Antenna Users Research Articles

Deep Space Network Scheduling Using Quantum Annealing

NASA's Deep Space Network (DSN) is responsible for communication and navigation for several NASA and international missions. The DSN comprises three complexes located in Goldstone (California, USA), Cambera (Australia) and Madrid (Spain). This distribution in longitude guarantees a full sky coverage. Each complex has one 70-meter and several 34-meter antennas. The network routinely serves a few dozens missions. The scheduling of the DSN is complex and involves humans interventions as well as automated solutions. In order to increase the level of automation, different computing paradigms have been explored. In this paper, we report on the quadratic unconstrained binary optimization (QUBO) formulation of the DSN scheduling, as well as a custom classical solver designed around some of the unique features of this scheduling problem. Thanks to a hybrid framework that extends the size of the problems that can be solved with a quantum annealer, we are able to generate a schedule from the QUBO formulation of the problem for one week's worth of user antenna requests, which represents the time period scheduled during operation. In other words, this work describes a real- world application of quantum annealing using real-world, operational data. We compare the resulting schedules' quality to solutions obtained using a mixed-integer linear programming formulation on a commercial solver. Our custom solver, based on a quantum-inspired optimization technique called Substochatic Monte-Carlo, while much faster in generating schedules, could only treat a subset of requests and hence we report its results independently.

Rain Rate and Rain Attenuation Prediction For Satellite Communication in Ku Band Beacon Over TURKSAT Golbası

Satellite beacon is a signal which does not modulated and sent to ground in constant frequency with a specific designed power. Beacon signals are used by ground station antenna users track the satellite easily. The satellite operators generally choose beacon signal at Ku Band frequency band since this band is more resistant to rain attenuation. Ku band satellite system performance describe by the contribute of rainfall rate and rain attenuation. This paper includes the comparison of rain attenuation in Ku band beacon and taking as a reference ITU – 838 model caused by rain fall rate in TURKSAT premises. It is compared by calculating that how signals affected from rain between 2012, and 2019 observations for a TURKSAT satellite and in this measurement both theoretical formula and data are used.

Joint Transmit Waveform and Receive Filter Design for Dual-Function Radar-Communication Systems

In this paper, the problem of joint transmit waveform and receive filter design for dual-function radar-communication (DFRC) systems is studied. The considered system model involves a multiple antenna base station (BS) of a cellular system serving multiple single antenna users on the downlink. Furthermore, the BS simultaneously introduces sensing capabilities in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. A novel framework based on constrained optimization problems is proposed for the joint design of the transmit waveform and the radar receive filter such that different constraints related to the power amplifiers and the radar waveform are satisfied. In contrast to the existing approaches in the DFRC systems’ literature, the proposed approach does not require the knowledge of a predetermined radar beampattern in order to optimize the performance of the radar part through its approximation. Instead, a beampattern is generated by maximizing the radar receive signal-to-interference ratio (SINR) thus, enabling a more flexible design. Moreover, the radar receive filter processing and its optimization is considered for the first time on DFRC systems, enabling the effective exploitation of the available degrees of freedom in the radar receive array. Efficient algorithmic solutions with guaranteed convergence are developed for the defined constrained nonconvex optimization problems. The effectiveness of the proposed solutions is verified via numerical results.

Pengaruh Modifikasi Ground Plane Pada Antena Mikrostrip dengan Teknik Meader Line

Microstrip antennas generally have deficiency with small bandwidth, so that their utilization is very minimal and rarely used for antenna user applications on electronic devices. To increase antenna performance parameters bandwidth, voltage standing wave ratio, and return loss, need modification of antenna. This research was carried out by modifying of microstrip antenna with the meader line technique by ground plane wich is useful for increasing performance of microstrip antenna parameters. The ground plane on antenna was modified by adding a copper wire to the antenna. With modifications to ground plane antenna, the antenna performance parameters bandwidth, voltage standing wave ratio and return loss have decreased significantly. Antenna bandwidth has 124 MHz before modification, but has increased upto 214 MHz after modification ground plane, voltage standing wave raio has decreased from 1.835 to 1.12 after modifications ground plane antenna, and return loss of antenna before modification has -10.112 dB, after modification it decreased significanty upto -24.69 dB.

