Fluid antenna system (FAS) technology can further improve the performance by changing the antenna position and shape over a given space dynamically. In this paper, a FAS-assisted orbital angular momentum (FAS-OAM) communication system is proposed, in which the base station (BS) transmits OAM signals with multiple modes to a receiver equipped with fluid antennas. In order to analyze the performance of the proposed system accurately, the block-correlation model is employed to construct the channel correlation of FAS with low complexity. Then, the outage probability and ergodic capacity are derived based on the assumption of the non-central chi-square distribution, and the Gauss-Laguerre quadrature method is adopted to obtain closed-form results. Simulation results show that the derived theoretical approximate and closed-form results of outage probability and ergodic capacity are consistent with corresponding simulation results. Compared with the conventional OAM and MIMO systems, the proposed FAS-OAM system exhibits significant performance advantages in terms of outage probability and ergodic capacity, and confirms the effectiveness of FAS-OAM over fading channels.

This paper explores the challenges of user equipment (UE) association and downlink power control in scalable cell-free massive multiple-input multiple-output (MIMO) systems. Initially, we develop a scalable UE association algorithm, which ensures that each UE is connected to only a small set of access points (APs). This approach effectively reduces both fronthaul requirements and the computational load on the APs. The algorithm employs a competition-based mechanism to ensure that APs efficiently distribute workloads while satisfying the quality of service (QoS) requirements of UEs, preventing them from losing network connectivity. Second, we introduce a distributed downlink power control method based on deep neural networks (DNNs) to improve the long-term downlink spectral efficiency (SE) of the entire network. This method relies solely on locally collected large-scale fading information as DNN input, making it adaptable to dynamic scenarios involving varying numbers of associated UEs. To further enhance the training efficiency of the DNN, we design a distributed training framework that fully leverages the computational resources of distributed edge processors (EPs). Simulation results show that the proposed UE association algorithm and downlink power control method exhibit significant advantages over the benchmarks.

A novel semi-orthogonal transmission method, the pilot data superimposed dynamic inflow (PDSDI) method, is proposed to tackle the challenges of massive access in grant-free random access wireless communication systems. This approach aims to balance large-scale user access while mitigating performance degradation associated with non-orthogonal pilot sequences. The traditional orthogonal transmission scheme is also analyzed as a baseline for comparison. Uplink rate expressions in closed form are obtained under two common channel estimation methods, that is, least squares and minimum mean square error. The analysis investigates the asymptotic behavior of important system parameters, including RiceanK-factor, antenna numbersM, and symbol power, revealing that the sum uplink rate converges to log2M, regardless of theK-factor. In addition, a parameter optimization model is developed to maximize system performance by adjusting critical variables, such as antenna number and power scaling factors, ensuring improved system capacity and efficiency. The simulation results confirm that the PDSDI method greatly exceeds the performance of the traditional scheme with respect to uplink rate and user access capacity. This work presents a practical framework for optimizing system parameters in future large-scale wireless networks.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) is one of the most promising technologies from 5G-based Internet of Things (IoT) to future wireless communication-based IoT, which usually relies on beamforming codebooks for data transmission. However, traditional codebooks often consist of numerous narrow beams, which causes substantial training overhead. Although centralized machine learning-based methods can address this issue to some extent, they overlook minority IoT devices scattered across various areas, which is vital for the coverage equity of the environmental adaptive codebook and the optimal average achievable rate. To circumvent the problem, we propose a distributed learning (DL) framework for codebook design in mmWave massive MIMO systems with uneven user distribution. Specifically, the user channel set is first divided into subsets by pre-classification based on the power responses of the featured combining vectors from different subregions. Then, a novel DL architecture processes these subsets, each assigned to different baseband processing boards (BPBs) in building baseband units, alleviating the centralized machine learning burden on the active antenna unit (AAU) or its directly connected BPB. Meanwhile, the current algorithms lack the hardware perspective or only implement the inference stage of the model. Thus, we deploy an FPGA-adapted DL-based codebook training prototype that runs on FPGA, which fully explores the “Backward-While-Forward" strategy for data reuse in the forward and backward passes. Simulation validates the effectiveness of distributed learning. Notably, the FPGA implementation on the embedded-level board outperforms consumer-grade CPU and GPU in terms of both latency and energy efficiency.

