The authors therefore, acknowledge with thanks DSR for technical and financial support.

Uplink and Downlink channel estimation in massive Multiple Input Multiple Output (MIMO) systems is an intricate issue because of the increasing channel matrix dimensions. The channel feedback overhead using traditional codebook schemes is very large, which consumes more bandwidth and decreases the overall system efficiency. The purpose of this paper is to decrease the channel estimation overhead by taking the advantage of sparse attributes and also to optimize the Energy Efficiency (EE) of the system. To cope with this issue, we propose a novel approach by using Compressed-Sensing (CS), Block Iterative-Support-Detection (Block-ISD), Angle-of-Departure (AoD) and Structured Compressive Sampling Matching Pursuit (S-CoSaMP) algorithms to reduce the channel estimation overhead and compare them with the traditional algorithms. The CS uses temporal-correlation of time-varying channels to produce Differential-Channel Impulse Response (DCIR) among two CIRs that are adjacent in time-slots. DCIR has greater sparsity than the conventional CIRs as it can be easily compressed. The Block-ISD uses spatial-correlation of the channels to obtain the block-sparsity which results in lower pilot-overhead. AoD quantizes the channels whose path-AoDs variation is slower than path-gains and such information is utilized for reducing the overhead. S-CoSaMP deploys structured-sparsity to obtain reliable Channel-State-Information (CSI). MATLAB simulation results show that the proposed CS based algorithms reduce the feedback and pilot-overhead by a significant percentage and also improve the system capacity as compared with the traditional algorithms. Moreover, the EE level increases with increasing Base Station (BS) density, UE density and lowering hardware impairments level.

Traditional channel estimation algorithms such as minimum mean square error (MMSE) are widely used in massive multiple-input multiple-output (MIMO) systems, but require a matrix inversion operation and an enormous amount of computations, which result in high computational complexity and make them impractical to implement. To overcome the matrix inversion problem, we propose a computationally efficient hybrid steepest descent Gauss–Seidel (SDGS) joint detection, which directly estimates the user’s transmitted symbol vector, and can quickly converge to obtain an ideal estimation value with a few simple iterations. Moreover, signal detection performance was further improved by utilizing the bit log-likelihood ratio (LLR) for soft channel decoding. Simulation results showed that the proposed algorithm had better channel estimation performance, which improved the signal detection by 31.68% while the complexity was reduced by 45.72%, compared with the existing algorithms.

In this paper, we propose a random access protocol for vehicular-to-infrastructure communications. We consider the case where an unmanned aerial vehicle (UAV) provides assistance to a roadside unit to enhance the system throughput. In a traditional carrier sense multiple access schemes (CSMA), the vehicle senses the channel first and it does not transmit the data until the channel is free. However, the CSMA has been shown to be often wasteful of resources and includes potentially unbounded channel access delays in dense networks. In this paper, we use the capture effect, where collisions can be resolved, provided the signal-to-interference-plus-noise ratio is larger than a predetermined threshold. Moreover, we show that the access probability of the vehicles can be optimized based on the known density of the network to maximize throughput. Based on the proposed random access protocol, we model the behavior of the vehicles using a two-dimensional Markov chain and derive the expression for the average system throughput. Finally, we propose two transmission power control schemes to further enhance system throughput. We present extensive simulation results to show that the UAV can provide 9%-38% improvement in throughput for variable network densities. INDEX TERMSVehicular and wireless technologies, unmanned aerial vehicles, wireless communication, wireless network, CSMA, Markov chain, random access, capture effect. I. INTRODUCTION Vehicular communication networks have received huge attention from the research community as well as the industry, for its potential to enhance road safety, traffic efficiency, and on-board information and entertainment. Some of the main communication standards developed for vehicular networks include the dedicated short range communication (DSRC) in the US [1]. DSRC is based on IEEE 802.11p [2], which is part of the wireless access in vehicular environments (WAVE) architecture. IEEE 802.11p uses carrier sense multiple access with collision avoidance (CSMA/ CA) [3], originally designed for wireless local area networks (WLAN). However, while WLAN is characterized by low mobility and low density, vehicular networks are notorious for their dynamic topology, high-mobility environment, and requirement for scalability for high density networks. Thus, researchers have recently grown skeptical of the CSMA/CA for vehicular networks [3], [4].In this regards, the 3rd Generation Partnership Project (3GPP) proposed long term evolution vehicle-to-everything (LTE V2X) [5], which offers cellular networks to vehicles, based on D2D communications. LTE V2X is still, however, a centralized approach as it relies heavily on the network for time and frequency synchronization. In general, centralized approaches are more reliable than CSMA/CA and, thus, more suitable for critical safety-related messages. However, centralized approaches incur large control overhead, require control channels and infrastructure as well as complex resource allocation algorithms. Thus, they might be unnecessarily costly for less critic...

