel in the ultra-reliable region and then incorporating these statistics into the rate selection. In this paper, we propose a framework for determining the rate selection for ultra-reliable communications based on the extreme value theory (EVT). We first model the wireless channel at URLLC by estimating the parameters of the generalized Pareto distribution (GPD) best fitting to the tail distribution of the received powers, i.e., the power values below a certain threshold. Then, we determine the maximum transmission rate by incorporating the Pareto distribution into the rate selection function. Finally, we validate the selected rate by computing the resulting error probability. Based on the data collected within the engine compartment of Fiat Linea, we demonstrate the superior performance of the proposed methodology in determining the maximum transmission rate compared to the traditional extrapolation-based approaches. [ABSTRACT FROM AUTHOR]

At millimeter-wave (mm-wave) frequencies, narrow beamwidth and inaccurate channel estimation result in beam alignment errors, which degrade the achievable signal-to-noise ratio (SNR) and rate. The extent of beam misalignment depends on the various link parameters, however, prior works ignored such a dependence and considered oversimplified array patterns. This paper formulates beam alignment errors for the uniform linear array at the base station as a function of the link parameters by using the Cramér Rao lower bound of angle-of-arrival estimates and proposes a tractable array pattern approximation to better reflect the effects of such alignment errors. Utilizing stochastic geometry and the statistics of the Student’s t-distribution, a novel and robust analytical technique is developed to obtain the downlink SNR and rate coverage probabilities due to beam misalignment, assuming a single antenna at the user. We show that beam alignment errors significantly affect the achievable SNR and rate for non line-of-sight (NLOS)/distant users whereas the effect is negligible for LOS/nearby users. It is also illustrated that the effects of alignment errors cannot be mitigated only by increasing the number of antennas. We also show that our proposed array pattern approximation better captures the effects of beam alignment errors as compared to the widely used flat-top pattern. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is considered as a multiple access technique for supporting massive number of devices with high spectral efficiency. Simultaneous wireless information and power transfer (SWIPT) is an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in networks employing cooperative NOMA (CNOMA) is investigated with imperfect channel state information (CSI). Performance of the CNOMA network with SWIPT is investigated in a multi-tier downlink scenario with macro-base stations (MBSs) and femto-base stations (FBSs). The distribution of MBSs and FBSs follow Poisson point process model. Unlike solution in the existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating node/user employs EH using interference from the adjacent base stations. Expressions for outage probability and throughput are derived at the user pair (comprising a cell-center user and a cell-edge user) served using NOMA. Comparison of the proposed EH system with the existing solutions is highlighted. Monte Carlo simulations are carried out to validate the analytical results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) communication system using hybrid precoding of subarray-connected structure at the base station. To maximize the received signal power of the user, we jointly optimize the active beamforming at the BS and passive beamforming at the IRS, respectively. Exploiting the rank-one property of mmWave channels, we transform the original problem into an approximate optimization problem and give the closed-form solution of the approximated problem. Simulation results show that our proposed scheme can effectively improve the received signal quality of the user with a high energy efficiency. [ABSTRACT FROM AUTHOR]