Resource allocation (RA) has been considered as a key technique to achieve the optimal system performance in symbiotic radio (SR) systems by optimizing system parameters. However, most of the existing works only consider the ideal hardware conditions or perfect channel information, where the system performance of the above algorithms may be degraded under imperfect channel state information (e.g., channel estimation errors) and unideal hardware conditions (e.g., distortion noises). In order to improve transmission robustness and information security, in this paper, we study the robust secure RA problem for a multiple-input single-output SR system under channel uncertainties and hardware impairments (HWIs) with an eavesdropper. The robust RA problem with bounded channel uncertainties is formulated to maximize the total energy efficiency (EE) of the system under the minimum energy-harvesting constraint of each backscatter device (BD), the maximum transmit power constraint of the primary base station, the minimum secrecy rate of each BD, the decoding constraint, as well as the reflection coefficient constraint. To address the non-convex optimization problem, the original robust RA problem with the infinite constraints is converted into a deterministic one via a worst-case approach. Then, the objective function is transformed into a non-fractional form by using the Dinkelbach’s method. After that, the above problem is converted into a convex problem based on S-Procedure and the eigenvalue decomposition approach, and an iterative-based robust secure RA algorithm is proposed via an alternating optimization principle. Simulation results demonstrate that the proposed algorithm has lower outage probabilities and higher EE compared to the non-robust algorithm and the RA algorithm without HWIs.

In this paper, we investigate the frequency domain phase noise estimation in millimeter wave filter-bank multicarrier with offset quadrature amplitude (mmWave FBMC-OQAM) systems. In the frequency domain, the primary impairment introduced by phase noise is the common phase error (CPE), whose estimation performance is significantly affected by the inter-carrier interference (ICI) and inter-symbol interference (ISI) experienced by OQAM symbols. To address the above problem, we propose novel frequency domain phase noise estimation and pilot symbol design methods to mitigate the impact of ICI and ISI. Firstly, we quantify the interferences affecting each pilot symbol and design appropriate weights to mitigate the impact of ICI and ISI on the phase noise estimation. Then, through analysis, we observe that the ICI and ISI originate from the interaction between the imaginary intrinsic interferences and the phase noise terms. Accordingly, we propose a pilot symbol design method by eliminating the primary imaginary intrinsic interferences, thereby reducing the ICI and ISI. Furthermore, we analyze the mean squared error (MSE) lower bound and the computational complexity. Simulation results demonstrate that the proposed phase noise estimation method outperforms the traditional method and the proposed pilot structure further improves the accuracy of the phase noise estimation.

This article studies the cell-free massive multi-input multi-output (MIMO) two-way relay systems with low-resolution analog-to-digital converters (ADCs). Primarily, we analyze the normalized mean square error (NMSE) and derive a closed-form expression for NMSE’s expectation${\mathrm {Exp}}_{\mathrm {nmse}}$. Asymptotic analysis showcases that, with an infinite number of access point (AP) antennas M or ideal ADCs,${\mathrm {Exp}}_{\mathrm {nmse}}$converges to a finite value. Particularly, as pilot power decreases with M in a power law, the channel estimation performance can be maintained at a desired level. Secondly, we derive two closed-form expressions of spectral efficiencies (SE) for multiple-access channel (MAC) phase and broadcasting (BC) phases, respectively. The corresponding analysis implies that, as AP number$L\rightarrow \infty $, the SE tends to infinity with MAC phase and approaches to constant with BC phase. Interestingly, all SE expressions can reduce to the conventional cases with high-resolution ADCs. Finally, based on geometric programming, an effective power successive approximation (PSA) scheme is provided to maximize the sum SE. Results prove that the proposed PSA scheme can significantly improve the SE compared to baseline benchmarks, particularly for median AP antenna regime. All the derived closed-form expressions are validated through Monte Carlo simulations.

The performance of mean square error (MSE) in channel estimation for multi-cell massive multiple-input multiple-output (MIMO) systems with Rician fading is studied. In this report, we initially derive the closed-form expressions of the probability distribution function and cumulative distribution function of MSE, which are applicable for any number of base-station antennas M and any Rician K-factor. Furthermore, we perform an asymptotic analysis for both strong line-of-sight (LOS) and Rayleigh fading scenarios. Subsequently, we present closed-form expressions for the expectation of MSE $\left(\operatorname{Exp}_{\mathrm{mse}}\right)$ and the variance of MSE. Next, utilizing maximal-ratio combining detector, we investigate the relationship between achievable downlink spectral efficiency and $\operatorname{Exp}_{\mathrm{mse}}$. It is observed that as $\operatorname{Exp}_{\text {mse }}$ increases, the achievable downlink spectral efficiency constantly reduces, eventually reaches a given constant. Finally, Monte-Carlo simulations are performed to corroborate the results discussed earlier.

