This paper proposes a centralized digital twin (DT)–driven framework for end-to-end route optimization in flying ad hoc networks (FANETs) that explicitly accounts for channel dynamics in delay-Doppler (DD) domain and residual unmanned aerial vehicles (UAVs)’ energy states. A DT server maintains a high-fidelity virtual representation of the physical FANET using compact DD channel descriptors and UAVs’ energy information, thereby reducing communication overhead while enabling proactive best route selection. In this context, the UAV routing problem is formulated as a centralized context-aware combinatorial bandit. The DT server will utilize combinatorial neural Thompson sampling (CN-TS) or combinatorial neural upper confidence bound (CN-UCB) algorithms to learn the non-linear relationship between UAVs’ dynamics and the achievable end-to-end transmitted data for selecting the optimal multi-hop route sequentially. Numerical results demonstrate that the proposed DT framework significantly outperforms conventional approaches in highly dynamic FANET scenarios.

Recently, robust technologies such as unmanned aerial vehicles (UAVs) and intelligent reconfigurable surfaces (IRSs) have demonstrated remarkable abilities to boost the coverage of limited-range millimeter wave (mmWave) communications. This paper handles the use of UAV-mounted IRS (U-IRS) to aid the mmWave base station (mWBS) in assisting clients/mobile users (MUs) positioned within hotspot regions. The UAV should wrap large-capacity hotspots while minimizing its aviation/hovering energy consumption. Hence, an energy-efficient advanced multi-armed bandit (MAB) scheme, named explore then commit based minimax optimal stochastic strategy (ETC-MOSS), is proposed as a practical self-learning technique to handle such issues intelligently. In this framework, the UAV (MAB player) selects hotspots (arms) to serve, aiming to maximize communication rate (reward) and minimize energy use (budget). Simulations demonstrated the superior performance of the envisioned ETC-MOSS scheme over classical MOSS and formal heuristic solutions according to mean rate and energy efficiency (EE), enabling future applications in the Metaverse, IoT, VANETs, and beyond.

The automotive industry has witnessed flourishing development in the last few years with the hope of achieving fully autonomous driving. In parallel, dual-function radar-communication (DFRC) systems are being developed to tackle the spectrum scarcity challenge and integrate radar and communication transceivers into a unified system. However, with a DFRC system, critical information may be intercepted by unauthorized people in the target vehicle. Therefore, the physical layer security of the DFRC system needs to be given considerable attention. To improve the security of information transmission, artificial noise (AN) is used to interfere with eavesdroppers. The main objective of this work is to maximize the secrecy rate of vehicles subject to both radar signal-to-interference-plus-noise ratio (SINR) and communication transmit power constraints. To overcome the difficulty caused by the existence of the nonconvex Shannon capacity formula in the formulated problem, we convert the secrecy rate optimization to an equivalent bi-convex optimization problem. Furthermore, due to the nonconvexity of the formulated design problem, we exploit an inexact block coordinate descent (IBCD) algorithm based on semi-definite Programming (SDP) to obtain the optimal solution. Moreover, to reduce the complexity of the SDP-based IBCD algorithm, a bi-search-based IBCD algorithm is employed for single target scenarios. Finally, numerical simulations are used to demonstrate the superiority of using the AN approach to enhance the performance of the DFRC system in terms of the secrecy rate.

This article presents accurate approximation expressions for the outage and secrecy outage probabilities of relay-assisted free-space optical (FSO) communication links utilizing unmanned aerial vehicles (UAVs). We consider the effects of weather attenuation and random fluctuations of UAVs’ orientations and positions. The obtained expressions are applicable to different types of intensity modulation direct detection techniques. We utilized$L$-ary multipulse pulse-position modulation$(L$MPPM) scheme and optimized link performance by adjusting the$L$MPPM settings using our derived expressions. We conduct numerical analyses to evaluate the impact of system settings, including beam divergence and modulation settings, on outage and secrecy outage probabilities. Our results demonstrate that adaptive$L$MPPM can improve average spectral efficiency, particularly in time-varying UAV-based FSO channels, while maintaining the same outage and secrecy outage probabilities as ordinary multipulse pulse-position modulation. Moreover, we investigate the effect of an eavesdropper’s location on the probability of a secure channel between legitimate users. We validate the accuracy of our expressions by comparing them with Monte Carlo (MC) simulation results under different channel conditions. Derived expressions provide efficient and accurate evaluation results to optimize the performance of UAV-based relay-assisted FSO communication systems.

