In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

Device-to-device transmission modes in NOMA network with and without Wireless Power Transfer

NOMA and 5G emerging technologies: A survey on issues and solution techniques

Massive MIMO (multiple-input-multiple-output) is one of the key technologies of 5G mobile cellular networks, which can form a huge antenna array by providing a large number of antennas at the cell base station. It will greatly improve the channel capacity and spectrum utilization and has become a hotspot in the field of wireless communications in recent years. Aiming at the high complexity of channel estimation algorithm for massive MIMO system, a sparse channel estimation algorithm with low complexity is proposed based on the inherent sparsity of wireless communication channel. The algorithm separates the channel taps from the noise space on the basis of the traditional discrete Fourier transform (DFT) channel estimation, so that the channel estimation only needs to calculate the part of the channel tap, so the computational complexity of the algorithm is greatly reduced. The simulation results show that the proposed algorithm can achieve near minimum mean square error (MMSE) performance while maintaining low complexity. Moreover, the Bit Error Rate and Inter-Cell Interference also indicates that the proposed improved algorithm shows better overall performance than the conventional algorithms which makes it suitable from practical perspective.

Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems

In this paper, we propose and analyze a downlink non-orthogonal multiple access (NOMA) relay system with full-duplex transmission model and decode-and-forward (DF) scheme. We assume that the source and the destination nodes have fixed power, whereas relay nodes have constrained energies and should harvest radio frequency (RF) energy from the source for operation power. The outage performance of this system is analyzed over the Nakagami-m fading channel, full-duplex transmission protocol and imperfect channel state information (CSI), and then the duration of energy harvesting in every block time, α T , is optimized to achieve the maximal throughput. Furthermore, the closed-form of outage probabilities and the fraction α are derived and evaluated in several scenarios. Moreover, the optimal power allocation coefficients that achieve the minimal outage probability while keeping the fairness in outage performance of end users are discussed. The analytical results are verified by Monte Carlo simulations.

Optimizing duration of energy harvesting for downlink NOMA full-duplex over Nakagami-m fading channel

In this paper, we present a robust beamforming design to tackle the weighted sum-rate maximization (WSRM) problem in a multicell multiple-input multiple-output (MIMO) – non-orthogonal multiple access (NOMA) downlink system for 5G wireless communications This work consider the imperfect channel state information (CSI) at the base station (BS) by adding uncertainties to channel estimation matrices as the worst-case model ie, singular value uncertainty model (SVUM) With this observation, the WSRM problem is formulated subject to the transmit power constraints at the BS The objective problem is known as non-deterministic polynomial (NP) problem which is difficult to solve We propose an robust beamforming design which establishes on majorization minimization (MM) technique to find the optimal transmit beamforming matrix, as well as efficiently solve the objective problem In addition, we also propose a joint user clustering and power allocation (JUCPA) algorithm in which the best user pair is selected as a cluster to attain a higher sum-rate Extensive numerical results are provided to show that the proposed robust beamforming design together with the proposed JUCPA algorithm significantly increases the performance in term of sum-rate as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme

User clustering and robust beamforming design in multicell MIMO-NOMA system for 5G communications

In this paper, we examine a joint user pairing and dynamic power allocation (JUPDPA) design to maximize the energy efficiency (EE) in the multicell multiple-input multiple-output (MIMO) — non-orthogonal multiple access (NOMA) downlink system. A novel JUPDPA scheme is proposed based on median and the euclidean norm of the channel vectors. In addition, base station dynamically allocates the transmission power to the paired users. Comprehensive numerical results illustrate that the proposed scheme attains higher energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

A novel joint user pairing and dynamic power allocation scheme in MIMO-NOMA system

In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE) maximization problem in a 5G massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) downlink system with imperfect channel state information (CSI) at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA) algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM). A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP) that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach's algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System

To accelerate future intelligent wireless systems , we designed an energy-efficient Massive multiple-input-multiple-output (MIMO)- non-orthogonal multiple access (NOMA) aided internet of things (IoT) network in this paper to support the massive number of distributed users and IoT devices with seamless data transfer and maintain connectivity between them. Massive MIMO has been identified as a suitable technology to implement the energy efficient IoT network in beyond 5G (B5G) communications due to its distinct characteristics with large number of antennas. However, to provide fast data transfer and maintain hyper connectivity between the IoT devices in B5G communications will bring the challenge of energy deficiency. Hence, we considered a massive MIMO–NOMA aided IoT network considering imperfect channel state information and practical power consumption at the transmitter. The far users of the base stations are selected to investigate the power consumption and quality of service. Then, calculate the power consumption which is non-convex function and non-deterministic polynomial problem. To solve the above problem, fractional programming properties are applied which converted polynomial problem into the difference of convex function . And then we employed the successive convex approximation technique to represent the non-convex to convex function. Effective iterative based branch and the reduced bound process are utilized to solve the problem. Numerical results observe that our implemented approach surpasses previous standard algorithms on the basis of convergence, energy-efficiency and user fairness. 

Poongundran Selvaprabhu

Papers

User clustering and robust beamforming design in multicell MIMO-NOMA system for 5G communications

A novel joint user pairing and dynamic power allocation scheme in MIMO-NOMA system

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System

Energy efficient MIMO-NOMA aided IoT network in B5G communications

Worst-case weighted sum-rate maximization in multicell massive MIMO downlink system for 5G communications