Accurate channel state information (CSI) is critical for signal detection and precoding design in massive multiple-input multiple-output (MIMO) systems. However, traditional channel attention mechanisms for CSI feedback heavily rely on global pooling methods, which overlook finer-grained statistical patterns. In this paper, we propose a novel CSI feedback scheme for massive MIMO systems by utilizing a differentiable histogram attention mechanism, named DHANet, to boosts channel feature extraction and improve system performance. Specifically, DHANet replaces the traditional global pooling operations in the Squeeze-and-Excitation Block with kernel density estimation (KDE)-based differentiable histogram feature extraction, thereby enabling the capture of detailed channel-specific statistical information for more efficient CSI feedback. Moreover, the proposed mechanism can be seamlessly integrated into the existing CSI feedback architectures. Simulation results demonstrate that the DHANet achieves superior performance compared to the traditional pooling-based methods, particularly under 1/8 compression rates, highlighting its significant potential for CSI feedback in massive MIMO systems.

This letter proposes a novel hybrid automatic repeat request with chase combining assisted sparse code multiple access (HARQ-CC-SCMA) scheme. Depending on whether the same superimposed packet is retransmitted, synchronous and asynchronous modes are considered for retransmissions. Moreover, a factor graph aggregation (FGA) method is used for multi-user detection. Specifically, a large-scale factor graph is constructed by combining all the received superimposed signals and message passing algorithm (MPA) is applied to calculate log-likelihood ratio (LLR). Monte Carlo simulations are preformed to show that FGA surpasses bit-level combining (BLC) and HARQ with incremental redundancy (HARQ-IR) in synchronous mode. Moreover, FGA performs better than BLC at high signal-to-noise ratio (SNR) region in asynchronous mode. However, FGA in asynchronous mode is worse than BLC at low SNR, because significant error propagation is induced by the presence of failed messages after the maximum allowable HARQ rounds.

Precise positioning is becoming increasingly crucial across various applications, including rescue operations, intelligent transportation, logistics, and environmental monitoring. However, previous research often assumes that wireless sensor networks are perfectly synchronized or have unlimited communication resources. These assumptions frequently do not align with real-world scenarios, particularly in resource-constrained networks with stringent power and communication limits. In this paper, we propose a target localization and time synchronization algorithm based on the quantization of time-of-arrival (TOA) measurements. This approach accounts for both the quantization process and clock offsets caused by asynchronous clocks between sensor nodes and the target source. To tackle the problem, a variational Bayesian learning method is proposed to estimate the target source location and clock offsets jointly. This algorithm aims to find a feasible variational distribution that approximates the true posterior distribution, thereby conducting the Bayesian estimation over the approximate distributions and obtaining the accurate locations and clock offsets. Additionally, we derive the quantized Bayesian Cramr-Rao Bound (QBCRB) for quantized TOA measurements to evaluate localization and time synchronization performance. Simulation and experimental results demonstrate that the proposed method provides effective estimations and outperforms other comparison algorithms across various scenarios.

The Internet of Vehicles (IoV) is a specific instance of the Internet of Things (IoT) in the transportation field, driven by application requirements, such as intelligent traffic services and automatic vehicle control, can improve road safety and enhancing transmission efficiency. However, highly open networks tend to bring more security threats, and secure authentication becomes an important guarantee for reliable communication. Traditional IoT authentication and key agreement methods are costly, inefficient, and rely on the third-party trusted institutions, making them unsuitable for direct application in IoV systems. To meet the security authentication needs of IoV, and improve authentication efficiency and anonymity, this article proposes a verifiable commitment-based mutual authentication and key agreement protocol, called mutual authentication and key agreement protocol based on verifiable commitment (VC-MAKA). In VC-MAKA, we construct a verifiable commitment scheme where the verifier can verify the committed secret. Furthermore, based on this verifiable commitment scheme, we implement secure authentication and session key agreement, allowing vehicles to freely negotiate secure session keys and achieving conditional anonymous protection. Additionally, the proposed VC-MAKA also achieves rapid session key updates, enhancing the security of the session keys. We have conducted formal and informal security analysis, and the results show that VC-MAKA meets security requirements, such as mutual authentication, anonymity, traceability, and untraceability. Moreover, we have used the ProVerif tool for security experiment and performance comparison analysis, and the results indicate that compared to other schemes, the VC-MAKA protocol offers higher security and better efficiency.

