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This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks. [ABSTRACT FROM AUTHOR]

The vulnerability of civil receivers of the Global Satellite Navigation System (GNSS) to spoofing jamming has raised significant concerns in recent times. Traditional multi-antenna spoofing detection methods are limited in application scenarios and come with high hardware costs. To address this issue, this paper proposes a novel GNSS spoofing detection method utilizing three low-cost collinear antennas. By leveraging the collinearity information of the antennas, this method effectively constrains the observation equation, leading to improved estimation accuracy of the pointing vector. Furthermore, by employing a binary statistical detection model based on the sum of squares (SSE) between the observed value and the estimated value of the pointing vector, real-time spoofing signal detection is enabled. Simulation results confirm the efficacy of the proposed statistical model, with the error of the skewness coefficient not exceeding 0.026. Experimental results further demonstrate that the collinear antenna-based method reduces the standard deviation of the angle deviation of the pointing vector by over 55.62% in the presence of spoofing signals. Moreover, the experiments indicate that with a 1 m baseline, this method achieves 100% spoofing detection. [ABSTRACT FROM AUTHOR]

This work studies the impact of the array geometry on a space-time extension of a generalized class of null-space precoding format denoted as $\pi $ -waveforms (Projection-Invariant waveforms), which exhibit subspace invariance and robustness to subspace leakage in the single-antenna case. In feedforward opportunistic communications, subspace leakage decreases the interference mitigation capability and the energy efficiency of conventional null-space precoding schemes. These effects are more notorious in multi-antenna scenarios, where miscalibrated sensors alter the array geometry. The study, which is carried out at both time-frequency non-asymptotic and asymptotic regimes, concludes that $\pi $ -waveforms are independent of the array geometry, allowing their use in general unstructured or uncalibrated arrays. The array-geometry invariance is fundamental to enabling calibration-less coherent opportunistic communication.

This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks.

The effective spoofing detection can prevent GNSS receiver from providing erroneous positioning, velocity and timing information. Most of the available multi-antennas carrier phase double difference-based spoofing detection methods, regardless of the aiding of inertial navigation system (INS), exploit the divergence in carrier phase double differences derived from the direction of arrival (DOA) characteristics of the authentic signal and spoofing signal. However, the traditional methods mainly implemented by dual-antenna GNSS receivers has spatial ambiguity and lacks global detection performance analysis. Furthermore, an INS-aided multi-antenna spoofing detection method is proposed without spatial ambiguity comparing the predicted signal DOA by INS and the vectoral carrier phase double differences measured by multi antennas. To analyze the influence of the antenna array baseline length and signal DOA on the detection performance, a comprehensive numerical simulation was conducted. The dead zone of the proposed detection method is revealed to assess the global spoofing detection performance. The effectiveness of the proposed method is verified under the GNSS positioning scenarios. In the worst case, spoofing signals can be effectively detected in 95.2% of the airspace under one time the wavelength antenna baseline configuration and in 99.3% of the airspace under five times the wavelength antenna baseline configuration, respectively.

We present two alternative frameworks for the statistical characterization and performance evaluation of the fluctuating two-ray (FTR) fading model which simplify previous approaches. The new formulations are based on the fact that the FTR fading distribution can be described, for arbitrary $m$ , as an underlying Rician Shadowed (RS) distribution with continuously varying parameter $K_{r}$ (ratio of specular to diffuse power components), while for the special case of $m$ being an integer, it is demonstrated that the FTR fading model can be described in terms of a finite number of underlying squared Nakagami- $m$ distributions. It is shown that any performance metric that is computed by averaging over the probability density function (PDF) of the FTR fading model can be expressed in terms of a finite-range integral over the corresponding performance metric for the simpler RS (for arbitrary $m$) or Nakagami- $m$ (for integer $m$) fading models, for which many results are available in closed-form. New expressions for some Laplace-domain statistics of interest are also obtained; these are used to analyze the outage probability of FTR fading under co-channel interference, as well as to obtain closed-form expressions for the main statistics of a composite wireless channel model encompassing FTR fading and shadowing. [ABSTRACT FROM AUTHOR]

In recent years, train-to-train communication has become an emerging technology and especially, an urgent need in the field of urban rail transit. But in the subway, due to the limited space, the electromagnetic wave propagation is seriously affected by the multipath fading, which is directly related to the operation safety of the urban rail transit. To solve this problem, this paper conducts the research on mitigating the multipath fading of direct train-to-train communication under the subway environment. Firstly, the appropriate communication distance and frequency band are selected to adapt to the actual tunnel environment. Secondly, a millimeter-wave communication device structure that uses three-antenna technology to mitigate multipath fading is designed. Then, the combination of Pre/Post-Rake technology and multi-antenna algorithm is proposed to reduce the bit error rate to meet the railway communication standard. Finally, the millimeter-wave path loss model is used to simulate and verify the error characteristics of the improved rake receiver. The simulation results show that the rake receiver can effectively mitigate the influence of multipath fading and improve the reliability and security of urban rail communication. [ABSTRACT FROM AUTHOR]