This paper uses an aerial mounted RIS (A-RIS) to assist WiGig base station (BS) in serving mobile equipments (MEs) located within hotspot zones. The aerial should cover numerous large-capacity hotspots in this context while anticipating its flying/hovering energy expenditures. Hence, two advanced multi armed bandit (MAB) approaches, i.e. perturbed history exploration (PHE) and mini-max optimal Thompson sampling (MOTS), are envisioned as applicable self-learning methodologies to deal with such a problem effectively. Simulation results ensure the excellent performance of the envisioned schemes over naive upper confidence bound (UCB) and Thompson sampling (TS) algorithms and traditional heuristic solutions.

Recently emerging WiGig systems experience limited coverage and signal strength fluctuations due to strict line-of-sight (LoS) connectivity requirements. In this paper, we address these shortcomings of WiGig communication by exploiting two emerging technologies in tandem, namely the reconfigurable intelligent surface (RIS) and unmanned aerial vehicles (UAVs). In ultra-dense traffic sites (referred to as hotspots) where WiGig nodes or User Devices (UDs) experience complex propagation and non-line-of-sight (non-LoS) environment, we envision the deployment of a UAV-mounted RIS system to complement the WiGig base station (WGBS) to deliver services to the UDs. However, commercially available UAVs have limited energy (i.e., constrained flight time). Therefore, the trajectory of our considered UAV needs to be locally estimated to enable it to serve multiple hotspots while minimizing its energy consumption within the WGBS coverage boundaries. Since this tradeoff problem is computationally expensive for the resource-constrained UAV, we argue that sequential learning can be a lightweight yet effective solution to locally solve the problem with a low impact on the available energy on the UAV. We formally formulate this problem as a contextual multi-armed bandit (CMAB) game. Then, we develop the linear randomized upper confidence bound (Lin-RUCB) algorithm to solve the problem effectively. We regard the UAV as the bandit learner, which attempts to maximize its attainable rate (i.e., the reward) by serving distinct hotspots in its trajectory that we treat as the arms of the considered bandit. The context is defined as the hotspots’ locations provided using GPS (global positioning system) service and the reward history of each hotspot. Our proposal accounts for the energy expenditure of the UAV in moving from one hotspot to another within its battery charge lifetime. We evaluate the performance of our proposal via extensive simulations that exhibit the superiority of our proposed Lin-RUCB algorithm over benchmarking methods.

Abstract Due to the importance of both rate‐less digital fountain codes and Multi‐Input Multi‐Output (MIMO) schemes in Fifth and Sixth Generation (5G/6G) wireless networks, this paper investigates enhancing the performance of MIMO‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) based system by employing the rate‐less fountain codes. The rate‐less codes, such as the Tornado codes and Raptor codes, have a flexible code rate that differs from the classical channel coding schemes. This paper uses Raptor code to study the MIMO system's performance with the rate‐less codes over different wireless communications channels. The proposed MIMO‐fountain code‐based system has been tested over the various wireless communication channel conditions concerning the varied Frequency Doppler (FD). In the proposed rate‐less wireless communication system, multiple interleaving techniques and equalizers are used to improve its performance. Computer‐based simulation experiments are assigned to evaluate the MIMO‐fountain code system using the different data transmission scenarios. Simulation results confirm the superiority of the presented wireless rate‐less system compared to fixed‐rate wireless systems in terms of Bit Error Rate (BER) and throughput.

Abstract Millimetre wave (mmWave) communications, that is, 30 to 300 GHz, have intermittent short‐range transmissions, so the use of reconfigurable intelligent surface (RIS) seems to be a promising solution to extend its coverage. However, optimizing phase shifts (PSs) of both mmWave base station (BS) and RIS to maximize the received spectral efficiency at the intended receiver seems challenging due to massive antenna elements usage. In this paper, an online learning approach is proposed to address this problem, where it is considered a two‐phase multi‐armed bandit (MAB) game. In the first phase, the PS vector of the mmWave BS is adjusted, and based on it, the PS vector of the RIS is calibrated in the second phase and vice versa over the time horizon. The minimax optimal stochastic strategy (MOSS) MAB algorithm is utilized to implement the proposed two‐phase MAB approach efficiently. Furthermore, to relax the problem of estimating the channel state information (CSI) of both mmWave BS and RIS, codebook‐based PSs are considered. Finally, numerical analysis confirms the superior performance of the proposed scheme against the optimal performance under different scenarios.