The spectral efficiency in cell-free massive MIMO with grant-free random access under Ricean fading, utilizing a linear maximal-ratio combining detector, is the focus of this paper. Firstly, explicit expressions for the effective signal-to-interference-plus-noise ratio (SINR) are introduced, using the least squares (LS) and minimum mean squared error (MMSE) estimation methods, valid for varying Ricean K-factor and for varying numbers of access point antennas M. Furthermore, we drive the asymptotic SINR expressions under extreme conditions of infinite Ricean K-factor, infinite M, and the appropriate scaling of the users’ power. The corresponding analysis shows that as M approaches infinity, the sum uplink rate converges to $\log _{2} M$, regardless of the Ricean K-factor. Particularly, in the case of LS estimation, reducing each user’s transmit power by $1 / \sqrt{M}$ maintains the rate, converging to a definitive value irrespective of the Ricean K-factor. In the case of MMSE estimation, power is able to be scaled down by $1 / \sqrt{M}$ to achieve it when Ricean K-factor is zero, whereas the power of nonzero Ricean K-factor necessitates a reduction by $1 / M$. Finally, simulations confirm all theoretical outcomes.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) technology represents a promising technology in wireless communication. This technology relies on beamforming codebooks for initial access and transmission. However, conventional codebooks comprise a multitude of single-lobe narrow beams, resulting in redundant beams that may never be utilized in beam training. While centralized machine learning methods can partially address the concern of redundancy, they tend to overlook the presence of minority users scattered across diverse regions. The equitable coverage of environmental adaptive codebooks depends on addressing this issue. Hence, we devise a distributed learning (DL) framework for codebook design, which is tailored for scenarios with uneven user distribution and fully exploits the decentralized and online learning features of DL. Our approach begins by segmenting the user channels into various subsets through a pre-classification process. Then, we introduce a novel DL architecture designed to process the subsets that are assigned to individual user equipments (UEs). Each UE then generates a phase shift matrix that contributes to the concatenation-based global aggregation in the base station. The simulation results confirm the effectiveness of DL in improving the performance of mmWave massive MIMO systems in scenarios with unevenly distributed users.

This paper studies the downlink spectral efficiency of a two-way cell-free massive multiple-input multiple-output system with few-bit analog-to-digital converts (ADCs). By utilizing minimum mean squared error channel estimation method and maximal-ratio transmission precoder, we derive a closed-form expression for the effective downlink signal-to-interference-plus-noise ratio (SINR), which applies to any number of access point (AP) antennas M and distortion factors of ADCs in both AP and user pair sides. Additionally, the obtained analytical expression specializes to the conventional one when the ideal ADCs are adopted. Moreover, the asymptotic performance and power scaling law of the effective SINR in high M regime are studied. The corresponding analysis indicates that increasing M can provide considerable gains to compensate the rate loss caused by non-ideal ADCs and also, appropriately cutting down the pilot power and transmission power will not affect the SINR performance in high M region. Finally, all the theoretical results are verified via simulations.

This paper studies the trade-off between the comunication capacity of unmanned aerial vehicle (UAV) and the UAV's unintentional interference in a shared spectrum scenario. In fact, UAV will also cause unintentional interference to other ground users (GUs) who do not communicate with the UAV while transmitting data to a specific ground node (GN). And the location of these GUs is usually unknown. For this problem, we introduce the concept of unacceptable area in which the interference power received by GUs from UAV exceeds their anti-jamming tolerance, and study the trade-off between UAV's average communication capacity and average unacceptable area by joint UAV's 3D trajectory and transmit power optimization. Further, we propose an effective iterative algorithm to solve this non-convex problem by using successive convex approximation (SCA) and block coordinate descent (BCD) method. Numerical results show the proposed scheme can meet the communication requirements of UAV and reduce the UAV's unintentional interference at the same time.