Joint design of transmit beamforming, IRS platform, and power splitting SWIPT receivers for downlink cellular multiuser MISO

A multiple antenna base station (BS) with an intelligent reflecting surface (IRS) platform, and several single antenna users are considered in the downlink mode. Simultaneous wireless information and power transfer (SWIPT) is utilized by the BS via transmit beamforming to convey information and power to all devices. Each device applies power splitting (PS) to dedicate separate parts of received power to information decoding and energy harvesting. We formulate a total transmit power minimization problem to jointly design the BS beamforming vectors, IRS phase shifts, and PS ratios at the receivers subject to minimum rate and harvested energy quality of service (QoS) constraints at all the receivers. First, we develop a block coordinate descent algorithm, also known as alternating optimization that can decrease the objective function in every iteration with guaranteed convergence. Afterwards, two low-complexity sub-optimal algorithms that rely on well-known maximum ratio transmission and zero-forcing beamforming techniques are introduced. These algorithms are beneficial when the number of BS antennas and/or number of users is large, or coherence times of channels are small. Simulations corroborate the expectation that by deploying a passive IRS, BS transmit power can be reduced by 10–20 dBw while maintaining similar guaranteed QoS. Furthermore, even the proposed sub-optimal algorithms outperform the globally optimal SWIPT solution without IRS for a modest number of IRS elements.

Random Beam-Based Non-Orthogonal Multiple Access for Low Latency K-User MISO Broadcast Channels.

In this paper, we propose random beam-based non-orthogonal multiple access (NOMA) for low latency multiple-input single-output (MISO) broadcast channels, where there is a target signal-to-interference-plus-noise power ratio (SINR) for each user. In our system model, there is a multi-antenna transmitter with its own single antenna users, and the transmitter selects and serves some of them. For low latency, the transmitter exploits random beams, which can reduce the feedback overhead for the channel acquisition, and each beam can support more than a single user with NOMA. In our proposed random beam-based NOMA, each user feeds a selected beam index, the corresponding SINR, and the channel gain, so it feeds one more scalar value compared to the conventional random beamforming. By allocating the same powers across the beams, the transmitter independently selects NOMA users for each beam, so it can also reduce the computational complexity. We optimize our proposed scheme finding the optimal user grouping and the optimal power allocation. The numerical results show that our proposed scheme outperforms the conventional random beamforming by supporting more users for each beam.

Sum-Rate Improvement in Massive MIMO System with User Grouping and Selection, and Antenna Scheduling Scheme

In this paper, we have proposed user grouping method and new user selection techniques in TDD Massive (MIMO) systems. Using K-means clustering algorithm we separate the users into different groups. Upon user groups are formed users are selected from different groups with Semi-Orthogonal (SO) and random user selection criteria. We use two user selection method to select users from different groups, those are-(i) Users are selected in intra-group those are SO with each other along with it also SO with other group’s users, (ii)-Users are selected from inter-group those are SO with each other. For antenna scheduling we used maximum channel gain measure in both the user selection situation. Semi-Orthogonal user selection (SOU) and antenna scheduling with zero forcing precoding demonstrated maximum system sum-rate that is seen in results. In table-1 appraised the computational cost of our modified SOU algorithm. The proposed algorithm’s efficiency is explored through the simulations.

ADMM-Based Infinity-Norm Detection for Massive MIMO: Algorithm and VLSI Architecture

In this article, we propose a novel data detection algorithm and a corresponding VLSI design for massive multiuser (MU) multiple-input-multiple-output (MIMO) wireless systems. Our algorithm uses alternating direction method of multipliers (ADMM)-based infinity-norm-constrained equalization and is called ADMIN. ADMIN is an iterative algorithm that outperforms linear detectors by a large margin when the ratio between the numbers of base-station (BS) and user antennas is small. In the first iteration, ADMIN computes the linear minimum mean-square error (MMSE) solution, which is sufficient when the ratio between the numbers of BS and user antennas is large. We develop time-shared and iterative VLSI architectures for LDL-decomposition-based soft-output ADMIN supporting 16- and 32-user systems. We present application-specific integrated circuit (ASIC) designs for 16-64 antenna base stations in 28-nm CMOS that supports up to 64 quadrature amplitude modulation (QAM). The 16-user ADMIN ASIC achieves 303 Mb/s while dissipating 85 mW. The 32-user ADMIN ASIC achieves 287 and 241 Mb/s while dissipating 121 and 135 mW for 32 and 64 BS antennas, respectively. ADMIN has also been implemented on a Xilinx Virtex-7 field-programmable gate array (FPGA) and is compared with state-of-the-art massive MIMO data detectors.

