Location aware sensor routing (LASeR) protocol for mobile wireless sensor networks

Location aware sensor routing (LASeR) protocol is a novel solution to the challenges of routing in mobile wireless sensor networks (MWSNs). It addresses the high reliability and low latency requirements of emerging applications. The protocol uses location information to maintain a gradient field even in highly mobile environments, whilst reducing the routing overhead. This allows the protocol to utilise a blind forwarding technique to propagate packets towards the sink. The protocol inherently utilises multiple paths simultaneously to create route diversity and increase its robustness. LASeR is designed for use in a high variety of MWSN applications with autonomous land, sea or air vehicles. Analytical expressions are derived and evaluated against the simulations. Extensive modelling and simulation of the proposed routing protocol has shown it to be highly adaptable and robust. It is compared with the recent MWSN proactive highly ambulatory sensor routing protocol, the high performance mobility adaptive cross-layer routing protocol, as well as ad-hoc on-demand distance vector and optimised link state routing. Protocols are evaluated on packet delivery ratio, end-to-end delay, overhead, throughput and energy consumption. The results highlight both the high performance of LASeR in various challenging environments and its superiority over the state-of-the-art

A closed-form approximation of correlated multiuser MIMO ergodic capacity with antenna selection and imperfect channel estimation

Abstract—Antenna selection (AS) is a promising technology to substantially reduce the complexity of massive multiple-input multiple-output (MIMO) systems. However, spatial correlation and imperfect estimation of channel state information (CSI) are well known to have a direct impact on the capacity of feasible MIMO schemes. In this paper, a tight closed-form approximation of the ergodic capacity for correlated Rayleigh fading multiuser MIMO channels with receive AS and imperfect CSI is presented. The derived expression takes into account the spatial correlation at both link sides and channel estimation error at the receiver. It can be used for arbitrary numbers of users, antennas, and receive RF chains. Furthermore, a concise analytical capacity formula is derived in the high signal-to-noise ratio (SNR) region. Numerical results validate the accuracy of our closed-form expressions over different channel conditions and SNRs. The new capacity approximation extends the state-of-the-art and enables efficient performance evaluation of varied multiantenna applications including massive MIMO for 5G systems

Extending the user capacity of MU-MIMO systems with low detection complexity and receive diversity

Multiple-input multiple-output (MIMO) based technologies are considered as an integral part of the upcoming 5G communications to fulfil the ever-increasing demands of wireless applications with high spectral efficiency requirements. However, in uplink multiuser MIMO (MU-MIMO) channels, the number of allowed users is limited by the number of receive antennas associated with radio frequency (RF) chains at the base-station and the complexity burden of multiuser detection (MUD). In this paper, a novel group layer MU-MIMO scheme with low complexity MUD is proposed to increase the number of served users well beyond the available RF chains. By taking the advantage of power control and inherent path loss in cellular systems, the allowed users are divided into groups based on their received power. Efficient group power allocation and group layer MUD (GL-MUD) are utilized to provide a valuable tradeoff between complexity and achieved performance. Furthermore, when more receive antennas than RF chains is implemented, a generalized norm based antenna selection algorithm is proposed to enhance the error performance. Symbol error probability expressions are derived and the effectiveness of proposed scheme is demonstrated through numerical simulations compared with the conventional MU-MIMO and non-orthogonal multiple-access (NOMA) systems over Rayleigh fading channels. The results show a substantial increase in user capacity up to two-fold for the available number of RF chains. In addition, significant signal-to-noise ratio gain is achieved using GL-MUD compared with different MUD techniques

Efficient user clustering, receive antenna selection, and power allocation algorithms for massive MIMO-NOMA systems

Massive multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA)-based technologies are considered as essential parts in the 5G systems to fulfill the escalating demands of higher connectivity and data rates for emerging wireless applications. In this paper, a new approach of massive MIMO-NOMA with receive antenna selection (RAS) is considered for the uplink channel to significantly increase the number of connected devices and overall sum rate capacity with improved user-fairness and less complexity. The proposed scheme is designed from two multiuser MIMO (MU-MIMO) clusters, based on the available number of radio frequency chains (RFCs) at the base station and channel conditions, followed by power-domain NOMA for the simultaneous signal transmission. We derive the sum rate and capacity region expressions for MIMO-NOMA with RAS over Rayleigh fading channels. Then, an optimal and three highly efficient sub-optimal dynamic user clustering, RAS, and power allocation algorithms are proposed for sum rate maximization under received power constraints and minimum rate requirements of the allowed users. The effectiveness of designed algorithms is verified through extensive analysis and numerical simulations compared to the reference MU-MIMO and MIMO-NOMA systems. The achieved results show a substantial increase in connectivity, up to two-fold for the accessible number of RFCs, and overall sum rate capacity while satisfying the minimum users’ rates. Besides, important tradeoffs can be realized between system performances, hardware and computational complexities, and desired user-fairness in terms of serving more users with equal/unequal rates

Using K-fold cross validation proposed models for SpikeProp learning enhancements

Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte's algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets

Density functional theory study of molecular structure, Electronic properties, UV–Vis spectra on coumarin102.

The various properties of the ground and excited electronic states of coumarins 102 using density functional theory (DFT) and time-dependent density functional theory (TDDFT) was calculated by the B3LYP density functional model with 6-31G(d,p) basis set by Gaussian 09 W program. Spectral characteristics of coumarin102 have been probed into by methods of experimental UV-visible, and quantum chemistry. The UV spectrum was measured in methanol. The optimized structures, total energies, electronic states (HOMO- LUMO), energy gap, ionization potentials, electron affinities, chemical potential, global hardness, softness, global electrophilictity, and dipole moment were measured. We find good agreement between experimental data of UV spectrum and TDDFT excitationenergies