To handle the exponentially growing service expectations from mobile users and circumvent the band switching slow rate, device-to-device (D2D) communication is receiving much research attention in the Internet of Things (IoT). While the emerging D2D nodes can support heterogeneous frequency bands [radio frequency (RF) including 2.4 GHz/5 GHz wireless local area network (WLAN), 38-GHz millimeter wave (mmWave), and visible light communication (VLC)], the physical constraints (e.g., blocking) require the user devices to dynamically switch between the bands in order to avoid the loss of connectivity and throughput degradation. In this article, we investigate an effective online link selection in hybrid RF-VLC scenarios for direct user data handling. First, we model the multiband selection issue as a multiarmed bandit (MAB) problem. The source/relay node acts as a player who gambles to maximize its long-term feedback/reward via selecting suitable arms, i.e., available bands (WLAN, mmWave, or VLC). Then, we propose an online, energy-aware band selection (EABS) methodology by leveraging three theoretically guaranteed MAB techniques [upper confidence bound (UCB), Thompson sampling (TS), and minimax optimal stochastic strategy (MOSS)] to derive optimal band selection policies. Based on these adopted policies, we propose three algorithms, namely, EABS-UCB, EABS-TS, and EABS-MOSS, to implement the EABS strategy, respectively. Extensive simulations demonstrate our proposed algorithms’ superior performance compared to the traditional link selection schemes regarding energy efficiency, average throughput, and convergence rate. In particular, EABS-MOSS emerges as the best algorithm as it exhibits near-optimal performance due to its flexibility to both stochastic and adversarial environments.

While hybrid-band wireless systems recently gained prominence to achieve high capacity, selecting the best channel in these systems in real-time is still a formidable research challenge that requires further investigations. In this paper, we address this challenge in terms of an optimization problem, which is reformu-lated as a stochastic multi-armed bandit (MAB). Then, we introduce online learning-based solutions to solve the MAB problem for the multi-band/channel selection (MBS). Improved variants of the upper confidence bound (UCB) scheme are investigated and modified to be energy-aware. Hence, we propose Energy-Aware Randomized UCB-MBS (EA-RUCB-MBS) and Energy-Aware Kullback-Leibler UCB-MBS (EA-KLUCB-MBS) methods, which demonstrate near-optimal results. Also, EA-KLUCB-MBS exhibits the fastest convergence, while the convergence of EA-RUCB-MBS is similar to that of the original UCB. Based on extensive simulation results, we evaluate the performance of our proposed algorithms against benchmark MBS schemes including UCB and Thompson sampling (TS).

Relaying is a key technology for millimeter wave communications to extend the range and to route around blockages. In practical implementation of relaying systems, probing is required to identify proper neighbor terminals which will serve as relays. There is however an inherent trade-off between relay probing and required overhead. In this paper, we consider WiGig (IEEE 802.11ad) devices, which are multiband capable with Wi-Fi support, and propose a Wi-Fi assisted relay probing for WiGig device-to-device networks. In the proposed scheme, Wi-Fi received signal strengths are used to pre-select the WiGig relays expected to maximize the spectral efficiency of the overall system. Then, only these pre-selected relays are used in online relay probing step. Simulation analysis demonstrate improvements in throughput and energy consumptions over existing relay probing schemes.

In this study, joint users selection and beamforming based on users positioning are proposed for enabling non‐orthogonal multiple access (NOMA) in the millimetre‐wave (mmWave) transmissions. In the proposed scheme, the mmWave users are arranged in descending order based on their estimated distances from the mmWave base station (BS), which guarantees the users ordering based on their line‐of‐sight availability, as well. Out of the ordered users, the two users having the minimum pairing angle and expected to lie within the specified mmWave beamwidth are selected as the two NOMA users. In this study, the users' pairing angle is defined as the maximum expected difference angle between two users considering their location‐uncertainty areas. Consequently, the BS antenna beam is formed towards the two selected NOMA users by adjusting its boresight angle to lie in between their estimated location angles. Then, the fixed power allocation scheme is used to adjust the power allocation coefficients of the selected NOMA users. Mathematical analysis is performed to bound the performance of the proposed mmWave NOMA scheme. Also, numerical simulations are conducted to prove the mathematical findings and to show the superior performance of the proposed scheme over the well‐known technique of using random beamforming.

