Antenna Selection Method Research Articles

Joint Antenna Selection and Power Allocation Method Based on Quantum Energy Valley Optimization Algorithm for Massive MIMO IoT Systems

Joint Antenna Selection and Power Allocation Method Based on Quantum Energy Valley Optimization Algorithm for Massive MIMO IoT Systems

Improving physical layer security in distributed multiple‐input multiple‐output dual‐function radar‐communication systems

AbstractA distributed multiple‐input multiple‐output (MIMO) dual‐function radar‐communication (D‐MIMO DFRC) system is composed of multiple distributed dual‐function transmitters, multiple radar receivers and multiple communication receivers, which is capable of performing communication and radar tasks simultaneously. In a DFRC system, the goal is on optimising both the sum ‐rate in communication receivers and detection/localisation performance in radar receivers. The secrecy rate is maximised in D‐MIMO DFRC systems by decreasing the eavesdropper data rate as much as possible with a two‐step antenna selection method while maintaining optimal radar performance. In the first step of the proposed method, all transmitter antennas have been classified into groups based on their distance from each other, and each group is called a cluster. Then, a cluster of distributed transmitter antennas is selected based on path fading effects. In the second step of this method, the antenna selection algorithm is performed in the pre‐selected cluster based on channel capacity information utilising QR decomposition. The results show that this antenna selection method, along with low computational complexity and high performance, leads to the maximisation of the secrecy rate. In DFRC systems, it is desirable to minimise the total transmit power while satisfying system requirements to provide low probability of interception (LPI). Finally, after antenna selection, a power allocation strategy is also applied on the selected antennas to optimise the total transmit power and to maximise throughput in communication radar receivers simultaneously, and as a result it leads to provide LPI.

Transmit Antenna Selection for Sum-Rate Maximization with Multiclass Scalable Gaussian Process Classification

Antenna selection techniques are extensively applied to reduce hardware cost and power consumption in multiple-input multiple-output (MIMO) systems. This paper proposed a low-cost antenna selection method for system sum-rate maximization based on multiclass scalable Gaussian process classification (SGPC) which is capable to perform analytical inference and is scalable for massive data. Simulation results show that the average sum-rate obtained by SGPC is 1. 9 bps/Hz more than that obtained by conventional optimization driven user-centric antenna selection (UCAS) algorithm and 1 bps/Hz more than that obtained by the up-to-date learning scheme based on a deep neural network (DNN) when signal-to-noise ratio (SNR) is 10 dB, the number of total antennas at BS is 6, the number of selected antennas is 4, and the number of single-antenna users is 4. The superiority of SGPC over UCAS and DNN is more obvious as SNR, the number of selected antennas, or the number of users increases.

Real-Time CRLB Based Antenna Selection in Planar Antenna Arrays

Estimation of User Terminals’ (UTs’) Angle of Arrival (AoA) plays a significant role in the next generation of wireless systems. Due to high demands, energy efficiency concerns, and scarcity of available resources, it is pivotal how these resources are used. Installed antennas and their corresponding hardware at the Base Station (BS) are of these resources. In this paper, we address the problem of antenna selection to minimize Cramer-Rao Lower Bound (CRLB) of a planar antenna array when fewer antennas than total available antennas have to be used for a UT. First, the optimal antenna selection strategy to minimize the expected CRLB is proposed. Then, using this strategy as a preliminary step, we present a two-stage greedy antenna selection method whose goal is to minimize the instantaneous CRLB. The optimal start point of the greedy algorithm is presented alongside some methods to reduce the algorithm’s computational complexity. Numerical results confirm the accuracy of proposed solutions. They demonstrate that the proposed antenna selection method only requires a small proportion of the total available antennas to accomplish a significant amount of the total performance, enhancing hardware utilization efficiency. Also, it is shown that the presented algorithm has a high error tolerance.

Joint optimization of FDA-MIMO antenna selection and frequency offset to maximize SINR

The structured sparse multiple-input multiple-output frequency diverse array (FDA-MIMO) radar has range, angle estimation, and resolution advantages. However, the structured sparse array is not the optimal array structure with the max output signal-to-interference-noise ratio (SINR). In order to obtain the optimal FDA-MIMO structure, this paper maximizes the output SINR of the array by optimizing the array antenna selection and frequency offset. Because there is a coupling relationship between frequency offset and array antenna selection, and it is difficult to optimize them simultaneously, we divide the problem into two subproblems. According to the spatial separation coefficient (SCC), we propose the array antenna selection method using the angle of target and interference subspace and the frequency offset optimization approach using the constant modulus beamforming idea and particle swarm optimization algorithm. Then we join the two approaches to conduct alternate iterative optimization. By comparing the optimum FDA-MIMO with other structures of FDA-MIMO, we confirm the effectiveness and superiority of the optimum FDA-MIMO.