Recently, the reconfigurable intelligent surface (RIS) aided communication system has emerged as a promising candidate for future wireless communications. The existing channel estimation methods for RIS-aided millimeter-wave systems assume that the RIS is ideal without blockage. However, in practice, RIS may be blocked by the rain, snow, or dust, which will cause absorption and scattering of the incident/reflected signals and change the channel characteristics. In this paper, we formulate the system model of RIS-aided multi-user communications considering the blocked RIS. Then, we propose a variational Bayesian based channel estimation algorithm that is robust to RIS blockage, where we can simultaneously estimate the channel information and the RIS blockage. Simulation results demonstrate the superior performance of the proposed algorithm to existing ones. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) control information delivery is a critical communication with stringent requirements in terms of reliability and latency. In this context, link adaptation plays an essential role in the fulfillment of the required performance in terms of decode error probability and delay. Link adaptation is usually based on channel quality indicator (CQI) feedback information from the user equipment that should represent the current state of the channel. However, measurement, scheduling and processing delays introduce a CQI aging effect, that is a mismatch between the current channel state and its CQI representation. Using outdated CQI values may lead to the selection of a wrong modulation and coding scheme, with a detrimental effect on performance. This is particularly relevant in ultra reliable and low latency communications (URLLC), where the control of the reliability can be negatively impacted, and it is more evident when the channel is fast varying as the case of UAVs. This paper analyzes the effects of CQI aging on URLLCs, considering transmissions under the finite blocklength regime, that characterizes such communications type. A deep learning approach is investigated to predict the next CQI from the knowledge of past reports, and performance in terms of decode error probability and throughput is given. The results show the benefit of CQI proposed prediction mechanism also in comparison with previously proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, we study an uplink non-orthogonal multiple access (NOMA) wireless system with short-packet communications (SPC) in the presence of channel estimation errors and residual transceiver hardware impairments. Two NOMA decoding schemes are considered, namely fixed-order decoding (i.e., mean-power based decoding) and dynamic-order decoding (i.e., instantaneous-power based decoding). We first derive the closed-form exact and asymptotic expressions of the cumulative distribution functions (CDF) of the signal-to-interference-plus-noise ratio (SINR) associated with the two decoding schemes. Based on this, we analyze the average block error-rate (BLER) achieved in the finite block-length regime. Next, we formulate maximum throughput optimization problems with respect to the length of the packet. This problem is shown to be quasi-concave, which facilitates the finding of the optimal block-length via a search algorithm. Analytical comparisons between the NOMA-SPC system and its OMA-SPC counterpart are also performed. Simulation results are provided to validate the analysis. [ABSTRACT FROM AUTHOR]

Faster-than-Nyquist signaling (FTNS) has emerged as a promising technique to increase communication capacity in bandwidth-limited channels. However, the presence of FTN-induced inter-symbol interference (FTN-ISI) in the received observations, is detrimental to channel estimation (CE) and data detection in terms of computational complexity and performance. This paper copes with these problems by incorporating linear pre-equalization (LPE) and composite precoding formed by linear spectral precoding and Tomlinson-Harashima precoding (THP), into the FTNS. Specifically, LPE completely pre-equalizes the FTN-ISI, while spectral precoding resolves the LPE-caused signal spectral broadening by introducing proper artificial ISI, which is pre-equalized by THP. Channel-induced ISI, as the only ISI component in the observations, is estimated and equalized using the classical frequency-domain low-complexity schemes. We show that there are four advantages of the LPE-aided CE over the CE designed for the FTN transmissions without FTN-ISI pre-equalization, namely lower pilot overhead, simpler yet optimal pilot sequence design, lower mean-squared error of CE, and more robust against the FTN-ISI. Simulation results show that our scheme improves the performance of CE and detection, compared to existing FTN frequency-domain CE and equalization schemes. [ABSTRACT FROM AUTHOR]

An emerging technology namely the intelligent reflecting surface (IRS) can be deployed in millimeter wave (mmWave) communication to overcome the large propagation loss and huge power consumption issues. However, the large number of passive IRS elements without signal processing abilities induces high pilot overhead for channel estimation (CE). In this paper, the angular spread feature (or cluster feature) is exploited to formulate the CE problem as a structured sparse signal recovery problem. Then, a structured orthogonal matching pursuit (S-OMP) algorithm is proposed to efficiently solve the problem by utilizing the structure of the channel matrix in row and column without a priori knowledge of the angular spread. Simulation results demonstrate that S-OMP reduces about 40% to 50% pilot overhead at low signal-to-noise ratio (SNR) (SNR= 0 dB) while maintaining the same accuracy of CE compared with the existing methods. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) massive antenna array has shown great potential in gaining high-accuracy localization for user equipments (UEs). Yet, mmWave signals suffer from non-line-of-sight (NLOS) propagation and fast time-scale fading in practice, which will affect UE localization performance. It is non-trivial to reveal the impact of multipath propagation and fast time-scale fading on mmWave-based localization, and gain guidelines for harnessing NLOS propagation. In this paper, we aim to provide a unified framework for performance analysis of mmWave-based UE localization and channel estimation. Firstly, closed-form Cramer-Rao lower bounds for UE localization and channel estimation, respectively, are derived to shed lights on their performance limits. Secondly, the effect of system elements (e.g., channel correlation and fast time-scale fading) on UE localization performance is quantitatively analyzed. In addition, information contribution from NLOS channel is quantified to reveal the impact of multipath propagation on UE localization. Finally, we show that the NLOS propagation-caused localization error can be alleviated by using a NLOS model folding-based localization scheme1, which strikes a balance between computational complexity and localization performance. [ABSTRACT FROM AUTHOR]

