Massive MIMO technology offers a promising solution for supporting the simultaneous connectivity of a large number of ultra high-density massive IoT devices. However, due to limited resources, effective channel estimation becomes a challenge. One approach is to reuse the orthogonal reference signal (ORS) in a repetitive manner, while another option is to employ random non-orthogonal reference signals (NORS) for distributed massive IoT devices. In order to enhance performance in the face of high RS congestion from massive IoT devices, the strategic utilization of several critical resources becomes imperative. In this paper, we delve into the methodology of resource management to effectively address the severe high-density scenario of massive IoT devices. Specifically, we examine the system bandwidth, which proves particularly advantageous in bandwidth-limited environments. Exploiting the spatial domain, we leverage the distinctive features of massive MIMO to enable simultaneous parallel transmission by increasing the number of service antennas at the base station (BS). Furthermore, we explore the potential benefits of sectorization, a technique that involves dividing a circular cell into multiple sectors, thereby reducing RS congestion. Nevertheless, it is crucial to acknowledge that increasing these resources may entail certain trade-offs and could potentially have adverse effects on overall system performance. To gain comprehensive insights, we conduct a thorough performance analysis under various scenarios, aiming to identify key characteristics that can facilitate the optimal operation of massive MIMO in high-density IoT environments. Building upon our findings, we devise an algorithm that efficiently manages resources, ultimately leading to improved system performance.

The use of massive multiple-input–multiple-output (MIMO) technology is gaining traction as a potential solution to various wireless challenges, particularly in addressing the deployment of ultra high-density massive Internet of Things (IoT) connectivity. To facilitate the ultra high-density massive IoT, it is imperative to implement intelligent power control strategies that ensure low latency and high-reliability requirements are met. In this article, we present power control strategies based on the performance analysis of the two efficient uplink power control schemes, channel inversion (CI) power control and max–min (MM) fairness power control, designed to enhance massive IoT connectivity in massive MIMO networks. Additionally, we propose an initial power determination scheme based on closed-form expressions of signal-to-interference-plus-noise ratio (SINR) to effectively leverage system performance. Furthermore, we apply the CI power control scheme to reference signal (RS) to further enhance the performance of the system. Our results reveal that applying CI power control to RS significantly improves system fairness, and that the performances of CI power control and MM fairness power control are comparable in the context of massive IoT connectivity. Thus, utilizing the simple CI power control scheme provides satisfactory performance in terms of both spectral efficiency and fairness. Our proposed power control strategies and initial power determination scheme can be well suited for various massive IoT systems using massive MIMO.

We investigate the effectiveness of simple and efficient downlink power control schemes for Massive multiple-input multiple-output (MIMO) systems in high-density massive IoT networks. The complexity and delay of sophisticated power control schemes make them unsuitable for high-density massive IoT situation, necessitating the development of simpler alternatives. We examine two such schemes, equal power allocation and max-min fairness power control, analyzing their performance through signal-to-interference-plus-noise ratio (SINR) derivation in scenarios with and without uplink reference signal (RS) power control. We find that equal power allocation provides high spectral efficiency (SE) but not high fairness, while max-min fairness power control provide high fairness, but requires more complexity and channel state information (CSI) knowledge. However, the performance of max-min fairness power control becomes similar to that of equal power allocation in some cases, suggesting the suitability of the simpler scheme for system performance enhancement. We also propose an algorithm to reduce power consumption through initial radiation power determination. Our findings provide valuable insights into the effectiveness of power control schemes for Massive MIMO in high-density massive IoT networks.

To support a vast amount of Internet of Things (IoT) devices using massive multiple-input multiple-output (MIMO) systems simultaneously, due to the limited available resources, uplink orthogonal reference signals (ORSs) must be heavily reused for channel estimation. In this situation, the performance of massive MIMO systems can be seriously degraded due to the collision of ORSs. Random nonorthogonal reference signals (NORSs) can be considered to cope with this problem since they can be independently generated regardless of the number of IoT devices. However, as the number of IoT devices increases, RS contamination also increases. In this article, we consider applying random NORSs in massive MIMO to support a large amount of IoT devices. Generally, ORSs can deliver better spectral efficiency (SE) than NORSs. However, we show that the NORS scheme has higher fairness and lower outage probability. In addition, in the situation of massive IoT, the SE performance of the NORS scheme shows substantially similar to that of the ORS scheme. These results indicate that the NORS scheme has several advantages compared to the ORS scheme in the situation of massive IoT. We provide numerical analysis which reveals some useful characteristics of using NORSs for massive MIMO with massive IoT connectivity. Using these characteristics, we propose an algorithm that can significantly reduce the outage probability.