Fast session transfer (FST) is introduced by wireless gigabit (WiGig) standards to transfer the data communication session among wireless fidelity (Wi‐Fi) and WiGig channels based on the availability. In the conventional Wi‐Fi/WiGig FST decision‐making, the session of data transmission is transferred from Wi‐Fi to WiGig whenever it is possible regardless of the traffic loads on both bands and the expected WiGig blocking probability. Also, WiGig to Wi‐Fi FST occurs whenever the WiGig signal is lost even if the WiGig link is undergoing an instant path blocking. This results in Wi‐Fi/WiGig load imbalance as well as large average delay in user data delivery. In this study, a novel Wi‐Fi/WiGig FST decision‐making algorithm is proposed utilising fuzzy logic while considering the coverage probability of the WiGig link, the sizes of the users' remaining messages relative to the available data rates of both bands, and the probability of WiGig path blocking. Furthermore, a WiGig interruption‐classification methodology is proposed to detect the occurrence of the WiGig instant path blocking and then decide if it is better to wait for WiGig signal recovery or immediately transfer to Wi‐Fi. Simulation analysis demonstrates the effectiveness of the proposed Wi‐Fi/WiGig FST decision‐making algorithm, over the conventional one.

Analogue beamforming is normally used in conjunction with millimetre wave (mmWave) communications to overcome the high propagation and penetration losses inherent in mmWave transmissions. Thus, producing a high‐efficient mmWave beamforming using low beamforming training (BT) complexity turns to be a big challenge towards ubiquitous mmWave communications. In this study, low‐complex and high‐efficient adaptive mmWave BT using compressive sensing (CS) based channel estimation is introduced utilising mobile station (MS) localisation. In which, MS positioning is used to estimate the ranges of angle of departures and angle of arrivals of the mmWave channel considering the statistics of its angular spread. Then, an adaptive multi‐level beam search using CS‐based channel estimation is used to estimate the best transmit/receive beam for establishing the mmWave link. Thus, the antenna weight vectors of each beam searching level, i.e. the beamwidth and the number of beams, used for constructing the sensing matrix are adaptively adjusted. The high potency of the proposed mmWave BT scheme over the conventional ones in both BT complexity and performance is proved by the means of mathematical and simulation analysis.

Underwater wireless communications (UWCs) play a major role in both civilian and military applications. The features of the underwater transmission media are big challenges towards the design of efficient and robust UWCs. Many types of propagation waves have been investigated for UWCs such as acoustic, electromagnetic, optical and magnetic induction. Underwater acoustic communications have long transmission range, but with limited data rate and high-power consumption. Meanwhile, electromagnetic and optical UWCs can achieve high data rate but with difficulties in establishing reliable communication links due to their short communication distances and alignment problem respectively. Recently, magnetic induction has been introduced as a new physical layer for UWCs providing unique features such as high data rate and low propagation delay, but it still suffers from high propagation losses especially in sea water. In this paper, the performance of these different UWCs paradigms are explored and compared in terms of energy efficiency and total system throughput under different scenarios. Then, a hybrid UWC network based on an interoperability among these different technologies is suggested.

Integrating the ultra-high capacity millimeter wave (mmWave) band into the existing cellular network is one of the main challenges towards a unified multi-Gbps 5G network. In this paper, a novel hierarchical multi-band heterogeneous network (HetNet) is proposed to efficiently overlay mmWave small cells (SCs) over the legacy LTE eNB. By which, a medium coverage band is used to precisely control the access over the small coverage mmWave band via the proposed concept of sub-clouds; while, the large coverage LTE eNB only controls the access over the distributed sub-clouds. Without loss of generality, Wi-Fi band is used as the medium coverage band in this paper. Consequently, a novel concept of 2C/U plane splitting is introduced. The C1 plane carried by the LTE eNB controls the access between the LTE eNB and the sub-clouds. Whereas, the C2 plane carried by the medium coverage (Wi-Fi) band controls the access among the mmWave SCs inside each sub-cloud. By this distributed control, associated signaling/processing load on both core network (CN) and LTE eNB can be highly relaxed. Analytical and numerical analysis confirm performance superiority of the proposed LTE/Wi-Fi/mmWave HetNet over the conventional LTE/mmWave HetNet in terms of handover (HO) signaling delays and mmWave band usability.