Range-based positioning using wireless signal has gained remarkable attention in past decades. Accurately extracting the distance information, i.e., estimating parameters of individual multipath, is of interest. It has been shown that diffuse multipath component (DMC) in wireless propagation channel has significant influence on channel estimation and, thus, the ranging performance. In addition to specular component (SC), DMC should be considered in order to improve the estimated accuracy of channel paths’ parameters and, thus, the ranging performance. In this paper, we extend the quasi-maximum likelihood space-alternating generalized expectation-maximization (SAGE) algorithm by incorporating an autoregressive moving average (ARMA) filter to estimate the channel parameters under the presence of DMC. The parameters of both DMC and SC are estimated in an iterative approach, where the DMC is assumed as colored noise and the ARMA-filter is used to estimate the contributions from DMC. The validation of the algorithm based on measured data shows that both SC and DMC can be accurately estimated by the proposed approach. A comparison with standard SAGE and AR-SAGE is conducted, where the results reveal that the proposed method outperforms other two approaches in terms of ranging accuracy. [ABSTRACT FROM AUTHOR]

Applying non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) networks has the potential to significantly enhance the spectral efficiency. In this paper, we propose an opportunistic beam-splitting NOMA scheme to fully exploit the antenna array gain in downlink mmWave networks, where single- and double-beam NOMA transmissions are dynamically enabled according to the spatial angle difference between the paired NOMA users, thereby enhancing the flexibility of applying NOMA in mmWave networks. The base station with a single radio frequency chain implements the double-beam NOMA transmission by adopting the beam-splitting technique. We develop a unified theoretical performance analysis framework for the proposed scheme, while taking into account the spatially random users, link blockage model, and general distance-based user pairing strategy. By using tools from stochastic geometry, we derive the exact expressions of the coverage probability and the sum rate of the proposed scheme under both the LoS exponential and ball models. We further derive the corresponding upper bound to simplify the analytical expressions. Simulations validate the theoretical analysis and demonstrate the performance gains of the proposed scheme in terms of the coverage probability and sum rate over the conventional single-beam NOMA and orthogonal multiple access (OMA) schemes. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of channel estimation in a single cell massive multiple-input multiple-output (mMIMO) system with distributed antenna arrays (DAA) under the availability of limited pilots. Specifically, we propose to use pilots that are not only orthogonal within a cluster but also minimally correlated across clusters. We show that pilot sets that form mutually unbiased orthonormal bases (MUOB) minimize both inter- and intra-cluster pilot contamination. This also allows us to optimally choose the number of pilots needed to serve a given number of user equipments (UEs) within a DAA-mMIMO system. Following this, we develop an access point (AP) centered UE clustering algorithm to optimally cluster UEs for MUOB pilot allocation. Our experiments reveal that in a DAA-mMIMO system, MUOB not only offers better fairness compared to the traditional orthogonal pilot reuse scheme in terms of channel estimation error and per-UE throughput, but also delivers a higher sum system throughput. [ABSTRACT FROM AUTHOR]

This paper proposes a convex optimization problem, based on which the optimal strategy of false data injection (FDI) attacks is obtained to intrude machine-type-communications (MTC) networks from the perspective of an attacker, aiming to seek effective defensive measures based on a good understanding of attackers’ behaviour. We consider a target tracking example, which is a typical application of MTC networks. Specifically, as a type of MTC devices, smart sensors each have the ability of perception, calculation and communication and all of them can form a sensor network. In this network, its sensors and transmission channels are vulnerable to FDI attacks, resulting in the degradation of system estimation performance. In order to maximize the estimation error covariance of MTC network, the attacker needs to decide which sensors and channels to intrude due to limited energy budget. The estimation error covariance of the MTC network is calculated, based on which a convex optimization problem to obtain the optimal attack strategy is proposed. Simulation results demonstrate that the optimal attack strategy maximizes the transient mean-square deviation and estimation error covariance of the MTC network. [ABSTRACT FROM AUTHOR]