Massive multiple-input–multiple-output (MIMO) systems provide efficient connectivity services for a large number of industrial Internet of Things (IoT) devices. To support numerous IoT devices, this article considers downlink hybrid architecture massive MIMO systems with phase shifters-based and switches-based cases, in which the data stream is first sent to the digital domain and then processed by the analog domain. zero-forcing (ZF) receivers are considered under the assumption of perfect channel state information (CSI). For hybrid architecture MIMO systems under quantized phase shifters and switches, we derive asymptotic approximations of the spectrum efficiency (SE) for massive MIMO systems using random matrix theory and then we propose an iterative algorithm to generate corresponding analog processing matrix, which can be used arbitrary quantized phase shifters. With the help of derived theoretical SE and realistic power consumption model, we further evaluate the total energy efficiency (EE) and provide insights into the tradeoffs between the SE and total EE. Finally, Monte Carlo simulation results are presented to substantiate our theoretical analysis results and showcase that the hybrid architecture provides higher total EE compared to existing all-digital architectures, but its achievable SE is lower than that of the hybrid architecture. Furthermore, the results also show that the switchbased hybrid architecture have ability to achieve the enhanced EE performance with large number of base station antennas and low-signal-to-noise ratio regimes compared to quantized phase shifters.

The accuracy of channel state information (CSI) acquisition is nontrivial for intelligent reflection surface (IRS)-assisted massive multiple-input–multiple-output (MIMO) systems due to the IRS comprises of a large number of low-cost electromagnetic reflection units that can smartly reflect the impinging signal. In this article, we investigate the downlink channel estimation problem for IRS-assisted millimeter-wave (mmWave) massive MIMO systems, where the IRS provides effective reflected paths to enhance the coverage and spectral efficiency. To reduce the hardware costs and power consumption, the massive MIMO system is adopted the mixed-analog-to-digital converter (ADC) architecture, in which a fraction of antennas is equipped with high-resolution ADCs and a large number of antennas are deployed with low-resolution ADCs. Considered the finite-dimensional millimeter-wave channel model, we utilize the special row–column sparse characteristics of the channel matrix and propose a row-structured sparsity based on the orthogonal matching pursuit (RS-OMP) algorithm. The RS-OMP algorithm sequentially calculates the row–column support sets of the angular cascaded channel matrix and then uses the least square (LS) algorithm to reconstruct the cascaded channel matrix, which aim to achieve a lower computational complexity. Simulation results demonstrate that under the same system configuration, the proposed RS-OMP algorithm not only improves the accuracy of channel estimation but also reduces more than 75% of the pilot overhead compared to the traditional orthogonal matching pursuit algorithm.

Due to the rapid development of indoor Unmanned Aerial Vehicles (UAVs) in recent years, the localization of indoor UAVs has become a focus of attention in UAVs applications. Among them, the Wireless Local Area Network (WLAN) based localization approach has become an effective means to achieve indoor localization due to the widely-deployed WLAN infrastructure. At the same time, with the increased use of WLAN module in the state-of-the-art mobile devices, various types of mobile WLAN Access Points (APs) exist in indoor environment. In this circumstance, the mobile WLAN APs deteriorates localization accuracy since their associated Received Signal Strength (RSS) data become unstable with the variation of locations. To address this problem, a new approach based on the Density-based Spatial Clustering of Applications with Noise (DBSCAN) is proposed to detect mobile WLAN APs for manifold alignment localization of UAVs. Specifically, first of all, the DBSCAN is conducted at the Reference Points (RPs) on motion paths to detect mobile APs. Second, the RSS data from mobile APs are removed from the database to enhance the location-dependency of RSS data used for the localization. Third, the concept of augmentation process is considered in manifold alignment to achieve satisfactory localization accuracy. Finally, the extensive experimental results show that the proposed system performs better in localization accuracy compared with the existing CIMLoc and WILL under the presence of mobile WLAN APs.

In this paper, we propose a decentralized robust transmit beamforming and power splitting design for multi-relay assisted simultaneous wireless information and power transfer (SWIPT) systems with imperfect channel state information (CSI). Under the constraints of target rates at relays and users, and energy harvesting (EH) at users, a total transmit power minimization problem is proposed by considering the non-linear EH model. We first reformulate this nonconvex problem into an semidefinite program (SDP) by semidefinite relaxation (SDR) and S-procedure, and then propose an alternating direction method of multipliers (ADMM)-based decentralized algorithm. Numerous simulations are provided to demonstrate the effectiveness of the proposed design. Results show that the proposed design outperforms traditional design in terms of quality of service (QoS) and the feasibility of practical implementation.