In this paper, a low complexity MIMO detection is proposed for MIMO-Constant Envelope Modulation (MIMO-CEM) transceiver system. In which, an adaptive sphere decoder (SD) based on an affine linear transformation model is proposed to adaptively select a small number of candidate sequences from all lattice states. Then, the more accurate (the more complex) IF based maximum likelihood MIMO-CEM decoder (MLD) is used to estimate the transmit sequence that the most likely to be transmitted from the selected candidates.

Although Wireless Gigabit (WiGig) access operating in the 60 GHz band plays a significant role towards multi-Gbps WLANs, its transmission suffers from harsh propagation loss and path blocking reducing its transmission range to be few meters around a WiGig access point/station (AP/STA). Consequently, directional transmissions using antenna beamforming is tremendously used in WiGig communication. In this paper, a novel approach of leveraging Wi-Fi channel fingerprints for localizing WiGig coverage area along with reducing its beamforming training (BT) complexity over conventional exhaustive search BT is proposed. The proposed approach is motivated by the fact that Wi-Fi fingerprints, WiGig coverage area and the best beam identifications (IDs) of a WiGig AP and STA are all location dependent. Hence, by linking Wi-Fi fingerprints with WiGig information, e.g., WiGig coverage and the best AP/STA beam IDs, using statistical learning, Wi-Fi fingerprints comparisons can be used to detect if a WiGig STA is within the coverage area of a WiGig AP or not and which AP/STA beam IDs are expected to maximize the link quality. Experimental work in real indoor environment is conducted to prove the effectiveness of the proposed approach compared to the conventional exhaustive search BT.

5G and beyond will occupy a big position in wireless communication future, because of the high end-user data rate over the Internet demand. Millimeter wave (mmWave) plays the major role as a 5G networks enabler. To overcome mmWave band propagation losses and increase its coverage area, beamforming training (BT) is typically used in conjunction with mmWave transmissions. IEEE 802.11ad standard defines an exhaustive search BT to find out the best transmit (TX)/receive (RX) beam identifications (IDs). Narrow, i.e., pencil, beams guarantee a high gain, i.e., a wide coverage area, but at the expense of very high setup time due to the exhaustive searching process. Localization provides a promising solution for finding out the best mmWave beams within a small setup time. However, localization techniques having high localization errors compared with the coverage area of the sharp beams (e.g., 5 degrees) are not suitable for these scenarios. Motivated by the high publicity of light emitting diode (LED) lambs and their high accurate positioning (e.g., around 10 cm), a mmWave BT technique based on light fidelity (Li-Fi) multilateral positioning is proposed in this paper to work efficiently in mmWave sharp beam scenarios. Via mathematical and simulation analysis, the proposed mmWave BT achieves a few number of beam switchings with a comparable performance with the exhaustive search BT. Besides, it achieves a lower outage probability than that comes from using Wi-Fi Localization.

Wireless Gigabit (WiGig) access points (APs) using 60 GHz unlicensed frequency band are considered as key enablers for future Gbps WLANs. Due to its short range transmission with high susceptibility to path blocking, a multiple number of WiGig APs should be installed to fully cover a typical target environment. However, using autonomously operated WiGig APs with IEEE 802.11ad DCF, the exhaustive search analog beamforming and the maximum received power based autonomous users association prevent the establishment of optimal WiGig concurrent links that maximize the total system throughput in random access scenarios. In this paper, we formulate the problem of WiGig concurrent transmissions in random access scenarios as an optimization problem, then we propose a Wi-Fi/WiGig coordination architecture to solve it. The proposed coordinated Wi-Fi/WiGig WLAN is based on a tight coordination between the 5 GHz (Wi-Fi) and the 60 GHz (WiGig) unlicensed frequency bands. By which, the wide coverage Wi-Fi band controls the establishment of the WiGig concurrent links. Statistical learning using Wi-Fi fingerprinting is used for estimating the best candidate AP and its best beam identification (ID) for establishing the WiGig concurrent link without making any interference to the existing WiGig data links.