Millimeter wave massive multi input multi output (mmWave m-MIMO) holds immense potential for beyond 5G networks, however its practical implementation faces hurdles in beamforming, channel estimation, and signal detection. Deep learning (DL) has emerged as a promising solution for tackling these challenges, but centralized learning (CL) approaches raise concerns about privacy and communication overhead. This is where federated learning (FL) steps in as a compelling alternative. By distributing the workload to user devices while respecting data privacy, FL offers a promising avenue for overcoming the limitations of CL in mmWave m-MIMO applications. This paper delves into the current and future advancements of FL in the context of mmWave m-MIMO. We specifically focus on how FL can be applied to channel estimation, beamforming, and signal detection within this framework. Furthermore, we summarize relevant open source dataset, code, and FL framework to facilitate the development of FL for tackling challenges in mmWave m-MIMO. To conclude, we analyze the key research challenges and opportunities that lie ahead for the development of FL in mmWave m-MIMO applications.

In this paper, we consider a sectorized Massive multiple-input multiple-output (MIMO) system to simultaneously support a large amount of Internet of things (IoT) devices. To implement low complexity and low latency IoT networks, it has been shown that the reuse of orthogonal uplink reference signal (RS) is quite effective. Sectorization can reduce the heavy RS reuse in the situation of massive IoT connectivity, and consequently reduce interference from RS contamination. However, inter-sector interference (ISI) can be another source that reduces performance. We investigate the characteristics of sectorized Massive MIMO with RS reuse, and compare the performance with the case of unsectorized Massive MIMO. We present theoretical closed-form expressions of spectral efficiency (SE) applying sectorized antenna systems in the situation of massive IoT connectivity. We show that, in the massive IoT connectivity situation, sectorized Massive MIMO can show better total SE performance than unsectorized Massive MIMO, while outage probability can be seriously increased due to nonuniform antenna patterns and ISI. We also show that the power control mechanism which has been applied to unsectorized RS reuse Massive MIMO also can be successfully applied to the sectorized RS reuse Massive MIMO.

In this article, we consider the application of a cell-free (CF) massive multiple-input–multiple-output (MIMO) system to low-power Internet of Things (IoT) networks. CF Massive MIMO distributes many access points (APs) in a given area and supports many IoT devices simultaneously using the same time and frequency resources. To support many IoT devices that have more than the number of service antennas within limited coherence time/frequency, the reference signal (RS) should be reused. Generally, CF Massive MIMO with massive connectivity requires a lot of power and, thus, it is difficult to use in low-power IoT networks. In this regard, we propose an energy-efficient (EE) power control scheme that can significantly reduce the radiation power of IoT devices. We note that with many IoT devices, there is a quite large amount of interference, and if we choose the radiation power based on this information, the signal-to-interference-plus-noise ratio (SINR) becomes approximately independent of the radiation power. We show that our scheme can reduce the radiation power more than 90% compared to the normal operation. We also show that with many IoT devices, there is a spectral efficiency hardening effect, and maximum ratio (MR) processing and minimum mean square error (MMSE) processing present similar performance and, thus, MR processing could be sufficient for the use in CF Massive MIMO with massive IoT connectivity.

Massive multiple-input–multiple-output (MIMO) systems can support a large number of Industrial Internet of Things (IIoT) devices using many service antennas that are equipped at a base station (BS). Even though massive MIMO can increase both spectral efficiency (SE) and energy efficiency (EE), to support numerous IIoT devices, a drastic amount of downlink power consumption can be required. We investigate the performance of downlink signal transmission for massive IIoT networks using massive MIMO, and propose a downlink signal transmission scheme that can significantly reduce the transmission power consumption with little SE loss. We consider a system in which the number of IIoT devices is much larger than the number of service antennas, and in order to simultaneously support a large number of IIoT devices, every orthogonal uplink reference signal (RS) may be reused by a few IIoT devices. In this situation, we derive simple SE approximations, and based on the approximation, a target data rate is determined. The target data rate is used to determine a target signal-to-interference and noise ratio (SINR) and the corresponding signal power. In addition, the SE approximation can be used as an upper bound and the corresponding signal power can be determined by setting an adjustable parameter, which is multiplied to the upper bound. The maximum allowable uplink/downlink signal power is predefined to prevent unexpected high-power requirements. The simulation results are provided to show the effectiveness of the proposed schemes.