This work investigates the physical layer secrecy of a multi-antenna hybrid satellite-terrestrial relay networks (HSTRN) with jamming, in which a satellite aims to make communication with a destination user by means of a relay, along with spatially random eavesdroppers. In order to weaken the signals of eavesdroppers, the conventional relay can also generate intentional interference, besides forwarding the received signal. Shadowed-Rician fading is adopted in satellite link, while Rayleigh fading is adopted in terrestrial link, eavesdropper link and jamming link. The analytical and asymptotic formulas for the system secrecy outage probability (SOP) are characterized. Practical insights on the diversity order of the network are revealed according to the asymptotic behavior of SOP at high signal-to-noise ratio (SNR) regime. Then, analysis of the system throughput is examined to assess the secrecy performance. In the end, numerical simulation results are presented to validate the theoretical analysis and point out: (1) The secrecy performance of the considered network is affected by the channel fading scenario, the system configuration; (2) Decrease of the relay coverage airspace can provide better SOP performance; (3) Jamming from the relay can improve secrecy performance without additional network resources. [ABSTRACT FROM AUTHOR]

Signal detection is one of the most critical and challenging issues in ambient backscatter communication (AmBC) systems. In this paper, a multi-antenna AmBC signal detection method is proposed based on reconfigurable intelligent surface (RIS) and deep reinforcement learning. Firstly, an efficient multi-antenna AmBC system is developed based on RIS, which can achieve information transmission and energy collection simultaneously. Secondly, a smart twin delayed deep deterministic (TD3) AmBC signal detection method is presented, based on deep reinforcement learning. Extensive quantitative and qualitative experiments are performed, which show that the proposed method is more compelling than the outstanding comparison methods. [ABSTRACT FROM AUTHOR]

We discuss the physical layer security scheme in the Full-Duplex (FD) MIMO point-to-point two-way communication system with residual self-interference, in which legitimate nodes send confidential information and null space Artificial Noise (AN) while receiving information. Because the Channel State Information (CSI) of the eavesdropper is unavailable, we optimize the covariance matrices of the information signal as well as the allocation of the antenna for transmitting and receiving to minimize the signal power consumption under the target rate constraint. As a result, the power of AN is maximized within the limit of total power, so the interception capability of the eavesdropper is suppressed as much as possible. Since self-interference cannot be completely eliminated, the optimization process of one legitimate node depends on the optimization result of the other. By substituting self-interference power in the secrecy rate formula with its average value, the joint optimization process at the two nodes is transformed into two separate and solvable optimization processes. Then, the Water-Filling Algorithm (WFA) and bisection algorithm are used to get the optimal covariance matrices of the signal. Furthermore, we derive the theoretical lower bound of ergodic achievable secrecy rate under rayleigh channels to evaluate the performance of the scheme. The simulation results show that the theoretical derivation is correct, and the actual achievable rate is very close to the target rate, which means that the approximation in the optimization is feasible. The results also show that secrecy transmission can be realized because a considerable secrecy rate can be achieved.

Spatial beamforming is an efficient way to realize dual-functional radar-communication (DFRC). In this paper, we study the DFRC design for a general scenario, where the dual-functional base station (BS) simultaneously detects the target as a multiple-input-multiple-output (MIMO) radar while communicating with multiple multi-antenna communication users (CUs). This necessitates a joint transceiver beamforming design for both MIMO radar and multi-user MIMO (MU-MIMO) communication. In order to characterize the performance tradeoff between MIMO radar and MU-MIMO communication, we first define the achievable performance region of the DFRC system. Then, both radar-centric and communication-centric optimizations are formulated to achieve the boundary of the performance region. For the radar-centric optimization, successive convex approximation (SCA) method is adopted to solve the non-convex constraint. For the communication-centric optimization, a solution based on weighted mean square error (MSE) criterion is obtained to solve the non-convex objective function. Furthermore, two low-complexity beamforming designs based on CU-selection and zero-forcing are proposed to avoid iteration, and the closed-form expressions of the low-complexity beamforming designs are derived. Simulation results are provided to verify the effectiveness of all proposed designs. [ABSTRACT FROM AUTHOR]

It is crucial for legitimate wireless surveillance to enhance the continuity of the monitor. In this letter, we investigate a non-outage probability maximization problem for a jamming-assisted eavesdropping scenario, where MRC and MRT schemes are applied to receive and transmit antennas at a full-duplex monitor, respectively. We first take QoS to a suspicious receiver into non-outage probability to formulate continuous eavesdropping. A closed-form expression of continuous eavesdropping non-outage probability is derived subject to the desired minimum SINR at a suspicious receiver. Then we prove the quasiconcavity of this expression and show the optimal solution to maximize non-outage probability by a simple bisection search. Numerical results demonstrate the continuous eavesdropping performance is optimal when the jamming signals are sent with the optimal power. The number of receive and transmit antennas at the legitimate monitor is positively correlated with the continuity of eavesdropping. [ABSTRACT FROM AUTHOR]