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising candidate to support the requirements of the fifth-generation (5 G) and beyond networks. However, the capacity of the fronthaul network dramatically influences its performance. While wired fronthaul links can be seen as the optimal choice, they may not be practically feasible. Exploiting the enormous bandwidth available in the millimeter-Wave (mmWave) band to support the fronthaul links paves the way to achieve the full potential of CF mMIMO systems. In this paper, we investigate the uplink (UL) performance of CF mMIMO systems supported by mmWave-fronthaul networks. Using tools from stochastic geometry, we derive analytical expressions for both the distribution of the provided fronthaul capacity and the average UL data rates. We show that although increasing the density of blockages degrades the average UL data rates, increasing the density of CPUs can limit such effect. Moreover, the obtained results reveal that the network deployment should be adjusted according to the available fronthaul bandwidth and the density of blockages. In particular, for a given fronthaul bandwidth, increasing the density of APs beyond a certain limit would not achieve further improvement in the UL data rates. Besides, increasing the number of antennas per AP may even cause a degradation in the system performance. [ABSTRACT FROM AUTHOR]

In practical propagation environments, different massive MIMO users can have correlated angles in spatial paths. In this paper, we study the effect of angle correlation on inter-user channel correlation via a combination of measurement and analysis. We show three key results. First, we collect a massive MIMO channel dataset for examining the inter-user channel correlation in a real-world propagation environment; the dataset is now open-access. We observed channel correlation higher than 0.48 for all close-by users. Additionally, over 30% of far-away users, even when they are tens of wavelengths apart, have inter-user channel correlation that is at least twice higher than the correlation in the i.i.d. Rayleigh fading channel. Second, we compute the inter-user channel correlation in closed-form as a function of inter-user angle correlation, the number of base-station antennas, and base-station inter-antenna spacing. Our analysis shows that inter-user angle correlation increases the inter-user channel correlation. Inter-user channel correlation reduces with a larger base-station array aperture, i.e., more antennas and larger inter-antenna spacing. Third, we explain the measurements with numerical experiments to show that inter-user angle correlation can result in significant inter-user channel correlation in practical massive MIMO channels. [ABSTRACT FROM AUTHOR]

In this paper, we consider the orthogonal frequency division modulation (OFDM) based integrated radar-communication (Rad-Com) for automobile systems operating in millimeter wave band with imperfect time-frequency synchronization for communications. Since different pilot power allocation and placement strategies can result in different performance, we investigate the power allocation and placement design for the Rad-Com system. We analyze the effect of power allocation and pilot placement on the system performance. Based on the analysis, we provide, along with a channel estimation scheme, the pilot power allocation and placement designs that can optimize the radar and communication performance, respectively. Moreover, the tradeoff between radar and communication performance is discussed and a tradeoff design is presented. We conduct simulations to validate our analysis and evaluate the proposed designs. Results show that our analysis is valid and insightful and the proposed designs are effective. [ABSTRACT FROM AUTHOR]

The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts. [ABSTRACT FROM AUTHOR]

In this paper, we introduce channel prediction to wireless communications aided by intelligent reflecting surface (IRS) in order to reduce the overhead brought by full channel estimation. Hopefully, fine-grained control of reflection coefficients on IRS can still be maintained with sufficiently accurate channel prediction. Innovatively, we consider channel prediction as an analogy to image super-resolution in computer vision since elements on an IRS are normally two-dimensionally arranged, which makes the popular deep learning techniques efficient solution. To this end, we first solve the element control problem via semi-definite relaxation (SDR) and then design an end-to-end deep-learning model for channel prediction. With estimation for only part of the entire channel state information (CSI), the total channel estimation overhead can be significantly reduced. We visually show that the purposed deep-learning model maintains considerably high accuracy for channel prediction, which makes the proposed channel-prediction method outperforms the existing group-based method in terms of achievable rate. Moreover, the tradeoff between channel estimation overhead and channel prediction accuracy is also investigated with numerical results. [ABSTRACT FROM AUTHOR]