Hardening the Channels by Precoder Design in Massive MIMO With Multiple-Antenna Users

In this paper, we investigate the potential of a novel precoder for a massive multiple-input multiple-output (MIMO) system with multiple-antenna users under the influence of both Rayleigh and keyhole channel models. The proposed precoder is based on maximum-ratio processing and aims to enhance the channel hardening effect at the users. This is achieved through dividing each channel vector by its norm square. Closed-form expressions for the achievable spectral efficiency of the conventional and proposed precoders are derived. If modeled with Rayleigh fading, due to the physical hardening property of this channel, a moderate gain in performance is produced by the proposed precoding method, where the highest spectral efficiency gain occurs for a small number of users and user antennas. Under Rayleigh fading, the results also demonstrate that similar performance is returned by increasing the number of users or user antennas. By comparison, the proposed precoder is shown to produce significant performance gains in the keyhole channels under the condition that the system is limited to single-antenna users. Corresponding to this, the results showcase how the performance of massive MIMO under keyhole channels is heavily dependent on increasing the number of served users over user antennas.

Alternating Beamforming With Intelligent Reflecting Surface Element Allocation

Intelligent reflecting surface (IRS) has become a promising technology to aid next generation wireless communication systems. In this paper, we develop an alternating beamforming technique with a novel concept of IRS element allocation for multiple-input multiple-output systems when a base station supports multiple single antenna users aided with a single IRS. Specifically, we allocate each IRS element separately to each user, thus, in the beamforming stage allowing the IRS element only consider a single user at a time. In result to this separation, the complexity is vastly decreased. The proposed beamforming technique tries to maximize the minimum rate of all users with minimal complexity. In the numerical results, we show that the proposed technique is comparable to the convex optimization-based benchmark with sufficiently less complexity.

Spectral efficiency analysis and optimal power allocation for uplink multi-user cell-free massive MIMO networks employing STBCs

Massive MIMO is a key technique in the fifth generation (5G) of wireless networks. This technique, by increasing base station (BS) antennas and exploiting spatial diversity, improves system performance in terms of common criteria such as spectral efficiency. Cell free massive MIMO is considered for 6G mobile communications to overcome the shortcomings associated with the cellular design. In this system, BS antennas are distributed across the system environment and provide uniform service to all users. Also, space time block codes (STBCs) can be used to improve the diversity gain and reliability of the system. In this paper, a cell free massive MIMO system is investigated in the uplink considering effects of channel estimation errors. Users are equipped with two antennas and employ STBC. Lower bound of spectral efficiency is derived for the zero forcing (ZF) and maximal ratio combining (MRC) decoders. Furthermore, spectral efficiency is compared for dual and single antenna users. Moreover, two power control problems are defined based on minimizing the total transmit power and maximizing the minimum rate of the users. Also, an algorithm is proposed for removing the inefficient BSs in the network. The proposed theoretical ideas are evaluated via numerical simulations.

Low-Complexity Near-Optimal Iterative Signal Detection Based on MSD-CG Method for Uplink Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) wireless system is increasingly becoming a vital factor in fifth-generation (5G) communication systems. It is attracting considerable interest due to improve range, spectral efficiency, and coverage as compared to the conventional MIMO systems. In massive MIMO systems, the maximum likelihood detector achieve the optimum performance but it has exponential complexity for realistic antenna configurations systems, Moreover, Linear detectors commonly suffer from a matrix inversion which is not hardware-friendly. There is an increase in the computational complexity associated with the unique benefits of the massive MIMO systems. The system might be classified as an ill-conditioned problem and hence, the signal cannot be detected. To reduce the data detection complexity, we investigate a linear detector based on the multiple search direction conjugate gradient (MSD-CG) in the massive MIMO uplink systems. Several theoretical iterative techniques that can be used to balance complexity and performance for massive MIMO detection have been proposed in the literature. These methods whose convergence rate for common applications is slow where there is a decrease in the base station to user antenna ratio. In this paper, the performance of the CG method has been advanced by a projection method that necessitates a search direction in each sub-domain instead of making all search directions conjugate to each other. In this regard, our results show that the proposed algorithm with realistic antenna configurations is superior to the existing methods in terms of computational complexity for large-scale MIMO systems.