Enhanced vehicle-to-everything (eV2X) communication is one of the key challenges to be addressed by the fifth generation (5G) of cellular mobile communications. In particular, eV2X includes some 5G vehicular applications targeting fully autonomous driving which require ultra-high reliability. Although vehicular communications are by default assumed between single antennas located on the roof of the transmitter and receiver vehicles, prior art has shown that there are other antenna positions more suitable for V2X communication, depending on the specific communication context. Antenna selection can be used in this case to select one specific antenna or a subset of them better suited for a certain communication link. In this work, we propose a context-aware antenna selection procedure able to enhance the communication with multi-antenna vehicles. To enable such scheme in 5G systems, we discuss the necessary signaling to extend current 5G radio resource control and radio resource management mechanisms, which are mainly focused on single-antenna communication. The signaling overhead caused by context exchange for antenna selection is analyzed and compared to the overhead when reference signals are exchanged for that purpose instead. Finally, simulation results for a 5G platooning use case are presented to show the advantages of antenna selection. [ABSTRACT FROM AUTHOR]

In this paper, we consider a covert communication system with a multi-antenna full-duplex (FD) receiver to enhance covert performance. More precisely, the receiver Bob (i.e., multiple antennas) selects the best antenna to receive the covert message, and then it sends artificial noise (AN) to the warden Willie causing uncertainty by utilizing an antenna among the remaining antennas. In order to take full advantage of multi-antenna technology, we consider two cases: (1) Willie does not know which antenna Bob chooses to send AN, and Willie knows only part of the channel information. (2) Willie knows which antenna Bob chooses to send AN, and Willie knows all the channel information. Based on the analysis of the optimal detection threshold, we derive the minimum detection error probability of the two cases. Furthermore, given the pre-determined convert constraints, the closed-form expression of the maximum effective convert rate is derived. When the power of AN sent by Bob is greater than that of covert message sent by the sender Alice, surprised research results are shown: (1) the detection error rate of case 1 is higher than that of case 2. (2) the effective covert rate of case 1 is always lower than that of case 2. The simulation results show that the proposed scheme is more conducive to sending covert messages for Alice; in addition, the numerical results imply that the maximum effective covert rate of the system using the multi-antenna FD receiver is significantly higher than that of the traditional dual-antenna FD receiver system. [ABSTRACT FROM AUTHOR]

We investigate indoor human multi-target tracking in cartesian coordinates based on range, Doppler and Angle-of-Arrival measurements obtained with a four-antenna passive bistatic radar capturing 802.11ax Wi-Fi signals. A reference antenna selection method is described to perform angle processing correctly when dealing with target detection diversity among antennas. The tracking is performed by an Unscented Kalman Filter (UKF) to handle the non-linear relation between the measurement space and the state space. A Joint Probabilistic Data Association Filter is coupled to the UKF to handle the data association between tracks and measurements when dealing with multiple targets. Simulations are performed to determine the tracking parameters under heavy constraints and identify key scenarios. An experimental setup is built using Universal Software Radio Peripherals, featuring an over-the-air phase calibration for angle processing with an anchor antenna. It is used to validate the proposed single and multi-target tracking scheme. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) is an emerging paradigm that will enable using wireless to its full potential in future networks, not only to convey information but also to deliver energy. Such networks will enable trillions of future low-power devices to sense, compute, connect, and energize anywhere, anytime, and on the move. The design of such future networks brings new challenges and opportunities for signal processing, machine learning, sensing, and computing. The objective is to make the best use of the RF radiations, spectrum, and network infrastructure to provide cost-effective and real-time power supplies to wireless devices and enable wireless-powered applications. In this paper, we first review recent signal processing techniques to make WPT and wireless information and power transfer (WIPT) as efficient as possible. Topics include high-power amplifier and energy harvester nonlinearities, active and passive beamforming, intelligent reflecting surfaces, receive combining with multi-antenna harvester, modulation, coding, waveform, large-scale (massive) multiple-input multiple-output (MIMO), channel acquisition, transmit diversity, multi-user power region characterization, coordinated multipoint, and distributed antenna systems. Then, we overview two different design methodologies: the model and optimize approach relying on analytical system models, modern convex optimization, and communication/information theory, and the learning approach based on data-driven end-to-end learning and physics-based learning. We discuss the pros and cons of each approach, especially when accounting for various nonlinearities in wireless-powered networks, and identify interesting emerging opportunities for the approaches to complement each other. Finally, we identify new emerging wireless technologies where WPT may play a key role—wireless-powered mobile edge computing, wireless-powered sensing, and wireless-powered federated learning—arguing WPT, communication, computation, sensing, and learning must be jointly designed. [ABSTRACT FROM AUTHOR]

Signal detection is one of the most critical and challenging issues in ambient backscatter communication (AmBC) systems. In this paper, a multi-antenna AmBC signal detection method is proposed based on reconfigurable intelligent surface (RIS) and deep reinforcement learning. Firstly, an efficient multi-antenna AmBC system is developed based on RIS, which can achieve information transmission and energy collection simultaneously. Secondly, a smart twin delayed deep deterministic (TD3) AmBC signal detection method is presented, based on deep reinforcement learning. Extensive quantitative and qualitative experiments are performed, which show that the proposed method is more compelling than the outstanding comparison methods.