Diversity achieving full‐duplex DF relaying with joint relay‐antenna selection under Nakagami‐m fading environment

SummaryOne of the main imperfections degrading the performance of full‐duplex (FD) relaying systems is the outage floor. In this work, a power scaling method is proposed, which overcomes this effect even when there does not exist a direct channel between source and destination nodes. The system is composed of decode‐and‐forward (DF) FD relays over the Nakagami‐m fading environment. To promote system performance, joint antenna and relay selection methods are employed in the FD relaying systems. Each FD relay is equipped with multiple antennas for receiving and the other for transmitting the information. The transmitting and receiving antennas are chosen based on the instantaneous channel variations, and one relay is selected to improve the signal‐to‐interference and noise ratio (SINR) of the FD relaying system. The performance of the proposed design is investigated. Moreover, the closed‐form equations of the ergodic capacity and outage probability are attained. The analytical results are confirmed by different simulations. Results indicate that the proposed design achieves an additional spatial diversity gain because of using the antenna selection at the relay nodes. Moreover, by power scaling (PS) method, the system performance is effectively improved compared to the conventional FD relaying structures.

On the Energy Efficiency of Multicell Massive MIMO with Antenna Selection and Power Allocation

The energy consumption of massive multiple-input multiple-output (MIMO) systems increases with the number of antennas. Optimizing the energy efficiency (EE) of massive MIMO systems is one of the ways to achieve green communication. This paper proposes an EE optimization method that genetic algorithm-based antenna selection and power allocation (GA-AS+PA) for the downlink of a multicell massive MIMO system under the restriction of the users’ sum-rate. First, we use the genetic algorithm to determine the active transmitting antenna of each base station (BS). Then, the transmission power for each user is allocated using the convex optimization method. Finally, the EE of system is optimized under the achieved optimum BS’s transmit power and the number of active antennas. From the simulation results, the GA-AS+PA method can improve the EE of the system while meeting user sum-rate requirements, which achieves better performance compared with random antenna selection+equal power allocation method (RAS+EPA), random antenna selection+power allocation method (RAS+PA), the antenna selection method based on genetic algorithm+equal power allocation method (GA-AS+EPA), and equal power allocation (EPA) these four methods. The EE of the proposed GA-AS+PA method is improved by 33.3% compared to the EPA method.

Data-driven Diversity Antenna Selection for MIMO Communication using Machine Learning

<p>With the popularity of wireless application environments, smart antenna technology has completely changed the communication system. In order to improve the quality of wireless transmission, smart antennas have been widely used in wireless devices. Wireless signal modeling and prediction machine learning gradually replaced the traditional smart antenna selection method in the antenna selection solution. This article utilizes mobile devices to adjust the diversity antenna pattern for test verification in a MIMO wireless communication environment. The proposed method manipulates signal parameters through error vector magnitude (EVM) and adds data-driven training data. The results show that the SVM and NN methods proposed in this paper are 10.5% and 14% higher than the traditional EVM calculation methods, respectively. Thereby, realize precise antenna adjustment of mobile devices and improving wireless transmission quality.</p> <p> </p>

Indoor Tracking of Multiple Individuals With an 802.11ax Wi-Fi-Based Multi-Antenna Passive Radar

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

Transmit Antenna Selection Methods For Mimo Systems In Wireless Communications

Though MIMO systems improve performance of a wireless communication network by the usage of multiple antennas, demand of distinct set of RF chain (i.e., electronic components required for antenna transmission and reception, in wireless communication) for all the antennas leads to an increase in complexity and cost. Antenna selection technique of MIMO has proved to be a good means to solve this issue. Antenna Selection methods find optimal number of antennas required out of the total antennas present in the MIMO (Multiple Input Multiple Output) system. The selection of antenna can be performed at both ends of the communication network i.e., transmitter or receiver. In this paper, an overview of various Transmit Antenna Selection techniques for various MIMO systems is presented.

Design of Offset Spatial Modulation OFDM

In this paper, the idea of offset spatial modulation (OSM) is integrated with orthogonal frequency division multiplexing (OFDM), toward an efficient design to bridge their advantages. Compared to its conventional counterpart as spatial modulation (SM)-OFDM, the proposed OSM-OFDM scheme aims at providing a simplified implementation structure with less number of radio frequency (RF) chains, by introducing an offset between the RF chain and the index of the activated transmit antenna on each subcarrier. Specifically, three types of offset antenna selection (OAS) methods are developed to meet different scene requirements for different number of available RF chains. Furthermore, through theoretical analysis, we quantify the bit error rate upper bounds of OSM-OFDM with different types of OAS methods. Finally, extensive computer simulations demonstrate that OSM-OFDM provides a flexible tradeoff among implementation cost, computation complexity and error performance.