Due to the high spectral efficiency (SE) and energy efficiency (EE), it has been proven that massive multiple-input-multiple-output (MIMO) can be successfully applied to Industrial-Internet-of-Things (IIoT) networks. The channel hardening effect of massive MIMO which makes the instantaneous channel gain converges to its average value with little fluctuation, allows simple scheduling and little downlink reference signals (RSs) transmission. We analyze the performance of the channel hardening effect according to the parameter variations, and based on the analysis, we propose an adaptive switching scheme that switches between a mode of using downlink RS and a mode of without using downlink RS depending on the situation. If the downlink RS is not used, the available time and frequency resources can be increased, whereas the signal-to-interference plus noise ratio (SINR) can be decreased. On the other hand, using downlink RS can increase SINR, but it can decrease available time and frequency resources. The proposed scheme finds the mode providing better performance, and conducting mode switching based on various circumstances. The proposed scheme can achieve high SE improvement with little system burden and can thus be used as a useful tool to increase the performance of massive MIMO-based IIoT networks.

Massive multiple-input-multiple-output (MIMO) can be effectively applied to the data gathering system of Industrial Internet-of-Things (IIoT) networks. For long-term maintenance of industrial electronic systems with battery-limited IIoT devices, it is essential to increase the energy efficiency (EE) of the system. The high EE should be achieved with ultrareliability and low latency because there are a lot of critical information for the IIoT networks. With this in mind, in this article, we propose high EE operation schemes for the massive MIMO-based IIoT networks. An orthogonal multiple access (OMA) scheme is used and a signal clipping technique is applied to increase the EE of the industrial data gathering system. Clipping distortion for uplink massive MIMO with massive IIoT connectivity is analyzed, and we show that clipping distortion of maximum ratio (MR) processing is directly proportional to the number of service antennas and the number of IIoT devices, while that of zero-forcing (ZF) processing can be reduced as the number of IIoT devices increases. We define the EE metric and derive the closed-form inverses of the EE metric to determine the relevant parameters. Based on the derived closed-form equations, we introduce the EE operation schemes using low-latency parameter determination methods. Simulation results validate the theoretical analysis.

Massive multiple-input–multiple-output (MIMO) systems which include a large number of service antennas have received a great deal of attention as a means to improve both spectral efficiency (SE) and energy efficiency (EE) of wireless communication systems. In this article, we study the feasibility of using Massive MIMO to support a vast amount of underwater Industrial Internet of Things (IIoT) devices. By using a large number of antennas as compensation entities for the low bandwidth in the underwater channel, we show that underwater Massive MIMO can simultaneously support a massive amount of underwater IIoT devices. Electromagnetic radio frequency (RF) and acoustic waves are used to present the performance with respect to SE, throughput (TP) per user equipment (UE), and coverage. Additionally, we show that both Massive MIMO with RF and acoustic waves have the same closed-form upper bounds which can be used as an important design tool of underwater IIoT networks. Numerical results verify the theoretical analysis.

In this article, we present high energy-efficiency (EE) operation schemes for downlink orthogonal frequency division multiplexing (OFDM)-based massive multiple-input multiple-output (MIMO) systems in industrial Internet of Things (IIoT) networks. We derive the exact closed-form expressions for some important downlink operation parameters, and based on the parameters, we propose the methods of systematic operation to satisfy the required EE metric with low latency. The operation parameters include the number of service antennas, downlink transmission power, and related coverage. To increase the EE of downlink OFDM signal transmission, it is also well known that peak-to-average power ratio (PAPR) reduction is particularly important, and we apply the clipping PAPR reduction technique for the purpose to increase the power amplifier efficiency, and thus we also determine the level of clipping to satisfy the EE requirement. We use two representative linear precoding schemes for massive MIMO, such as maximum ratio (MR) precoding and zero-forcing (ZF) precoding, and show that the amount of clipping distortion of both MR precoding and ZF precoding is proportional to the distortion from the corresponding IIoT device's signal and distortions from other IIoT devices' signals. Simulation results are provided to validate the analysis and related schemes.