In this paper, we study an RF energy harvesting mobile edge computing network based on a SIMO/MISO system and NOMA schemes over Nakagami-m fading. Specifically, a multi-antenna user harvests RF energy from a power station by using a selection combining/maximal ratio combining scheme and offload its tasks to two MEC servers through downlink NOMA by employing transmit antenna selection/maximal ratio transmission scheme. Accordingly, we investigate the performance of six schemes, namely SC-TAS1, SC-TAS1, MRC-TAS1, MRC-TAS2, SC-MRT, and MRC-MRT, for this considered system. To evaluate the performance, exact closed-form expressions of successful computation probability are derived. We further propose the optimal algorithm to find the best parameter sets to achieve the best performance. Moreover, the impacts of the network parameters on the system performance for these schemes are investigated. Finally, the simulation results are also provided to verify the accuracy of our analysis.

In this study, we designed and demonstrated the performance of a multi-antenna remote differential monitoring system based on a single GNSS-over-fiber architecture. In this system, multiple GNSS signals are received by remote antennas through a microwave photonic link and are then transmitted to local end points. To enable fine positioning, we established a double differential model equation for use between the carrier phase of each remotely received signal and the reference GNSS signal, with the help of the time division mode using a high-speed optical switch. In our experiment, we established a 10 km microwave photonic link among three remote monitoring points. We estimate the resulting positioning accuracy to be within several millimeters and we obtained a response time of less than 10 ms. Compared with traditional single-antenna schemes, our designed system has significant advantages with respect to coverage area, real-time response time, and the performance cost of large-scale monitoring at no cost to the positioning accuracy. As such, this system will find important applications for the monitoring of large-scale civil engineering and natural environments.

The upcoming fifth-generation wireless technology application areas bring new communication performance requirements, mainly in terms of reliability and latency, but also in terms of radio planning, where the further detailed characterization of the wireless channel is needed. To address these demands, we developed an agile multi-node multi-antenna wireless channel sounding system, using multiple software-defined radio devices. The system consists of 12 testbed nodes which are controlled from a centralized testbed server. Each node features a control host computer and two multi-antenna universal software radio peripheral boards. By managing the transmission and reception of reference signals among all the distributed testbed nodes, the system can measure the channel conditions of all multiple independent radio links. At the same time, the distributed architecture of the testbed allows a large number of spatially distributed locations to be covered with only a few redeployments of the testbed nodes. As a consequence of this, the system favors the collection of a large number of distributed channel samples with limited effort within a short dedicated measurement time. In this paper, we detail the general testbed design considerations, along with the specific sounding signal processing implementations. As further support to the system design, we also include the results from different verification and calibration tests, as well as a real measurement application example.

In this paper, we consider a wireless-powered communication network (WPCN), where an energy-limited multi-antenna information source, powered by a dedicated power beacon, communicates with a mobile user (MU). The MU is equipped with a single-antenna and its mobility is characterized by the well-known random waypoint mobility model. To gain the advantages with the use of multiple antennas at the source, we adopt two popular multiple-antenna transmission schemes, namely maximal-ratio transmission (MRT) and transmit antenna selection (TAS). Differently from previous works which considered only static scenarios, this paper aims to investigate wireless power and information transfer in the scenario with a random mobile user under Nakagami-$m$ fading. It is noteworthy that a special case of our analysis, i.e., the Rayleigh fading case, has not been examined in the literature as well, which enhances the contribution value of the proposed analysis. Considering both MRT and TAS schemes, closed-form expressions for the outage probability, average delay-limit throughput, average delay-tolerant throughput, average bit error rate (BER), and throughput under average BER constraint are derived. The analysis quantifies the impact of the mobility and propagation environments, which are characterized by the path-loss exponent and multipath parameter for the PB-AP link and AP-MU link, on the performance of a WPCN. The analytical results are compared with Monte-Carlo simulations in order to validate the analysis and provide useful insights on the impact of different parameters on the system performance.

This paper presents a design of a Software Defined Radio (SDR) multi-antenna testbed able to record live cellular signals from multiple sites. This measurement setup based on Universal Software Radio Peripheral (USRP) boards, is used to record live Long Term Evolution (LTE) signals in sub-6 GHz frequency bands. Due to recording of raw I&Q samples, this fully digital testbed is suitable for variety of research activities spanning channel characterization and beamforming performance evaluation. We propose a phase calibration method based on transmission of a single out of band tone to overcome the uncertainty introduced by the USRP's lack of phase alignment. We demonstrate two use cases where the proposed testbed can be used and we validate its performance during two measurement campaigns with self-generated and real cellular signals.