Considering the widespread use of effective capacity in cross-layer design and the extensive existence of renewal service processes in communication networks, this paper thoroughly investigates the effective capacity for renewal processes. Exact expressions of the effective capacity at a given quality of service (QoS) exponent are derived for the renewal processes with either constant or variable reward. The simple expressions reveal meaningful insights, such as the monotonicity and bounds of the effective capacity. The analytical results are then applied to evaluate the cross-layer throughput for diverse hybrid automatic repeat request (HARQ) systems, including fixed-rate HARQ (FR-HARQ, e.g., Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR)), variable-rate HARQ (VR-HARQ), and cross-packet HARQ (XP-HARQ). Furthermore, aiming at maximizing the effective capacity via the optimal rate selection, it is disclosed that the VR-HARQ and XP-HARQ attain almost the same performance, and both of them perform better than FR-HARQ. In contrast, most of the prior outcomes approximate the effective capacity with the lack of insightful discussions that hampers the optimal design of HARQ systems. Finally, the numerical results corroborate the analytical ones.

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.

To facilitate green and secure communications, the energy efficiency (EE) and physical (PHY) layer security are desirable for cloud radio access network (C-RAN). However, the problem of EE optimization with guaranteed PHY layer security in C-RAN is challenging, especially in the presence of channel state information (CSI) uncertainty of both information receivers (IRs) and eavesdropper receivers (ERs). Thereby, in this paper, we investigate the worst-case EE maximization problem in a downlink C-RAN, subject to limited power budget of each BS and infinite number of PHY layer security constraints, which is a non-convex fractional programming problem and is NP-hard even in the event of perfect CSI. To solve this non-trivial problem, a fast-converging algorithm is developed. Specifically, based on successive convex approximation (SCA) technique, we transform the original problem into a semi-definite program (SDP) one, allowing for solving it iteratively. The tightness of the SDR is proved, and the SCA algorithm is also proved to converge to a Karush- Kuhn-Tucker point. Extensive simulations are carried out to verify the effectiveness of the proposed algorithm.

In this paper, we study the hybrid beamforming for the wideband mmWave MIMO-OFDM system with observation of beam squint. Firstly, we present the beam squint effect in the wideband mmWave system. We further characterize the mmWave wideband channel from Saleh-Valenzuela model. Secondly, in the full-connected hybrid architecture, we seek for the optimal hybrid precoder in the mmWave MIMO-OFDM system, which aims to maximize the spectral efficiency. The precoder design problem is formulated as the matrix factorization in this paper. To find the optimal precoder, wideband hybrid precoding (WHP) algorithm is proposed by using the manifold optimization. Finally, simulation results show the proposed algorithm could approximate to the optimal digital precoder in term of spectral efficiency for the wideband mmWave MIMO-OFDM system.

Applications on Location Based Services (LBSs) have attracted significant attention due to its personalized, convenient, and smart user experience, and meanwhile the accurate mapping and localization algorithm plays a crucial role in satisfying the LBSs. At the same time, motivated by the widely-deployed Wi-Fi network, the Wi-Fi signal based localization has become one of the superior positioning techniques in indoor environment, and the corresponding sample capacity involved in positioning database establishment should be given much attention due to its significant guidance meaning in practice. In this paper, we propose a new sample capacity optimization approach for indoor Wi-Fi localization from the information-theoretic view, namely Sample Capacity Optimization for Positioning database Establishment (SCOPE) in indoor Wi-Fi environment. Interestingly, we analogize the positioning database establishment process in indoor Wi-Fi environment into the information propagation process in a lossy channel, and meanwhile formulate the relations between the sample capacity and localization error. Experimental result shows that the proposed SCOPE can accurately estimate the minimum sample capacity with a given expected localization accuracy under different Access Point (AP) combination.