Consider a base station (BS) relying on a massive antenna array, which transmits information to multiple vehicles of vehicular networks. In order to jointly consider both the communication resource consumption and the road-traffic efficiency, we define a metric given by the BS's downlink power normalized by the vehicular velocity. We refer to it as the Power to Velocity Ratio (PVR). The prime objective of this paper is to minimize either the maximum individual vehicle PVR or the entire system's PVR by optimizing the power allocation at the BS, while guaranteeing the information requirements of the vehicles both under the total transmit power constraint and driving velocity constraint. As for the individual vehicle PVR, a closed-form power allocation expression is derived by assuming that the transmit power constraint is non-restrictive. Based on this an optimal power allocation algorithm is proposed for arbitrary finite transmit power constraints. As for the system PVR, the non-convex problem formulated is first transformed into a convex problem and then the optimal solution is found by conceiving an efficient iterative algorithm. Our simulation results show that the proposed algorithms indeed succeed in achieving the optimal individual vehicle or system PVR. [ABSTRACT FROM AUTHOR]

To meet stringent service requirements in 5G at reasonable CapEx/OpEx, multi radio unit (RU) joint transmission has gained great attention. Optimal precoding design is a main concern in multi-RU joint transmission. In this paper, a new multi-RU joint precoding scheme is newly designed for sum-r ate-maximization with a minimum rate guarantee constraint under imperfect channel estimation. Owing to non-convexity of minimum rate guarantee constraints which causes huge computational complexity, we decouple the non-convex rate guarantee constraint into two convex constraints; desired signal constraint and interference constraint. And then, we propose a computationally efficient algorithm to find the suboptimal solution by iteratively solving two convex suboptimization problems. Through intensive simulations, the performance of the proposed scheme is evaluated and found to be superior compared with two previous schemes as well as the small-cell base station (SBS) transmission scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep neural network (DNN) aided codebook based beamforming scheme for frequency-division-duplex (FDD) millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) systems under multipath. Different from the time-division-duplex (TDD) systems, for FDD systems the reciprocity between the down-link (DL) and up-link (UL) channel state information (CSI) does not hold in general, requiring an extra CSI feedback stage. Based on the previous theoretical analysis and measurements, however, partial reciprocities, including the spatial directional angles and number of propagation paths, do exist for FDD mmWave systems. Given such partial reciprocities, we propose a beamforming scheme based on a predefined codebook. Furthermore, we consider the channels with multipath where multiple codewords can be selected for some mobile stations (MSs) to utilize more than one propagation paths, which improves the link reliability. We then propose the corresponding beamforming strategy that not only identifies the number of paths, but also reconstructs the individual single-path channels which are easier to deal with at the beamforming stage. To select the optimal codewords for these single-path channels with lower complexity, we then present a DNN-aided approach that predicts the optimal codewords. The integration of DNN resolves not only the issue of power leakage, but also angular ambiguity in the codebook. Simulation results demonstrate that the performance of the proposed DNN-aided approach is very close to that of an exhaustive search. [ABSTRACT FROM AUTHOR]

In the existing millimeter-wave (mmWave) wireless positioning systems, the method based on sparse Bayesian learning (SBL) uses the channel sparsity to estimate the parameters required for positioning, such as angle of arrival (AOA) and time delay, but most existing SBL solutions only consider the angle sparsity. In this paper, we consider the joint sparsity of the angle domain and time delay domain to propose an improved SBL algorithm by using a new two-dimensional adaptive grid refinement method in the SBL framework. This algorithm solves the grid mismatch problem caused by the fixed grid in the traditional SBL method, and reduces the algorithm complexity of the off-grid SBL (OGSBL) algorithm. We also obtain the Cramér-Rao bound (CRB) of AOA, time delay and position estimation based on the mmWave multipath signals to analyze the estimation errors. Simulation results show that the performance of the proposed algorithm is better than existing algorithms and can approach CRB. [ABSTRACT FROM AUTHOR]