In this paper, we investigate the secrecy outage performance of decode-and-forward (DF) buffer-aided relaying networks with a multi-antenna destination in the presence of an eavesdropper. In order to take full advantage of the benefits provided by the multiple antennas at the destination and the available relays, we adopt the max-link relay selection scheme and propose a half-duplex and two full-duplex secure transmission schemes for secrecy improvement, i.e., 1) maximal-ratio combining (MRC), 2) maximal-ratio combining/cooperative jamming (MRC/CJ), and 3) zero-forcing beamforming/cooperative jamming (ZFB/CJ). For all proposed schemes, we present exact and asymptotic closed-form expressions of the secrecy outage probability by modeling the dynamic buffer state transitions as a Markov chain. Moreover, simple and informative asymptotic results are provided under both L → ∞ and L → ∞ scenarios (where L denotes the buffer size), from which we can obtain further insights on the secrecy diversity gain and the secrecy coding gain. The highlights of this paper can be summarized as follows: 1) Under L → ∞ scenario, the secrecy diversity gains of all proposed schemes both reach M. When L → ∞, the secrecy diversity gains of MRC, MRC/CJ and ZFB/CJ increase to M (1 + ND), MND and 2M respectively, where M is the number of relays and ND represents the number of antennas at the destination; 2) The secrecy coding gain of the system differs with different schemes, and it improves with the increase of M and ND under both the two scenarios; and 3) Under the scenario L → ∞, ZFB/CJ outperforms MRC and MRC/CJ across the entire signal-to-noise ratio (SNR) range of interest, however, when L→ ∞, ZFB/CJ outperforms MRC/CJ and MRC in the low SNR regime, while the opposite holds in the high SNR regime.

In this article, we study an intelligent reflecting surface (IRS)-assisted system where a multi-antenna base station (BS) serves a single-antenna user with the help of a multi-element IRS in the presence of interference generated by a multi-antenna BS serving its own single-antenna user. The signal and interference links via the IRS are modeled with Rician fading. To reduce phase adjustment cost, we adopt quasi-static phase shift design where the phase shifts do not change with the instantaneous channel state information (CSI). We investigate two cases of CSI at the BSs, namely, the instantaneous CSI case and the statistical CSI case, and apply Maximum Ratio Transmission (MRT) based on the complete CSI and the CSI of the Line-of-sight (LoS) components, respectively. Different costs on channel estimation and beamforming adjustment are incurred in the two CSI cases. First, we obtain a tractable expression of the average rate in the instantaneous CSI case and a tractable expression of the ergodic rate in the statistical CSI case. We also provide sufficient conditions for the average rate in the instantaneous CSI case to surpass the ergodic rate in the statistical CSI case, at any phase shifts. Then, we maximize the average rate and ergodic rate, both with respect to the phase shifts, leading to two non-convex optimization problems. For each problem, we obtain a globally optimal solution under certain system parameters, and propose an iterative algorithm based on parallel coordinate descent (PCD) to obtain a stationary point under arbitrary system parameters. Next, in each CSI case, we provide sufficient conditions under which the optimal quasi-static phase shift design is beneficial, compared to the system without IRS. Finally, we numerically verify the analytical results and demonstrate notable gains of the proposal solutions over existing ones. To the best of our knowledge, this is the first work that considers optimal quasi-static phase shift design for an IRS-assisted system in the presence of interference. [ABSTRACT FROM AUTHOR]

This paper brings forward a Deep Learning (DL)-based Chaos Shift Keying (DLCSK) demodulation scheme to promote the capabilities of existing chaos-based wireless communication systems. In coherent Chaos Shift Keying (CSK) schemes, we need synchronization of chaotic sequences, which is still practically impossible in a disturbing environment. Moreover, the conventional Differential Chaos Shift Keying (DCSK) scheme has a drawback, that for each bit, half of the bit duration is spent sending non-information bearing reference samples. To deal with this drawback, a Long Short-Term Memory (LSTM)-based receiver is trained offline, using chaotic maps through a finite number of channel realizations, and then used for classifying online modulated signals. We presented that the proposed receiver can learn different chaotic maps and estimate channels implicitly, and then retrieves the transmitted messages without any need for chaos synchronization or reference signal transmissions. Simulation results for both the AWGN and Rayleigh fading channels show a remarkable BER performance improvement compared to the conventional DCSK scheme. The proposed DLCSK system will provide opportunities for a new class of receivers by leveraging the advantages of DL, such as effective serial and parallel connectivity. A Single Input Multiple Output (SIMO) architecture of the DLCSK receiver with excellent reliability is introduced to show its capabilities. The SIMO DLCSK benefits from a DL-based channel estimation approach, which makes this architecture simpler and more efficient for applications where channel estimation is problematic, such as massive MIMO, mmWave, and cloud-based communication systems.

