Multiple-input Single-output Wireless Research Articles

IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches

This paper concentrates on the problem of associating an intelligent reflecting surface (IRS) to multiple users in a multiple-input single-output (MISO) downlink wireless communication network. The main objective of the paper is to maximize the sum-rate of all users by solving the joint optimization problem of the IRS-user association, IRS reflection, and BS beamforming, formulated as a non-convex mixed-integer optimization problem. The variable separation and relaxation are used to transform the problem into three convex sub-problems, which are alternatively solved through the convex optimization (CO) method. The major drawback of the proposed CO-based algorithm is high computational complexity. Thus, we make use of machine learning (ML) to tackle this problem. To this end, first, we convert the optimization problem into a regression problem. Then, we solve it with feed-forward neural networks (FNNs), trained by CO-based generated data. Simulation results show that the proposed ML-based algorithm has a performance equivalent to the CO-based algorithm, but with less computation complexity due to its offline training procedure.

Hybrid machine learning detection for orbital angular momentum over turbulent MISO wireless channel

This work proposes a machine learning detection scheme for wireless orbital angular momentum (OAM) communication systems. The new scheme works for single-input–single-output (SISO) and multiple-input–single-output (MISO) wireless communication between the transmitter and the receiver. The transmitter encodes its data in OAM modes which are constructed using Laguerre–Gaussian beam form. The transmitted beams travel through a wireless channel with weak to medium turbulence strength and they arrive at random positions on the same receiver area. The authors proposed detection scheme allows the reception of multiple overlapping beams without prior knowledge of beams centre positions. The proposed scheme uses a novel technique of receiver segmentation and space filtering along with neural network to decode the received beams. Simulations show the detection efficiency and the enhanced performance of their proposed scheme for the SISO case and the MISO case with 2, 4, and 16 beams.

Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for MISO Communication Systems

This letter investigates the intelligent reflecting surface (IRS)-aided multiple-input single-output wireless transmission system. Particularly, the optimization of the passive phase shift of each element at IRS to maximize the downlink received signal-to-noise ratio is considered. Inspired by the huge success of deep reinforcement learning (DRL) on resolving complicated control problems, we develop a DRL based framework to solve this non-convex optimization problem. Numerical results reveal that the proposed DRL based framework can achieve almost the upper bound of the received SNR with relatively low time consumption.

Bit error rate performance analysis of AC-MAP in multiple input single output wireless relay network

In this paper, we propose a joint decoding scheme called AC-MAP decoder for multiple input single output (MISO) wireless cooperative communication network that consists of single source, single relay, and single destination. The proposed scheme is based on both Alamouti combining (AC) scheme and maximum a posteriori (MAP) decoder and is used to estimate the data at the destination. The AC-MAP decoder is optimal in the sense that it minimizes the end-to-end bit error rate (BER). In order to analyze performance of the proposed decoder, we derive a closed form expression for the upper bound (UB) on the end-to-end error probability. Distances between system nodes, transmit energy, and channel noise and fading effects are considered in the derivation of the UB. Numerical results show that the closed form UB is very tight and it almost coincides with the exact BER results obtained from simulations. Therefore, we use the derived UB expression to study the effects of the relay position on the BER performance and to find the optimal location of the relay node.

Optimal Joint Channel Estimation and Data Detection for Massive SIMO Wireless Systems: A Polynomial Complexity Solution

By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constant-modulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (T) and the number of receive antennas (N), even when the number of receive antennas grows polynomially in the channel coherence time (N=O(T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> ) suffices to guarantee an expected computational complexity cubic in T and linear in N). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.