Wireless Gigabit (WiGig) access points (APs) using the 60 GHz unlicensed frequency band are considered as key enablers for future Gbps wireless local area networks (WLANs). Exhaustive search analog beamforming (BF) is mainly used with WiGig transmissions to overcome channel propagation loss and accomplish data transmissions. Due to its short range transmission with high susceptibility to path blocking, a multiple number of WiGig APs should be installed to fully cover a typical target environment. Therefore, coordination among the installed APs is highly needed for enabling WiGig concurrent transmissions while overcoming packet collisions and reducing interference, which highly increases the total throughput of WiGig WLANs. In this paper, we propose a comprehensive architecture for coordinated WiGig WLANs. The proposed WiGig WLAN is based on a tight coordination between the 5 GHz (WiFi) and the 60 GHz (WiGig) unlicensed frequency bands. By which, the wide coverage WiFi band is used to do the signaling required for organizing WiGig concurrent data transmissions using control/user (C/U) plane splitting. To reduce the interference to existing WiGig data links while doing BF, a novel location based BF mechanism is also proposed based on WiFi fingerprinting.

Increasing wireless cellular networks capacity is one of the major challenges for the coming years, especially if we consider the annual doubling of mobile user traffic. Towards that and thanks to the fact that a significant amount of mobile data is indeed delay tolerable, in this paper, we suggest embedding the delayed offloading of some user traffic to be a part of future wireless cellular networks. To accomplish this, user delayed files will be offloaded using ultra-high speed Millimeter Wave (Mm-W) gates. The Mm-W gate, which will be distributed inside the Macro basestation (BS) area, consists of number of Mm-W Access Points (APs) controlled by a local coordinator installed inside the gate. To effectively manage the delayed offloading mechanism, utilizing the concept of User/Control (U/C) data splitting, the gates coordinators and the Macro BS are connected to the Cloud Radio Access Network (C-RAN) through optical fiber links. Also, files offloading organizer software is used by the User Equipment (UE). A novel weighted proportional fairness (WPF) user scheduling algorithm is proposed to maximize the Gate Offloading Efficiency (GOFE) with maintaining long term fairness among the different mobility users pass through the gate. If the gate is properly designed and the files delay deadlines are properly set; near 100% GOFE with average reduction of 99.7% in UE energy consumption can be obtained, in time the user just passes through the gate.

The Remotely Keyed Encryption Schemes (RKESs) are greatly useful in solving the vital problem of how to do bulk encryption/decryption for high-bandwidth applications (like multimedia data encryption) in a way that takes advantage of both the superior power of the host and the superior security of the smart card. Accordingly, we propose two novel RKESs, a Length Preserving (LP) RKES and a Length Increasing (LI) RKES. The proposed LPRKES is based on a general view of the well known Feistel-Network (FN) in which we only use two rounds in an efficient and secure way, employing data-dependent cipher-keys. In the LIRKES, an extra ciphertext block is used, encrypting the one-time random cipher-key based on a signature of the whole ciphertext. The security of the presented schemes are theoretically proven and, compared to the state-of-the-art RKESs, the savings in the required computational complexity, run-time, bandwidth, and storage space are emphasized.

The authors have proposed Multi-Input Multi-Output (MIMO)-Constant Envelope Modulation, (MIMO-CEM), as power and complexity efficient alternative to MIMO-OFDM, suitable for wireless backhaul network in which relay nodes are fixed in their positions. One of the major problems to withstand real application of MIMO-CEM is to estimate MIMO channel characteristics. The MIMO-CEM is based upon two contrary schemes; one is the nonlinear CEM modified Maximum Likelihood Sequence Estimator (MLSE) proposed by the authors, which needs accurate channel information to replicate the received signal passing through channel. The other is low resolution analog-to-digital converter (ADC), i.e., 1-bit in the default operation and 2 or 3 bits in the optional operations, which means that the received channel amplitude information may be completely or partially destroyed. These two contrary demands make MIMO-CEM channel estimation a big challenge; how we can accurately estimate the channel in these severe low ADC resolution conditions. We consider this issue through designing an efficient MIMO-CEM channel estimator based upon a block based adaptive filter. In addition, in order to speed up the parameter convergence rate in the proposed adaptive estimator, we design a correlator estimator as initial channel state estimation used in the adaptive estimator. We prove the effectiveness of the proposed MIMO-CEM channel estimation under different channel scenarios and different low ADC resolutions such as 1, 2 and 3-bit.