In this paper, we investigate the secrecy performance against eavesdropping of a land mobile satellite (LMS) system, where the satellite employs the spot beam technique, and both the terrestrial user and eavesdropper are equipped with multiple antennas and utilize maximal ratio combining to receive the confidential message. Specifically, in terms of the availability of the eavesdropper's CSI at the satellite, we consider both passive (Scenario I) and active (Scenario II) eavesdropping. For Scenario I where the eavesdropper's channel state information (CSI) is unknown to the satellite, closed-form expressions for the probability of non-zero secrecy capacity and secrecy outage probability are derived. Furthermore, expressions for the asymptotic secrecy outage probability are also presented to reveal the secrecy diversity order and array gain of the considered system. For Scenario II where the eavesdropper's CSI is available at the satellite, novel expressions for the exact and asymptotic average secrecy capacity are obtained. Based on a simple asymptotic formula, we can characterize the high signal-to-noise ratio (SNR) slope and high SNR power offset of the LMS systems. Finally, simulations are provided to validate our theoretical analysis and show the effect of different parameters on the system performance.

The global navigation satellite system (GNSS)-based multi-antenna attitude determination method has the advantages of a simple algorithm and no error accumulation with time in long endurance operation. However, it is sometimes difficult to simultaneous obtain the fixed solutions of all antennas in vehicle attitude determination. If float or incorrect fixed solutions are used, precision and reliability of attitude cannot be guaranteed. Given this fact, a baseline-constrained ambiguity function method (BCAFM) based on a self-built four GNSS antennas hardware platform is proposed. The coordinates obtained by BCAFM can replace the unreliable real-time kinematic (RTK) float or incorrect fixed solutions, so as to assist the direct method for attitude determination. In the proposed BCAFM, the baseline constraint is applied to improve search efficiency (searching time), and the ambiguity function value (AFV) formula is optimized to enhance the discrimination of true peak. The correctness of the proposed method is verified by vehicle attitude determination results and baseline length difference. Experimental results demonstrate that the function values of error peaks are reduced, and the only true peak can be identified accurately. The valid epoch proportion increases by 14.95% after true peak coordinates are used to replace the GNSS-RTK float or incorrect fixed solutions. The precision of the three attitude angles is 0.54°, 1.46°, and 1.15°, respectively. Meanwhile, the RMS of baseline length difference is 3.8 mm.

This paper develops an analytical framework for exploring the benefits of applying cluster-based multi-antenna non-orthogonal multiple access (NOMA) in dense wireless networks. Using the tools of stochastic geometry, a new explicit expression and a tight approximation for per-cluster average data rates are derived in terms of relevant system parameters. Moreover, simulation results are provided to validate the accuracy of our analytical results and draw some essential system design insights. Based on the tractable expressions, we further consider the analysis and optimization of area spectral efficiency (ASE). It is analytically demonstrated that: 1) In terms of the per-cluster average data rate, there exists a wide range for the power allocation coefficient within a cluster where NOMA outperforms orthogonal multiple access (OMA); 2) The per-cluster average data rate of NOMA benefits more from the additional deployment of antennas at base stations (BSs) than that of OMA when the accuracy of channel state information at transmitter (CSIT) is fixed; 3) Regarding to ASE, there exists an optimal combination of the number of clusters and power allocation coefficient to maximize ASE. The performance gain of NOMA relative to OMA becomes marginal when the requirement of user fairness is stringent; 4) As for a special case that the power allocation coefficient is fixed, the analytical results indicate that ASE scales linearly with the number of antennas when the number of clusters is set optimally. [ABSTRACT FROM AUTHOR]

With the development of the maritime transportation industry, the number of ships is increasing and the ships are becoming more intelligent. Due to the rapid development of maritime communication, the demand for communication spectrum is increasing. Therefore, the maritime cognitive radio (CR) system is an effective solution. Because of the multipath fading caused by sea surface and atmosphere has a more serious influence on communication signals, which increases the instability of the signal reception, the spectrum sensing technology in maritime cognitive radio is more challenging than the spectrum sensing on land. In order to solve this problem, a cyclostationary detection algorithm for multiple antennas in fading model is proposed. A maximum ratio combining algorithm based on optimal weight correlation value (OWCV-MRC) is proposed for the diversity gain and system performance degradation caused by diversity technology on multipath fading channels. The algorithm uses the correlation values of the attenuation gains on the two different branches as the weighting coefficients of each branch, thus improving the coefficient matrix in the maximum ratio combining (MRC) algorithm. The simulation results show that the proposed algorithm can effectively detect the target signal in the fading channel with ultra-low signal to noise ratio (SNR). [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel secure random caching scheme for large-scale multi-antenna heterogeneous wireless networks, where the base stations (BSs) deliver randomly cached confidential contents to the legitimate users in the presence of passive eavesdroppers as well as active jammers. In order to safeguard the content delivery, we consider that the BSs transmits the artificial noise together with the useful signals. By using tools from stochastic geometry, we first analyze the average reliable transmission probability (RTP) and the average confidential transmission probability (CTP), which take both the impact of the eavesdroppers and the impact of the jammers into consideration. We further provide tight upper and lower bounds on the average RTP. These analytical results enable us to obtain rich insights into the behaviors of the average RTP and the average CTP with respect to key system parameters. Moreover, we optimize the caching distribution of the files to maximize the average RTP of the system, while satisfying the constraints on the caching size and the average CTP. Through numerical results, we show that our proposed secure random caching scheme can effectively boost the secrecy performance of the system compared to the existing solutions. [ABSTRACT FROM AUTHOR]