Joint Power Control and Rate Allocation Enabling Ultra-Reliability and Energy Efficiency in SIMO Wireless Networks

Coming cellular systems are envisioned to open up to new services with stringent reliability and energy efficiency requirements. In this paper we focus on the joint power control and rate allocation problem in Single-Input Multiple-Output (SIMO) wireless systems with Rayleigh fading and stringent reliability constraints. We propose an allocation scheme that maximizes the energy efficiency of the system while making use only of average statistics of the signal and interference, and the number of antennas $M$ that are available at the receiver side. We show the superiority of the Maximum Ratio Combining (MRC) scheme over Selection Combining (SC) in terms of energy efficiency, and prove that the gap between the optimum allocated resources converges to $(M!)^(1/(2M))$ as the reliability constraint becomes more stringent. Meanwhile, in most cases MRC was also shown to be more energy efficient than Switch and Stay Combining (SSC) scheme, although this does not hold only when operating with extremely large $M$, extremely high/small average signal/interference power and/or highly power consuming receiving circuitry. Numerical results show the feasibility of the ultra-reliable operation when $M$ increases, while greater the fixed power consumption and/or drain efficiency of the transmit amplifier is, the greater the optimum transmit power and rate.

User-Centric Energy Efficiency Optimization for MISO Wireless Powered Communications

We consider a multiple-input single-output wireless powered communication network (WPCN), where the single-antenna users harvest energy from a multi-antenna access point (AP) and then transmit information back to the AP. With multiple antennas, the AP can perform energy beamforming in the downlink and exploit multiplexing- or (receive) beamforming-gain in the uplink. We consider maximizing the sum of the users' energy efficiencies (EEs) by jointly optimizing the energy beamforming of the AP, transmit powers of the users, and time allocation. We formulate EE maximization problems for both TDMA-based WPCN (T-WPCN) and SDMA-based WPCN (S-WPCN), which are non-convex because they have the sum-of-ratios objective functions. We optimally solve the former by reformulating it into an equivalent parametric problem, whose solution can be obtained by iteratively solving convex problems, while the latter is optimally solved by convexifying it using the so-called feasible set reduction scheme. We show that the T-WPCN outperforms S-WPCN when there is no minimum throughput requirement, while we can observe that the S-WPCN outperforms T-WPCN when minimum throughput requirement is high. The simulation results verify our theoretical findings and demonstrate the effectiveness of our proposed schemes.

Capacity Results on Multiple-Input Single-Output Wireless Optical Channels

This paper derives upper and lower bounds on the capacity of the multiple-input single-output free-space optical intensity channel with signal-independent additive Gaussian noise subject to both an average-intensity and a peak-intensity constraint. In the limit where the signal-to-noise ratio (SNR) tends to infinity, the asymptotic capacity is specified, while in the limit where the SNR tends to zero, the exact slope of the capacity is given.

VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems

Channel estimation errors have a critical impact on the reliability of wireless communication systems. While virtually all existing wireless receivers separate channel estimation from data detection, it is well known that joint channel estimation and data detection (JED) significantly outperforms conventional methods at the cost of high computational complexity. In this paper, we propose a novel JED algorithm and corresponding VLSI designs for large single-input multiple-output (SIMO) wireless systems that use constant-modulus constellations. The proposed algorithm is referred to as PRojection Onto conveX hull (PrOX) and relies on biconvex relaxation (BCR), which enables us to efficiently compute an approximate solution of the maximum-likelihood JED problem. Since BCR solves a biconvex problem via alternating optimization, we provide a theoretical convergence analysis for PrOX. We design a scalable, high-throughput VLSI architecture that uses a linear array of processing elements to minimize hardware complexity. We develop corresponding field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs, and we demonstrate that PrOX significantly outperforms the only other existing JED design in terms of throughput, hardware-efficiency, and energy-efficiency.

Performance Analysis of DF/AF Cooperative MISO Wireless Sensor Networks With NOMA and SWIPT Over Nakagami-&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$m$ &lt;/tex-math&gt; &lt;/inline-formula&gt; Fading

In this paper, we investigate downlink cooperative multiple-input single-output wireless sensor networks with the nonorthogonal multiple access technique and simultaneous wireless information and power transfer over Nakagami- $m$ fading. Specifically, the considered network includes a multiantenna sink node, an energy-limited relay cluster, a high-priority sensor node (SN) cluster, and a low-priority SN cluster. Prior to transmission, a transmit antenna, a relay, a high-priority SN, and a low-priority SN are selected. In this paper, we propose three antenna-relay-destination selection schemes, i.e., sink node-high-priority, sink node-relay, and sink node-low-priority. In each proposed scheme, we consider two relaying strategies, i.e., decode-and-forward and amplify-and-forward, and then, we derive the corresponding closed-form expressions of outage probability at the selected SNs. In addition, we introduce two algorithms: 1) the power-splitting ratio optimization algorithm and 2) the best antenna-relay-destination selection determination algorithm. Finally, we utilize the Monte Carlo simulations to verify our analytical results.