Energy Efficiency Augmentation in Massive MIMO Systems through Linear Precoding Schemes and Power Consumption Modeling

Massive multiple-input multiple-output or massive MIMO system has great potential for 5th generation (5G) wireless communication systems as it is capable of providing game-changing enhancements in area throughput and energy efficiency (EE). This work proposes a realistic and practically implementable EE model for massive MIMO systems while a general and canonical system model is used for single-cell scenario. Linear processing schemes are used for detection and precoding, i.e., minimum mean squared error (MMSE), zero-forcing (ZF), and maximum ratio transmission (MRT/MRC). Moreover, a power dissipation model is proposed that considers overall power consumption in uplink and downlink communications. The proposed model includes the total power consumed by power amplifier and circuit components at the base station (BS) and single antenna user equipment (UE). An optimal number of BS antennas to serve total UEs and the overall transmitted power are also computed. The simulation results confirm considerable improvements in the gain of area throughput and EE, and it also shows that the optimum area throughput and EE can be realized wherein a larger number of antenna arrays at BS are installed for serving a greater number of UEs.

User Scheduling and Antenna Topology in Dense Massive MIMO Networks: An Experimental Study

A massive MIMO network can serve ten’s of users simultaneously. However, in dense scenarios the users are potentially closely-spaced, potentially resulting in substantial inter-user interference. Scheduling can overcome this by selecting the users that lead to the highest combined spectral efficiency. As scheduling comes with a significant pilot overhead, an alternative strategy could minimize user correlation by distributing the antenna elements in space. In this paper, we propose a comprehensive system study including antenna topology and distribution, user scheduling and pilot overhead reduction. Our user scheduling and pilot reduction algorithms are evaluated using system level simulations relying on indoor line-of-sight channel measurements from a 64 antenna base station at 2.61GHz. To have a thorough evaluation of the proposed algorithm, we consider four different antenna topologies, including co-located and distributed placement of the base station arrays. Our evaluation shows that in a conference room with 64 densely deployed users, our proposed low complexity algorithm can improve the spectral efficiency by at least 14% compared to random user selection with the best antenna distribution strategy. Finally, our results show that by relying on channel hardening, we reduce the pilot overhead by 3.2 $\times$ .

An Assistive Technology Solution for User Activity Monitoring Exploiting Passive RFID.

Population ageing is having a direct influence on serious health issues, including hampered mobility and physical decline. Good habits in performing physical activities, in addition to eating and drinking, are essential to improve the life quality of the elderly population. Technological solutions, aiming at increasing awareness or providing reminders to eat/drink regularly, can have a significant impact in this scenario. These solutions enable the possibility to constantly monitor deviations from users’ normal behavior, thus allowing reminders to be provided to users/caregivers. In this context, this paper presents a radio-frequency identification (RFID) system to monitor user’s habits, such as the use of food, beverages, and/or drugs. The device was optimized to fulfill specifications imposed by the addressed application. The approach could be extended for the monitoring of home appliances, environment exploitation, and activity rate. Advantages of the approach compared to other solutions, e.g., based on cameras, are related to the low level of invasiveness and flexibility of the adopted technology. A major contribution of this paper is related to the wide investigation of system behavior, which is aimed to define the optimal working conditions of the system, with regards to the power budget, user (antenna)-tag reading range, and the optimal inter-tag distance. To investigate the performance of the system in tag detection, experiments were performed in a scenario replicating a home environment. To achieve this aim, specificity and sensitivity indexes were computed to provide an objective evaluation of the system performance. For the case considered, if proper conditions are meet, a specificity value of 0.9 and a sensitivity value of 1 were estimated.

Attention-based deep convolutional neural network for spectral efficiency optimization in MIMO systems

Spectral efficiency (SE) optimization in massive multiple input multiple output (MIMO) antenna cognitive systems is a challenge originated from the coexistence restrictions. Traditional power allocation can optimize the SE; however, involving deep learning can meet real-time and fairness processing requirements. In unfair allocation problem, all power is possibly assigned to one or few antennas of a particular user. In this paper, we build a mathematical optimization model considering the fairness problem such that SE is optimized for all users. To implement the model, we propose an attention-based convolutional neural network (Att-CNN), where \(h_0\) and \(h_k\) (i.e., cross-interference and direct channels) attention mechanisms are used to improve the SE. The convolutional neural network is applied to decrease the floating point operations (FLOPs) and number of network parameters. We conducted experiments from these aspects: Fair antenna power allocation, power allocation performance and computational performance. Heat maps with different interference thresholds show the fair allocation for all users. Analyses of SE validate the superiority of the Att-CNN compared with the equal power allocation and fully connected neural network (FNN) schemes. The analyses of the FLOPs and number of parameters show the superiority of the Att-CNN over the FNN.