Non-orthogonal multiple access schemes with higher spectral efficiency and the capability of accessing more users than orthogonal multiple access (OMA) scheme is preferred for the fifth generation (5G) wireless networks. In this work, a non-orthogonal transmission scheme called pattern division multiple access (PDMA) is proposed for the 5G wireless communication systems. Two pattern matrices are proposed for 150% and 200% overloaded PDMA systems. Then, a low complexity symbolwise belief propagation iterative detection and decoding (BPIDD) algorithm for multiple user detection is proposed. Numerical results and analysis provided by utilizing extrinsic information transfer chart show that the proposed scheme outperforms the traditional OMA systems significantly, and the performance of the proposed scheme is better than that of the existing schemes up to 1 dB. Finally, simulation results show that the proposed scheme is superior to the existing schemes and the proposed BPIDD algorithm is better than that of the BP algorithm over 0.5 dB.

In order to reap the full scale of benefits of millimetre wave (mmWave) massive multiple‐input multiple‐output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm‐frequencies. In this study, the authors investigate the performance of two types of antenna array, namely uniform rectangular planar array (URPA) and uniform cylindrical array (UCYA). The channel behaviour is presented in full‐dimensional mmWave propagation conditions by considering both the azimuth and elevation dimensions. The squared inner product and singular value spread are studied for URPA and UCYA configurations, these properties reveal the effective interference and channel's stability of antenna array. The authors also evaluate the achievable rate with the equal power allocation and water‐pouring power allocation schemes. Simulation results show that under the same system configurations, the performance that includes the radiation pattern, the channel eigenvalue distribution, the effective interference, and the achievable rate, of UCYA configuration always outperforms that of URPA configuration. Therefore, it can be concluded that in three‐dimensional propagation environments, the UCYA configuration is especially appealing for mmWave MIMO systems.

We study the WiFi offloading problem in smart communications and adaptively seek for the optimal offloading strategies with the consideration of the mobility management and the dynamical nature of network state. With users mobility management, we formulate the offloading ratio optimization problem based on Markov process. Then, we propose a novel Congestion‐Optimal WiFi Offloading (COWO) algorithm based on subgradient method, which aims to obtain the optimal offloading ratio for each access point (AP) to maximize the throughput and minimize the network congestion. Due to the computational complexity of subgradient method, we further improve the COWO algorithm by the equivalent transformation. By viewing all the APs as one virtual WiFi network, we try to optimize the identical offloading ratio for virtual WiFi network and develop a Virtualized Congestion‐Optimal WiFi Offloading (VCOWO) algorithm with lower complexity. Under the equivalent conditions, the performance of the VCOWO algorithm could well approximate the optimal results obtained by the COWO algorithm. It is found that the VCOWO algorithm could obtain the upper bound of multiple APs WiFi offloading performance. Moreover, we investigate the impacts of user mobility on the WiFi offloading performance. Simulation results show that the proposed algorithm could achieve higher throughput with lower network congestion compared with other current offloading schemes.

Low cost and high efficiency, defined as energy efficiency (EE) and spectral efficiency (SE), have raised more and more attention in the fifth generation (5G) communication systems due to steadily rising hardware cost, energy consumption, and mobile traffic. This paper studies the hybrid architecture of multiuser massive MIMO systems, where the digital domain utilizes the zero‐forcing (ZF) precoding scheme and the analog domain uses discrete Fourier transform (DFT) processing that significantly reduces hardware cost and energy consumption. We derive analytical expressions on the total achievable SE and EE, as well as offering insight into some engineering parameters in the system performance. Our aim is to achieve low cost and high efficiency massive MIMO system, with constraints on the overall transmit power, the number of users, and the number of radio frequency (RF) chains. Results exhibit that the total achievable SE of the hybrid architectures with DFT precessing is inferior to the full digital architectures and hybrid architectures with the ideal phase shifters, but the performance attenuation can be compensated by providing the more input SNR and higher number of RF chains. Moreover, we find that the total achievable EE of hybrid architectures with DFT precessing outperforms other massive MIMO architectures that include a full digital implementation, ideal phase shifters, and a switched network.

In order to reap the full scale of benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm-frequencies. In this paper, we investigate the steering vector and array factor by considering three types of planar antenna arrays, namely uniform rectangular planar array (URPA), uniform hexagonal planar array (UHPA), and uniform circular planar array (UCPA). Based on these results, we investigate the array directivity/gain and the achievable spectral efficiency in a 3-dimensional massive MIMO system by considering both the azimuth and elevation dimensions. An important observation is that the the maximum array gain, the beamwidth, and the achievable spectral efficiency (SE) for the above three types of planar antenna array configurations are almost identical. The side lobe level and the geometric area of the UHPA configuration are systematically better than those of the UCPA and URPA configurations.