A cognitive radio network can be employed in any wireless communication systems, including military communications, public safety, emergency networks, aeronautical communications, and wireless‐based Internet of Things, to enhance spectral efficiency. The performance of a cognitive radio network (CRN) can be enhanced through the use of cooperative relays with buffers; however, this incurs additional delays which can be reduced by using virtual duplex relaying that requires selection of a suitable relay pair. In a virtual duplex mode, we mimic full‐duplex links by using simultaneous two half‐duplex links, one transmitting and the other one receiving, in such a way that the overall effect of duplex mode is achieved. The relays are generally selected based on signal‐to‐interference‐plus‐noise ratio (SINR). However, other factors such as power consumption and buffer capacity can also have a significant impact on relay selection. In this work, a multiobjective relay selection scheme is proposed that simultaneously takes into account throughput, delay performance, battery power, and buffer status (i.e., both occupied and available) at the relay nodes while maintaining the required SINR. The proposed scheme involves the formulation of four objective functions to, respectively, maximize throughput and buffer space availability while minimizing the delay and battery power consumption. The weighted sum approach is then used to combine these objective functions to form the multiobjective optimization problem and an optimal solution is obtained. The assignments of weights to objectives have been done using the rank sum (RS) method, and several quality‐of‐service (QoS) profiles have been considered by varying the assignment of weights. The results gathered through simulations demonstrate that the proposed scheme efficiently determines the optimal solution for each application scenario and selects the best relay for the respective QoS profile. The results are further verified by using the genetic algorithm (GA) and particle swarm optimization (PSO) techniques. Both techniques gave identical solutions, thus validating our claim.

In this paper, we propose an innovative self‐organizing medium access control mechanism for a distributed cognitive radio network (CRN) in which utilization is maximized by minimizing the collisions and missed opportunities. This is achieved by organizing the users of the CRN in a queue through a timer and user ID and providing channel access in an orderly fashion. To efficiently organize the users in a distributed, ad hoc network with less overhead, we reduce the sensing period through parallel sensing wherein the users are divided into different groups and each group is assigned a different portion of the primary spectrum band. This consequently augments the number of discovered spectrum holes which then are maximally utilized through the self‐organizing access scheme. The combination of two schemes augments the effective utilization of primary holes to above 95%, even in impasse situations due to heavy primary network loading, thereby achieving higher network throughput than that achieved when each of the two approaches are used in isolation. By efficiently combining parallel sensing with the self‐organizing MAC (PSO‐MAC), a synergy has been achieved that affords the gains which are more than the sum of the gains achieved through each one of these techniques individually. In an experimental scenario with 50% primary load, the network throughput achieved with combined parallel sensing and self‐organizing MAC is 50% higher compared to that of parallel sensing and 37% better than that of self‐organizing MAC. These results clearly demonstrate the efficacy of the combined approach in achieving optimum performance in a CRN.

Massive multiple input multiple output (MIMO) antenna systems have received a great deal of interest due to their applicability to industrial network systems. One of the major obstacles that reducing the performance of massive MIMO system is reference signal (RS) overhead which can be increased as the number of transmitter antenna increases in frequency division duplexing (FDD) system. Using channel reciprocity in time division duplexing (TDD) system can significantly increase the RS overhead performance using channel reciprocity. However, to use the channel reciprocity, channel calibration is a significant challenge to overcome in real system design. There are various base station (BS) calibration methods that have already successfully applied to the current BS, while there are few methods for the calibration of distributed user entities (UEs) and/or industrial Internet of things (IIoT) devices. In this paper, we propose a distributed UE RF calibration method of massive MIMO systems that uses the power headroom report. The power headroom is typically reported from the UEs and/or IIoT devices to the BS periodically or aperiodically. By including additional amplitude RF impairment information in the power headroom report, we can successfully transfer the necessary information from UE to BS. The proposed scheme does not require additional feedback, and is in compliance with current standards. There are five schemes based on the kind of information transferred to BS and the way of making a calibration factor that can be multiplied to the estimated channel. Numerical analysis shows that the proposed schemes can significantly increase the spectral efficiency with little system burden, and thus can be a core technology for the realization of massive MIMO for industrial network systems.