Optimal time allocation for multi-antenna wireless powered heterogeneous sensor network communications under imperfect CSI

Energy harvesting is a promising technology to overcome the energy bottleneck in battery-powered heterogeneous sensor networks (HSNs). In an energy harvesting HSN, heterogeneous sensor nodes harvest energy to power themselves, and use the harvested energy for data transmission. This paper investigates a multiple-input single-output wireless powered HSN with imperfect channel state information (CSI). The receiver harvests energy from the power transmitter via radio-frequency wireless power transfer (WPT) in the downlink to support wireless information transfer (WIT) in the uplink. To improve WPT efficiency, the power transmitter employs energy beamforming by using instantaneous CSI. Under imperfect CSI, the receiver performs channel estimation and feeds the CSI back to the transmitter. We focus on balancing the time duration for channel estimation, WPT, and WIT, so as to maximize the throughput while satisfying the time duration constraint. By solving this optimizing problem, we derive the optimal time resource allocation scheme. Numerical simulation results demonstrate the effectiveness of the proposed scheme.

Traffic-Aware Training and Scheduling for MISO Wireless Downlink Systems

In this paper, the problem of feedback and active user selection in multiple-input single-output (MISO) wireless systems such that the system's stability region is as big as possible is examined. The focus is on a system in a Rayleigh fading environment where zero forcing precoding is used to serve all active users in every slot. Acquisition of the channel states is done via uplink training in time division duplexing mode by the active users. Clearly, only a subset of users can perform uplink training and the selection of this subset is a challenging and interesting problem especially in MISO systems. The stability regions of a baseline centralized scheme and two novel decentralized policies are examined analytically. In the decentralized schemes, the transmitter broadcasts periodically the queue state information and the users contend for the channel in a carrier sense multiple access-based manner with parameters based on the outdated queue state information and real-time channel state information. We show that, using infrequent signaling between the base station and the users, the decentralized policies outperform the centralized policy. In addition, a threshold-based user selection and training scheme for discrete-time contention is proposed. The results of this paper imply that, as far as stability is concerned, the users must be involved in the active user selection and feedback/training decision. This should be leveraged in future communication systems.

SIMO channel performance evaluation on indoor environment at 2.4 GHz

This work presents an experimental study of single-input multiple-output (SIMO) channel performance in the indoor radio propagation environment. Indoor channel measurements at the 2.4-GHz ISM frequency band have been conducted using a versatile channel sounder test-bed platform. A single transmitting antenna, four receiving antennas with two proposed geometries and a four-branch receiver circuitry were used in order to achieve channel sounder measurements exploiting baseband signal-processing techniques. In-depth investigation of SIMO wireless channel performance was realised through three types of metrics: signal strength, gain coefficient and capacity. Performance results indicate SIMO channel capacity enhancement and illustrate differences between the two proposed geometries.

A Novel Strategy for Performance Improvement of MISO Wireless System using Alamouti STBC with 4th order FIR Filter

wireless communication system is highly desirable field among the researchers these days because of the increasing demand of high speed of networks to serve on the next generation mobile devices. The various researches already completed but the reliability like leased line hasn't achieved yet. The end to end performance of the system still needs to be improved. In this paper same we are trying to improve. The figure of merit is Bit Error Rate (BER). The proposed approach depends on multiple input single output (MISO) system which significantly improves the performance. Now to improve more the Alamouti STBC with 4th Order FIR filter is implemented. The filtering approach have changed picture of the system by reducing the error probability. BER performance achieved in this paper is better than existing work.