In this letter, we consider the issue of detecting signal of interest under interference. Two multi-antenna based spectrum sensing algorithms are presented given the knowledge of interference’s statistical characteristics. The first method is designed to neutralize the impacts of interference so that the generalized likelihood ratio test (GLRT) based method for interference-free environment can be used. In the second method, an optimization problem is formulated based on the GLRT concept and an approximate solution is derived in closed-form under low signal-to-interference ratio (SIR) condition. Numerical results are provided, which demonstrate the superiority of the proposed schemes compared with existing methods. [ABSTRACT FROM AUTHOR]

As the development of multidisciplinary techniques and ever-growing demands, autonomous system has been widely investigated in a variety of fields. In recent two decades, autonomous surface vessels have gained increasing attention from both industry and research communities, previous related work mainly focuses on the design of maneuver controllers, most algorithms are under some specific assumptions and always involve too many parameters that need to be tuned, leading to the lack of practical application or validation in real autonomous surface vessel platform. To tackle the issues, an integrated autonomous surface vessel prototype is designed and implemented, both navigation and maneuver control subsystems are improved, especially in terms of autonomous control, a simple geometrical model and constrained pure pursuit algorithm is firstly tried in autonomous surface vessel, the results show that the proposed system can achieve the path tracking with the error smaller than 40 cm, and even given a complicated waypoint mission, it can still maintain the accuracy of path tracking, demonstrating the stability and validity of the autonomous surface vessel prototype. [ABSTRACT FROM AUTHOR]

This paper proposes and analyzes the performance of a multi-antenna NOMA relay system over the Nakagami- m fading channel, in which the size of signal packets is finite. Based on the application of the Gaussian–Chebyshev quadrature, Riemann integral and incomplete Gamma function, the exact and asymptotic closed-form expressions of the block error rate (BLER), throughput, goodput of the proposed finite block-length multi-antenna NOMA relay system are derived. Moreover, in order to improve the BLER, throughput, and goodput of the proposed system, maximal-ratio combining and maximal-ratio transmission are applied to receivers and transmitters, respectively. The provided analysis and simulation results indicate that the proposed system meets the requirements of ultra-reliable and low-latency communications (URLLC). Especially, the reliability is higher than 99.99%, i.e., the BLER is less than 1 0 − 4 . The accuracy of analysis results is verified by simulation results. Furthermore, the number of transmission bits and power allocation coefficients are optimized to maximize the throughput, and goodput and minimize the BLER. [ABSTRACT FROM AUTHOR]

For symmetric non-orthogonal multiple access (NOMA)/multiple-input multiple-output (MIMO) systems, radio resource allocation is an important research problem. The optimal solution is of high computational complexity. Thus, one existing solution Kim et al. proposed is a suboptimal user selection and optimal power assignment for total data rate maximization. Another existing solution Tseng et al. proposed is different suboptimal user grouping and optimal power assignment for sum video distortion minimization. However, the performance of sub-optimal schemes by Kim et al. and Tseng et al. is still much lower than the optimal user grouping scheme. To approach the optimal scheme and outperform the existing sub-optimal schemes, a deep neural network (DNN) based approach, using the results from the optimal user selection (exhaustive search) as the training data, and a loss function modification specific for NOMA user selection to meet the constraint that a user cannot be in both the strong and weak set, and avoid the post processing online computational complexity, are proposed. The simulation results show that the theoretical peak signal-to-noise ratio (PSNR) of the proposed scheme is higher than the state-of-the-art suboptimal schemes Kim et al. and Tseng et al. by 0.7~2.3 dB and is only 0.4 dB less than the optimal scheme at lower online computational complexity. The online computational complexity (testing stage) of the proposed DNN user selection scheme is 60 times less than the optimal user selection scheme. The proposed DNN-based scheme outperforms the existing suboptimal solution, and slightly underperforms the optimal scheme (exhaustive search) at a much lower computation complexity.

Opportunistic beamforming (OBF) is a potential technique in the fifth generation (5G) and beyond 5G (B5G) that can boost the performance of communication systems and encourage high user quality of service (QoS) through multi-user selection gain. However, the achievable rate tends to be saturated with the increased number of users, when the number of users is large. To further improve the achievable rate, we proposed a multi-antenna opportunistic beamforming-based relay (MOBR) system, which can achieve both multi-user and multi-relay selection gains. Then, an optimization problem is formulated to maximize the achievable rate. Nevertheless, the optimization problem is a non-deterministic polynomial (NP)-hard problem, and it is difficult to obtain an optimal solution. In order to solve the proposed optimization problem, we divide it into two suboptimal issues and apply a joint iterative algorithm to consider both the suboptimal issues. Our simulation results indicate that the proposed system achieved a higher achievable rate than the conventional OBF systems and outperformed other beamforming schemes with low feedback information.