Energy efficient transmit antenna selection scheme for correlation relied beamspace mmWave MIMO system

mmWave communication system that offers high datarate are accompanied by massive number of antennas for high gain transmission. These large array of antennas requires special beamforming schemes and also requires large number of RF chains. Beamspace MIMO is a beamforming architecture that employs large antenna array and supports limited number of RF chains by converting conventional mmWave channel into sparse channel. Multiple antenna array system are susceptible to antenna correlation effect thereby reducing the system capacity. Transmit antenna selection methods considering the effect of antenna correlation provides feasible solution by maximising the system capacity and energy efficiency. In order to improve the energy efficiency of the transmitted signal three new transmit antenna selection schemes are proposed by considering the effects of antenna correlation. The simulation results demonstrate that the proposed methods improve energy efficiency and sum rate.

Antenna selection in nonorthogonal multiple access multiple‐input multiple‐output systems aided by machine learning

AbstractThis work proposes a transmitter antenna selection (TAS) method for multiple‐input multiple‐out (MIMO) nonorthogonal multiple access (NOMA) that is a promising multiple access technique for the fifth generation mobile communications systems. Specifically, we propose to activate the more suitable subset of base station antennas while allocating users into appropriate NOMA clusters such that the system operates in energy efficiency mode, selecting such appropriate antenna subset indexes that maximize the sum‐rate (SR) of the NOMA‐MIMO system. Once that the TAS based on exhaustive‐search is very complex to be implemented in real communication systems, we propose an effective TAS method based on machine learning while keeping very promising performance. A convolutional neural network‐based transmitter antenna selection (CNN‐TAS) method is proposed for efficiently select antennas aiming at maximizing the system SR. Hence, extensive numerical results demonstrate that the CNN‐TAS can suitably learn the problem, performing with very high accuracy choosing properly the antenna subset that maximizes the system spectral efficiency while reducing substantially the processing time of real‐time operations.

Secrecy Enhancing of SSK Systems for IoT Applications in Smart Cities

The secure exchange of messages between different communication devices is a major issue of Internet-of-Things (IoT) applications in future smart cities. Current security mechanisms focus on multiple antennas technology, such as spatial modulation (SM), but in space shift keying (SSK), there is still a space to explore. In this article, we propose a secrecy-enhancing SSK scheme for IoT applications by applying security technologies in the physical layer wherein the number of transmit antennas is arbitrary rather than the value of power of two. In this scheme, the security performance of the communication system is improved by using two technologies, namely, artificial noise (AN) and antenna selection. We assume that the application scenario of the SSK system is under the classic eavesdropping model. First, we design ANs according to the channel state information (CSI) to interrupt the eavesdropper and benefit the legitimate receiver via the appropriate cancellation technology. Second, the antenna selection method is designed based on the signal to leakage noise ratio (SLNR) to further boost the secrecy performance by expanding the mutual information difference between the main channel and the eavesdropping channel. Results from our simulations indicate that by the use of the proposed scheme, significant secrecy enhancing can be achieved in terms of bit error ratio (BER) and secrecy rate (SR) when compared with existing schemes. This achieved secrecy enhancing can benefit the suitable IoT communication applications in the smart city environment to avoid the leakage of data transmission.

Cooperative Antenna Selection Method for Directional Antenna Ad Hoc Networks Based on ALOHA

In recent years, directional antennas or phased array antennas are being widely used in communication systems due to the higher antenna gains. However, without external time synchronization and angle synchronization, the unsynchronized node usually takes a long time to synchronize with the existing nodes due to the narrow beams. Although the multibeam transmission or the digital phased array antenna can reduce this problem, it is clear that the cost of the digital phased array antenna is currently too high. Without external time synchronization and angle synchronization, a cooperative antenna selection method based on directional antennas is proposed in this paper. Our method only uses the narrow beams to transmit and to receive and reduces the time for self‐synchronization. In this paper, we give the expression of the expected average time for the self‐synchronization of multiple nodes, transform the problem into the problem of finding the minimum value of the infinite norm of the sequence, and then propose a cooperative antenna selection method which calculates the optimal transmission probability distribution of the node in different directions through parameter sharing and relative geometric position relationship between nodes. Finally, we verify the proposed method through simulation, and the number of beams is set between 6 and 10. In a typical scenario of five nodes, our method reduces the maximum average self‐synchronization time by 50% averagely, compared with the traditional method which sends the different antenna beams at equal probability.