Efficient Combinations of NOMA With Distributed Antenna Systems Based on Channel Measurements for Mitigating Jamming Attacks

This article aims at proposing new efficient combinations of distributed antenna systems with nonorthogonal multiple access for combating the influence of harmful jamming on a downlink transmission system. A large set of practical channel measurements was performed in an indoor work environment in order to encompass a wide panoply of user positions, antennas, and jammer configurations. Then, three strategies were studied for the selection of subbands, antennas, and transmit powers so as to alleviate the influence of jamming. First, a configuration where the paired users are served by a unique antenna was considered. Then, two other configurations were studied, where paired users are served by two different antennas. It was shown that, in these two strategies, under specific channel and power conditions, it is possible to allow both paired users to perform successive interference cancellation (SIC) to remove interuser interference. The dual-SIC strategy, when applied with joint antenna transmission, presents a good robustness to jamming and yields important throughput gains, compared to the classical single-SIC scenario and single-antenna dual-SIC transmission.

Efficient transmit antenna selection and resource allocation scheme for mmWave D2D networks

Employing switchable directional antennas in the user equipment (UE) is becoming a popular method to replace conventional beamforming methods. This approach is mainly applicable to mmWave networks due to the small size of directional microstrip antenna elements on corresponding frequencies, and requires exploiting proper transmit antenna selection (TAS) schemes to obtain the optimal directivity of selected active antennas. TAS is rather complex in the heterogeneous cellular networks with underlaid D2D communication. For each D2D user, selecting transmit antennas with maximum radiation gain does not necessarily lead to the optimal throughput due to the potential interference that might be imposed on other high priority users, thus, finding the optimal transmit antennas of users is generally a challenging issue in networks with multi-antenna D2D users. In this work, based on Lagrangian relaxation method, we propose a novel and efficient transmit antenna selection and resource (channel and power) allocation (ASRA) for D2D mmWave networks. In order to validate the performance of our proposed algorithm, we devise another efficient low-complexity greedy-based ASRA as well. Numerical results verify the performance of our proposed Lagrangian-based ASRA in comparison to other methods.

Beamforming Optimization for Intelligent Reflecting Surfaces without CSI

This letter considers the communication between a multiple antenna Base Station (BS) and a single antenna user, assisted by an Intelligent Reflecting Surface (IRS). Due to the large number of elements at the IRS, acquiring Channel State Information (CSI) requires many radio-frequency chains and considerable training overhead, what is limiting in practice. We aim to optimize the beamforming at the BS and IRS without CSI, by minimizing the transmit power, subject to a minimum signal-to-noise ratio (SNR). To solve this problem, a new method based on the Particle Swarm Optimization (PSO) technique is proposed. Results show that the new solution achieves similar performance as that of the optimal beamforming with CSI.

Efficient Pre-Conditioned Descent Search Detector for Massive MU-MIMO

Massive multi-user (MU) multiple-input multiple-output (MIMO) is an enabling technology for the next-generation wireless systems as it provides significant improvements in terms of spectral efficiency and per-user peak data rates compared to existing, small-scale MIMO. The presence of increasing antennas at the infrastructure base-station results, however, in high computational complexity for data detection, which requires low-complexity algorithms and efficient hardware designs. Descent search (DS) algorithms have been proposed to reduce complexity of data detection and enable efficient hardware implementations. However, their error-rate performance is not always satisfactory for channels that exhibit correlation or for systems with small base-station to user antenna ratios. In this paper, we propose a linear inequality constraint quadratic programming (LICQP) model, which enables the design of a constrained DS (CDS) detector that outperforms existing DS algorithms. To further improve the performance of CDS in the presence of challenging channel conditions, we propose a universal pre-conditioner. Theoretical analysis and numerical results indicate that CDS with pre-conditioning reduces the complexity over the state-of-the-art (SOA) by $60\%$ . We develop a reference Xilinx Virtex-7 FPGA design, which demonstrates that our implementation achieves $1.75\boldsymbol{\times }$ higher throughput (35 Mbps in a 128 antenna, 8 user system) at comparable hardware resource utilization.