Multiple-Input Single-Output Wireless Power Transmission System for Coal Mine

In Wireless Power Transmission (WPT) systems, the proportion of energy received by the load is critical and the efficiency is the more significant parameter. The magnetic multiple input algorithms as well as many multiple overlapping coils structures in the transmitter side are proposed for the coal mine Internet of the Things (IOT) nodes. Different phase shifts cases for the input signal were considered to study their effect on the efficiency. Simulation results showed that the axial component of magnetic induction resulted from the multiple overlapping coils structure has preferable homogeneous distribution over the traditional single input coil. The proposed system achieved an efficiency value of 60 % for a receiver placed 40 cm away from a 4 coils transmitter comparing to an efficiency value of 50% for the same distance using a single coil transmitter. Simulation results shows that the proposed model is well adapted for the movable charging mode for the IOT nodes in coal mine. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5439 Full Text: PDF

Trellis-Coded Quantization of Phases in MISO Wireless Systems

This correspondence considers multiple-input single-output (MISO) wireless systems with equal gain transmission (EGT) at the transmitter and maximum ratio combining (MRC) at the receiver. In such systems, the receiver feedbacks the elemental phases of beam-forming vectors to the transmitter. Due to finite feedback rate, the elemental phases of beam-forming vectors have to be quantized effectively before feedback. This correspondence makes use of the trellis-coded quantization (TCQ) to quantize elemental phases of beam-forming vectors. The principle of TCQ and its application to phase quantization are introduced in detail. Representative simulation results are given to verify the superiority of TCQ phase quantization over other quantization schemes. The research results can be applied to the image information such as wireless transmission systems on many agricultural robots to work together, improve the efficiency and reliability of information transmission DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1579 Full Text: PDF

Encrypted message transmission in a QO-STBC encoded MISO wireless Communication system under implementation of low complexity ML decoding algorithm

In this paper, we made a comprehensive BER simulation study of a quasi- orthogonal space time block encoded (QO-STBC) multiple-input single output(MISO) system. The communication system under investigation has incorporated four digital modulations (QPSK, QAM, 16PSK and 16QAM) over an Additative White Gaussian Noise (AWGN) and Raleigh fading channels for three transmit and one receive antennas. In its FEC channel coding section, three schemes such as Cyclic, Reed-Solomon and ½-rated convolutionally encoding have been used. Under implementation of merely low complexity ML decoding based channel estimation and RSA cryptographic encoding /decoding algorithms, it is observable from conducted simulation test on encrypted text message transmission that the communication system with QAM digital modulation and ½-rated convolutionally encoding techniques is highly effective to combat inherent interferences under Raleigh fading and additive white Gaussian noise (AWGN) channels. It is also noticeable from the study that the retrieving performance of the communication system degrades with the lowering of the signal to noise ratio (SNR) and increasing in order of modulation.

Behavioral Modeling of MIMO Nonlinear Systems With Multivariable Polynomials

This paper proposes a novel behavioral model for multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) nonlinear transmitters based on multivariable polynomials (MVPs). The main source of nonlinearity in these transmitters is the RF power amplifier, which is commonly modeled using polynomial models. The proposed MVP model is capable of handling the nonlinear effects of the RF transmitters, as well as the linear and nonlinear crosstalk between the input signals. At the same time, the developed model was optimized for computing efficiency without compromising its accuracy. The model was tested for MISO and MIMO wireless transmitters. The simulations and measurement results revealed that the proposed model gives excellent accuracy when modeling MIMO transmitters with different branch coupling factors.

Fiber Aided Wireless Network Architecture

We introduce the concept of a fiber aided wireless network architecture (FAWNA), which allows high-speed mobile connectivity by leveraging the speed of optical networks. Specifically, we consider a single-input, multiple-output (SIMO) FAWNA, which consists of a SIMO wireless channel interfaced with an optical fiber channel through wireless-optical interfaces. We propose a design where the received wireless signal at each interface is sampled and quantized before being sent over the fiber. The capacity of our scheme approaches the capacity of the architecture, exponentially with fiber capacity. We also show that for a given fiber capacity, there is an optimal operating wireless bandwidth and number of interfaces. We show that the optimal way to divide the fiber capacity among the interfaces is to ensure that each interface gets enough rate so that its noise is dominated by front end noise rather than by quantizer distortion. We also show that rather than dynamically change rate allocation based on channel state, a less complex, fixed rate allocation scheme can be adopted with very small loss in performance.