This paper gives an insight on the performance of mixed dual-hop radio-frequency (RF)-underwater optical wireless communication (UOWC) systems. The system consists of multipleinput multiple-output (MIMO) RF hop employing Nakagami-m fading channel on the source (S) node communicating with a destination node (D) considered as the legitimate receiver via an amplify-and-forward (AF) relay (R) node equipped with multiple RF antennas for reception. It considers transmit antenna selection (TAS) scheme for communication in the MIMO RF hop while the information is transmitted from the S node to the D node, i.e. submarine etc., via the UOWC hop. Specifically, the R node receives incoming information messages from S node via MIMO RF links, applies maximal-ratio combining (MRC) technique, amplifies the output combined signal, and subsequently forwards it to the destination utilising a variable gain relaying (VGR) via an UOWC link. We derive exact closed-form expressions for the system's end-toend (E2E) statistical channel characteristics. Our derived analytical expressions present an efficient technique to depict the impact of our system and channel parameters on the performance, namely the varying number of increasing antennas N t = N r = 2, 3, 4 or more from the S node towards R node and the involvement of underwater detection techniques of r = 1 for heterodyne detection and r = 2 for intensity modulation/direct detection (IM/DD) in the underwater turbulence severity of the UOWC link. Outage probability (OP) and average bit error rate (BER) closed-form expressions for the varying bubble levels (BL) (L/min) for different scenarios, varying temperature gradients (TG) (°C cm −1 ), different fresh and saline waters, and various binary modulation techniques have been accurately validated for the E2E system presented in this work along with the tightness of their respective high-end asymptotes.

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Routing protocols in Mobile Ad Hoc Networks (MANETs) play a pivotal role in ensuring quality of service (QoS) and improving network performance. Selection of optimal routing protocol and suitable parameters for a given network scenario is a major task that ultimately affects the behavior of network. This work exploits machine learning (ML) techniques for the selection of adequate routing parameters and protocol by regression of parameters in given network scenario to ensure optimal performance. The network is trained based on parametric setup of expanding ring search mechanism (ERS) and random early detection (RED) technique to estimate network throughput, end to end (E2E) delay and packets delivery ratio (PDR) and is tested via wide-ranging simulations in varying network topologies. Both RED and ERS mechanisms are aimed to control link and node level congestion in the reactive routing protocols and our aim is to select the best suited parameters for given network topologies based on ERS and RED parametric setups and improve performance for ensuring QoS. ML algorithms are trained and tested for their performance in varying network topologies. We have exploited these models with best performance for ERS and RED based routing in given topological arrangements. The performance of the ML algorithms is evaluated on the basis of root mean squared error (RMSE) and mean absolute error (MAE) for regression settings. Prediction models with up to par RMSE and MAE out-turns are attained and exploited for selection of suitable ERS and RED parameters and routing protocols in order to ensure the QoS for given network scenario. Variants of standard routing protocols are devised based on their performance and the ML techniques are exploited for prediction of QoS parameters to decide on the optimal variant that attains significant improvement in performance. Results are shown to confirm that considerable improvement in QoS is attained. INDEX TERMS Ad hoc multi hop wireless networks, congestion control, expanding ring search, machine learning, mobile ad hoc networks, on demand routing protocols, random early detection, regression, quality of service.

Efficient and Symmetry based precoding plays a key role in wireless communications. In order to improve the transmission performance of multi-user millimeter wave Multiple-Input Multiple-Output (MIMO) (MU-mmWave MIMO) systems, this paper proposes an analog precoding scheme for the receiver of mmWave MIMO with split sub-array hybrid analog and digital architecture. Then, we propose a hybrid analog and digital precoding algorithm based on channel reciprocity (APoCR) to maximize the spectral efficiency by utilizing the triple joint optimization problem, which can be divided into the analog and digital part. The analog combination vectors (ACVs) are obtained by the signal-to-interference-and-noise ratio (SINR) reception maximization of each downlink user and the analog precoding vectors (APVs) are obtained by the SINR reception maximization of each uplink antenna array. The digital precoder of the transmitter is designed after the analog part optimization to alleviate the interference between multiple data streams of the users. The simulation results show that the proposed precoding algorithm has a better sum rate, fast convergence, and improved SINR than the other state-of-the-art algorithms.