Ultra-dense cloud radio access networks (C-RANs) are an example of the architectures that will be critical components of the next-generation wireless systems. In a C-RAN architecture, an amorphous cellular framework, where each user connects to a few nearby remote radio heads (RRHs) to form its own cell, appears to be promising. In this paper, we study the ergodic capacity of such amorphous cellular networks at high signal-to-noise ratios (SNRs) where we model the distribution of the RRHs by a Poisson point process. We derive tractable approximations of the ergodic capacity at high-SNRs for arbitrary antenna configurations, and tight lower bounds for the ergodic capacity when the numbers of antennas are the same at both ends of the link. In contrast to prior works on distributed antenna systems, our results are derived based on random matrix theory and involve only standard functions which can be much more easier evaluated. The impact of the system parameters on the ergodic capacity is investigated. By leveraging our analytical results, we propose two efficient scheduling algorithms for RRH selection for energy-efficient transmission. Our algorithms offer a substantial improvement in energy efficiency compared with the strategy of connecting a fixed number of RRHs to each user.

This paper investigates the achievable spectral efficiency (SE) of millimeter wave downlink cellular systems accounting for both small and large-scale fading effects. The base station (BS) employs a uniform linear array (ULA) and maximal ratio transmission. We derive the expectation of the squared inner product for different channel links under the assumption that the users are randomly distributed within a circular-shaped cell. Using this result, a new lower bound on the achievable SE, valid for arbitrary numbers of antennas is derived. The analytical results show that the achievable SE increases with the number of BS antennas, whilst it converges to a saturated value in the high signal-to-noise ratio (SNR) regime and larger inter- antenna spacing.

We investigate the achievable ergodic sum-rate of multi-user multiple-input multiple-output systems in Ricean fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by utilizing the Mullen's inequality, which is then used to analyze the effect of channel mean information on the achievable sum-rate. With these results, a novel statistical-eigenmode space-division multipleaccess downlink transmission scheme is proposed. For this scheme, we derive an exact closed-form expression for the achievable ergodic sum-rate. Our results show that the achievable ergodic sum-rate converges to a saturation value in the high signal-to-noise ratio (SNR) region and reaches to a lower limit value in the lower Ricean K-factor range. In addition, we present tractable upper and lower bounds, which are shown to be tight for any SNR and Ricean K-factor value. Finally, the theoretical analysis is validated via numerical simulations.

In this paper, we investigate the achievable ergodic sum-rate for multiuser massive multiple-input multiple-output systems, where different array configurations such as uniform linear arrays (ULAs), uniform planar arrays (UPAs) and uniform circular arrays (UCAs), are deployed at the base station (BS). The investigation is carried out based on a three dimensional spatial propagation channel model by taking into account both the azimuth and elevation angular domains. We first investigate the value of the inner product of two channel vectors, for which closed-form expressions are derived with different array configurations in line-of-sight channel. After that, the effects of the BS antenna number, the angles of departure of users, and the inter-antenna spacing on the inner product, further, on the achievable ergodic rate, are investigated. Finally, theoretical results are verified via numerical simulations, which is shown that deploying ULAs outperforms the other array configurations (UPAs and UCAs) in terms of improving the achievable ergodic rate and the larger inter-antenna spacing can contributes substantially to the achievable sum-rate.

In this paper, we study the achievable ergodic sum-rate of multiuser multiple-input multiple-output downlink systems in Rician fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by using the Mullen's inequality, and then use it to analyze the effect of channel mean information on the achievable ergodic sum-rate. A novel statistical-eigenmode space-division multiple-access (SE-SDMA) downlink transmission scheme is then proposed. For this scheme, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable tight upper and lower bounds. Based on our analysis, we gain valuable insights into the impact of the system parameters, such as the number of transmit antennas, the signal-to-noise ratio (SNR) and Rician $K$-factor, on the system sum-rate. Results show that the sum-rate converges to a saturation value in the high SNR regime and tends to a lower limit for the low Rician $K$-factor case. In addition, we compare the achievable ergodic sum-rate between SE-SDMA and zero-forcing beamforming with perfect channel state information at the base station. Our results reveal that the rate gap tends to zero in the high Rician $K$-factor regime.