Energy efficient massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) antenna systems have received a great deal of attention for use in industrial network applications due to the possibility of reducing operation costs and carbon footprint. One of the difficulties in realizing high energy efficiency (EE) massive MIMO-OFDM antenna systems is the high peak-to-average power ratio (PAPR) of the signal, which seriously limits the efficiency of power amplifiers (PA). Selected mapping (SLM) is a powerful PAPR reduction scheme for OFDM related systems, however, there is implicit consensus that SLM could not be applied to massive MIMOOFDM antenna systems due to its high computational complexity and side information (SI) burden. In this paper, we propose an SLM-based PAPR reduction scheme that can be applied to massive MIMO-OFDM antenna systems based on antenna grouping. Using the antenna grouping based suboptimal scheme, we show that an SLM-based PAPR reduction scheme can be successfully applied to massive MIMOOFDM antenna systems with significant increase of EE. The proposed scheme has very high flexibility with various adjustable parameters, so one can easily choose the settings they desire between performance-complexity tradeoff. Numerical analysis shows that the propose scheme can increase EE by 18.69% compared with the conventional system.

To apply cyber-physical system (CPS) technique in industrial internet, a wireless technology that is able to robustly maintain hyperconnectivity between a data center and distributed user entities and/or industrial Internet of Things devices is required. We investigate the feasibility of utilizing massive multiple-input multiple-output (MIMO) as such a wireless technology. We analyze the performance of a massive MIMO base station deployed at a data center to provide massive connectivity to a large number of devices. In addition, we discuss related research challenges for deploying massive MIMO in industrial internet applications of CPS, such as device scheduling and power control, energy efficient design and radio frequency energy transfer/harvesting, signaling techniques for drones and mobile robots, and applications of underwater industrial internet.

We consider the problem of designing transmit beamformer and power for downlink cooperative base-station (BS) system with a large antenna arrays. Since the design of the beamforming vector at the transmitter requires high computational complexity, in a large antenna arrays, we utilize the zero-forcing transmit beamformer, which is the simplest form and the optimal performance in a large antenna arrays. Therefore, this paper focuses on the design of power allocation with fixed transmit beamformer for minimizing the transmit power while meeting target signal-to-interference-and-noise-ratio (SINR) of each user and power constraints. We consider two scenarios according to the power constraints of cooperative BSs. One scenario is the sum power constraint on the cooperative base-stations. In this case, the cooperative BSs share the total available transmit power. However, each BS exists a maximum available transmit power in practical implementations. Thus, we consider a more realistic per BS power constraints.We proposed the solution strategies for both scenarios: For the sum power constraint case, a simple intuitive solution, where the power is allocated without regard to the power constraint until the SINR constraints is satisfied, is presented. For the per BS power constraints case, we use the properties of a large antenna arrays to find the solution of closed form. We also demonstrate, via numerical simulation, the performance of proposed strategy is convergent to the optimal performance which is achieved by using the iterative algorithm.

This paper proposes an energy efficient antenna selection for large scale (LS) MIMO in green base station (BS). For the maximum energy efficiency (EE) operation of LS-MIMO, antenna selection is necessary, because the RF chains which are connected with antennas are expensive, and consumes a lot of power. However, the excessive amount of antennas in LS-MIMO hinder the efficient antenna selection. We determine optimum antenna number in various circumstances, and show that with LS-MIMO, simple random antenna selection can provide significant EE gain. Moreover, we also show if the EE optimum number of antenna is larger than a certain threshold, the random antenna selection is already very close to the optimum antenna selection. The proposed technique is verified by both mathematical analysis and numerical simulations.

Multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) has been receiving a great deal of attention as a solution for high-quality service for next generation wireless communications. However one of main problems of orthogonal frequency division multiplexing (OFDM) is its high peak-to-average power ratio (PAPR) which seriously limits power efficiency of high power amplifier (HPA). In this paper, we present PAPR reduction technique of V-BLAST based MIMO-OFDM system. We use the selected mapping (SLM) technique as a PAPR reduction technique since it does not cause any signal distortion. As a special protection for side information (SI) of SLM technique, we propose SI power allocation technique. Simulation results show that proposed technique gives significantly better BER performance than ordinary SLM technique for MIMO-OFDM systems.

Orthogonal Frequency Division Multiplexing (OFDM) has several attributes which make it a preferred modulation scheme for high speed wireless communications. However, its high Peak-to-Average-Power Ratio (PAPR) causes nonlinear distortion thus limiting the efficiency of the transmitter's High Power Amplifier. Among the approaches proposed for PAPR mitigation, the Partial Transmit Sequence (PTS) technique is very promising since it does not generate any signal distortion. However, its high complexity makes it difficult for use in high speed communication systems. We present a new lowcomplexity tree algorithm to implement the PTS approach, which seeks the best trade-off between performance and complexity. Simulation results show that this new technique's performance is similar to that of the optimum case, however with significantly lower complexity.