This article analyzes the channel estimation performance of massive multiple-input-multiple-output (MIMO) Internet-of-Things (IoT) systems with Ricean fading. First, by utilizing the least squares (LSs) and minimum mean squared error (MMSE) estimation methods, we consider the relative channel estimation error (RCEE) between the IoT device and base-station, and provide the approximations of the expectation of RCEE ( Exprcee). Then, it is found that when the number of antennas M becomes infinite, pilot contamination (PC) exists in both cases. However, for MMSE case, Exprceescales down by the inverse of Ricean K-factor, and hence PC phenomenon disappears with a large Ricean K-factor. Moreover, as M→ ∞, the power scaling laws show that the pilot sequence power can be scaled down proportionally to 1/Mα( α > 0) with the MMSE case, where the performance is determined only by the Ricean K-factor. Next, the channel hardening and favorable propagation effects are examined via analyzing the approximations of the variance of RCEE ( Varrcee). Analysis implies that Varrceedecreases by 1/M when M→ ∞. For a large Ricean K-factor, Varrceeapproaches a nonzero constant for the LS case and scales down by the inverse of the square of Ricean K-factor for the MMSE case. Finally, all results are verified via Monte Carlo simulations.

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

In this paper, by considering the average mean squared error (AMSE) of channel estimation, we primarily obtain the closed-from expressions of the probability density function (PDF) and cumulative distribution function of AMSE for the least squares (LS)/minimum mean squared error (MMSE) estimation method as the line-of-sight (LOS) component is known, where the asymptotic analysis is executed in Rayleigh fading and strong LOS conditions. Secondly, the closed-form expressions for the expectation of AMSE ( Expamse) and variance of AMSE ( Varamse) are acquired, where Varamseis inversely proportional to the number of antennas ( M). As M becomes infinite, the PDF of AMSE at Expamsehas an order of root M. When the pilot power decreases with M in a power law, the LS case keeps deteriorating while the MMSE case converges to a constant which basically depends on the Rician K-factor. Next, the spectral efficiency is investigated by considering AMSE. When Expamseaccelerates, the spectral efficiency of the LS method keeps dropping and that of the MMSE method firstly is degraded and then is improved to a constant except Rayleigh fading. Finally, all results are validated via simulations.

The massive Machine Type Communications (mMTC) is one of the three generic services to be supported by 5G wireless systems. To fulfill the ever-increasing network access demand from a large number of Machine Type Devices (MTDs), this paper develops a cluster-based group paging scheme. Specifically, with the proposed scheme, MTDs are divided into clusters and the group paging period is decomposed into two parts: intracluster access period and inter-cluster access period. In the intracluster access period, preamble reuse is adopted for facilitating the access request transmissions from MTDs in each cluster to its cluster head. In the inter-cluster access period, only cluster heads send access requests to the base station.To evaluate and optimize the access efficiency of the proposed scheme, the probability of successful access of each MTD is characterized, based on which the maximum probability of successful access and the corresponding optimal number of clusters and optimal length of the intra-cluster access period are obtained as explicit functions of key system parameters including the number of preambles and the number of MTDs. A comparative study of the access efficiency for group paging with clustering and without clustering is conducted, which reveals the critical threshold in terms of the number of MTDs, above which clustering is beneficial. The analysis is verified via extensive simulations. It is shown that the access performance of the proposed scheme significantly outperforms that of the traditional group paging scheme, especially in massive access scenarios.

In this paper, we firstly derive two approximations of the achievable uplink rate with the perfect/imperfect channel state information (CSI) in cell-free massive multi-input multi-output (MIMO) systems, and all these approximations are not only in the simple, but also converge into the classical bounds achieved in conventional massive MIMO systems where the base-station (BS) antennas are co-located. It is worth noting that the obtained two approximations with perfect CSI could be regarded as the special cases of the obtained two approximations with imperfect CSI when the pilot sequence power becomes infinite, respectively. Moreover, the theory analysis shows that all obtained approximations with perfect/imperfect CSI have an asymptotic lower bound α/2 log2L thanks to the extra distance diversity offered by massively distributed antennas, where L is the number of BS antennas and the path-loss factor α > 2, except for the free space environment. Obviously, these results indicate that the cell-free massive MIMO system has huge potential of spectral efficiency than the conventional massive MIMO system with the asymptotically tight bound log2L.

In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation Exprcee and the achievable uplink rate holding for any number of base station antennas M, with the least squares (LS) and minimum mean squared error (MMSE) methods. It is found that RCEE and Exprcee converge to the same constant value when M → ∞, which renders the pilot power allocation (PPA) substantially simplified and a PPA algorithm is proposed to minimize the average Exprcee per user under a total pilot power budget F in multi-cell massive multipleinput multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are significant only with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large F, the performance of the LS approaches to that of the MMSE. Besides, a scheduling strategy is proposed to allocate pilot power in the whole system, which can approach the optimal performance. For the achievable uplink rate, the PPA scheme and improves the minimum achievable uplink rate compared with the EPPA scheme.