Power Profile-Based Antenna Selection for Millimeter Wave MIMO With an All-Planar Lens Antenna Array

Lens antenna arrays can significantly reduce the number of radio frequency (RF) chains and provide a cost-effective beamforming approach to millimeter-wave massive multiple-input multiple-output (MIMO) systems. However, the presence of curved fronts has imposed practical challenges in fabrication and system integration as the antennas have to be deployed on the lens focal curve or face. To this end, we analyze the massive MIMO system with an all-planar RF lens antenna array whose lens is made of metamaterials and the antennas are distributed on a two-dimensional (2D) plane. In terms of this structure, we then propose a power-profile based antenna selection method. Specifically, by considering the coupling among antenna elements and that between the RF-lens and the antenna array, the analyses regard the lens and antennas as an entity using a full-wave model. Antennas are selected according to the angle of arrivals (AoAs) of signals transmitted by users and the lens response to the incident wave from an arbitrary direction on antennas. Simulations are provided for verifying the system performance and to show: i) Specific gain responses on each element of the base station antenna array for user terminals distributed at different locations; and ii) The all-planar lens antenna array supports a spectrum efficiency upto 170.7 bps/Hz for a 25 GHz MIMO system with 50 user terminals and a signal-to-noise ratio (SNR) of 10 dB.

Energy-efficient flexible and fixed antenna selection methods for XL-MIMO systems

Massive multiple-input multiple-output (M-MIMO) is a key technology for 5G networks which consists on equipping the base station (BS) with hundreds or thousands of antennas. Increasing the antenna separation is paramount in order to make real advantage from an array dimension of the order of thousands of antennas. One potential approach is the integration of the antenna array into large structures, which is referred to as extra-large-scale MIMO (XL-MIMO). However, when employing extremely large arrays, centralized processing architectures face a challenging complexity. A promising solution is to divide the antenna array into disjoint units, referred to as subarrays, with individual processing units. In this paper, we modify the M-MIMO channel model aiming to take two implications of the extreme array dimensions into account: the spherical wavefronts, which is called near-field propagation, and the concentration of the majority of energy received from a specific user on a small portion of the array, namely spatial non-stationarity. Considering the latter, it is not efficient from an energy perspective to activate the antennas with lower channel gains to transmit/receive the signal to/from a given user. Thus, antenna selection methods are quite important in the considered scenario. Two antenna selection (AS) methods for XL-MIMO are proposed in this paper: the adjustable-flexible antenna selection (FAS), and the fixed subarray selection (FSS). Numerical results demonstrate that, by judiciously selecting the antennas subset used to detect the signal of each user in the uplink (UL), the number of active antennas can be reduced considerably in a scenario with up to 64 active users, reducing the power consumption without compromising the throughput. The EE is hugely increased in such scenario. Finally, the FSS scheme achieved the same performance of FAS, while having a simpler hardware implementation.

Power Scaling and Antenna Selection Techniques for Hybrid Beamforming in mmWave Massive MIMO Systems

With the advent of massive MIMO and mmWave, Antenna selection is the new frontier in hybrid beamforming employed in 5G base stations. Tele-operators are reworking on the components while upgrading to 5G where the antenna is a last-mile device. The burden on the physical layer not only demands smart and adaptive antennas but also an intelligent antenna selection mechanism to reduce power consumption and improve system capacity while degrading the hardware cost and complexity. This work focuses on reducing the power consumption and finding the optimal number of RF chains for a given millimeter wave massive MIMO system. At first, we investigate the power scaling method for both perfect Channel State Information (CSI) and imperfect CSI where the power is reduced by 1/number of antennas and 1/square root (number of antennas) respectively. We further propose to reduce the power consumption by emphasizing on the subdued resolution of Analog-to-Digital Converters (ADCs) with quantization awareness. The proposed algorithm selects the optimal number of antenna elements based on the resolution of ADCs without compromising on the quality of reception. The performance of the proposed algorithm shows significant improvement when compared with conventional and random antenna selection methods.

Improvement of Interference Suppression Performance Using Antenna Selection of Mobile Terminal for Full-Duplex $4\times4$ MIMO System

This article presents an improvement of interference suppression between mobile terminals using the antenna selection method of a mobile terminal in a full-duplex MIMO system. In the system, a method of simultaneously transmitting and receiving a signal on the same frequency band in uplink and downlink between a base station and a mobile terminal has been studied. As a method of terminal-to-terminal interference suppression, a method of null beamforming for the first eigenvalue of the terminal-to-terminal channel by eigen-beamforming has been studied. The authors propose a method to realize further interference suppression by applying the antenna selection method to the mobile terminal when performing eigen-beamforming. The examination results of simulation using the ray tracing method and measurement in outdoor environment are reported assuming $4 \times 4$ MIMO in the frequency band of 12.9 GHz. From the evaluation results, the interference suppression effect by the antenna selection method is improved by about 3–5 dB compared with the conventional four element MIMO antenna.

Correction to: Antenna Selection in TDD Massive MIMO Systems

The original version of this article unfortunately contained mistakes in the “author group”, “System model” and “Antenna selection method based on energy efficiency” sections.