In this paper, the receive spatial modulation (RSM) scheme is studied in the downlink of the multi-cell multi-user systems, assuming that the base station (BS) is equipped with a large number of antennas and serves a large number of multi-antenna users, while the user-antenna ratio is bounded. The system model is practical and applicable in a comprehensive manner, accounting for channel estimation, spatially correlated channels, pilot contamination, and path loss. Combining the concepts of RSM and massive multiple-input multiple-output (MIMO), the spectral efficiency of linear preprocessing zero-forcing (ZF) and regularized ZF (RZF) methods is studied by approximating the probability of antenna detection and deriving a lower bound for the spatial-domain mutual information. With the aid of the large-system analysis, the deterministic equivalent expressions are provided for the achievable rates that are asymptotically exact. The Monte Carlo simulations validate this claim and show that for limited dimensions, the proposed deterministic equivalents yield accurate approximations, even for strongly correlated channels. The results further show that in comparison with the conventional massive MIMO systems with single-antenna users, equipping each user with multiple antennas and applying the RSM scheme enhances the spectral efficiency when each cell is not heavily loaded. [ABSTRACT FROM AUTHOR]

Caching at base station is a promising technology to reduce the backhaul loads in future wireless networks. To exploit the content diversity gain and multiple antenna transmission, in this paper, we consider the analysis and optimization of random caching in multi-antenna small-cell networks with limited backhaul. We analyze the performance of the success probability, which is defined as the probability that the requested file is not only obtained from the cache or the backhaul at the small base station (SBS), but also successfully delivered via the wireless link from the SBS to the user. Using tools from stochastic geometry, we first derive an exact expression and an upper bound of the success probability. Based on the analytical results, we then consider network throughput maximization by optimizing the design parameters in the cache-enabled multi-antenna small-cell networks. The optimization problem is a complicated mixed-integer optimization problem. After analyzing the optimal properties, we obtain a local optimal solution with low complexity. To further simplify the optimization, we solve an asymptotic optimization problem in the high user density region, using the upper bound as the objective function. Numerical simulations show that our proposed scheme improves the performance by up to $14\%$ when the Zipf parameter is 0.4 and the antenna number is 8. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep learning-based scheduling scheme for user selection in multi-user multi-antenna networks which is considered one of key technologies for the next generation mobile communication systems. We obtained 90,000 data samples from the conventional optimal scheme to train the proposed neural network and verified the trained neural network to check if the trained neural network is over-fitted. Although the proposed neural network-based scheduling algorithm requires considerable complexity and time for training in the initial stage, it does not cause any extra complexity once it has been trained successfully. On the other hand, the conventional optimal scheme continuously requires the same complexity of computations for every scheduling. According to extensive computer-simulations, the proposed deep learning-based scheduling algorithm yields about 88~96% average sum-rates of the conventional scheme for SNRs lower than 10dB, while it can achieve optimal average sum-rates for SNRs higher than 10dB. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. First, we estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high-estimation accuracy. The simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting the promising application of the proposed scheme in large-scale antenna array systems. [ABSTRACT FROM AUTHOR]

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In a multi-tag radio frequency identification (RFID) system, signals sent from different tags are likely to interfere with others, leading to a degradation in the transmission quality. To solve this problem, a novel widely linear minimum-mean-square-error (WLMMSE) anti-collision method is proposed by taking into account the improper second-order statistics of backscattered tag signals, which result from the quadrature nature of RFID readers. The proposed method provides an efficient means to separate multiple overlapping tag signals within a single time slot and can be easily integrated into multi-antenna RFID systems to improve their throughput. Its modeling advantages over conventional linear model-based signal recovery methods are illustrated by both the simulations and real-world experiments. [ABSTRACT FROM AUTHOR]

This paper studies a multiuser mobile edge computing (MEC) system in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancelation (SIC) to efficiently decode all users’ offloaded tasks for remote execution. In particular, we pursue energy-efficient MEC designs by considering two cases with partial and binary offloading, respectively. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS’s decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a mixed Boolean convex optimization problem that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that for the case with binary offloading, the proposed greedy method performs close to the optimal BnB-based solution, and the convex relaxation-based solution achieves a suboptimal performance but with lower implementation complexity. [ABSTRACT FROM AUTHOR]

A hybrid antenna is proposed for future 4G/5G multiple input multiple output (MIMO) applications. The proposed antenna is composed of two antenna modules, namely, 4G antenna module and 5G antenna module. The 4G antenna module is a two-antenna array capable of covering the GSM850/900/1800/1900, UMTS2100, and LTE2300/2500 operating bands, while the 5G antenna module is an eight-antenna array operating in the 3.5-GHz band capable of covering the C-band (3400-3600 MHz), which could meet the demand of future 5G application. Compared with ideal uncorrelated antennas in an 8 × 8 MIMO system, the 5G antenna module has shown good ergodic channel capacity of ~40 b/s/Hz, which is only 6 b/s/Hz lower than ideal case. This multi-mode hybrid antenna is fabricated, and typically, experimental results such as S-parameter, antenna efficiency, radiation pattern, and envelope correlation coefficient are presented.