Energy Efficient Power Allocation in Massive MIMO NOMA Systems Based on SIF Using Cell Division Technique

Pourrostam

2021

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Summary In this paper, we study energy‐efficient power allocation (PA) to secure the confidential information of massive multiple‐input multiple‐output (MIMO) systems in the presence of an eavesdropper. First a training scheme is presented in the uplink using the MMSE method. Based on the estimated channel state information (CSI), the base station (BS) encrypts the confidential information. Next, the ergodic secret rate is obtained at the downlink. In this paper, in order to maximize the ergodic secrecy rate, the allocation of common power in the uplink and downlink is proposed by considering the constraints such as the transmit power constraint in the uplink, the transmit power constraint in the downlink, and the minimum ergodic secret rate constraint. The objective function in the proposed scenario is a nonconvex problem, which first becomes a convex problem and then, by using the Lagrange dual function method, the problem becomes an unconstraint problem. We also divide the cell into two areas using the cell division method. A practical energy‐efficient PA algorithm is presented which deals with the adverse effect of the eavesdropper. Simulations are carried out to demonstrate the effectiveness of the proposed methods.

Section: Simulation Resultsmentioning

confidence: 99%

“…The objective function and optimization problem are formulated as follows:

\max_{\{z, p\}} η_{EE} goodbreak= \frac{\sum_{k = 1}^{K} R_{k}^{\sec}}{\sum_{k = 1}^{K} z_{k} + \sum_{k = 1}^{K} p_{k} + \sum_{m = 1}^{M} P_{c, m}},

C 1 : \sum_{k : d_{k} < \frac{D}{2}} p_{k} goodbreak= α P_{T},

C 2 : \sum_{k : d_{k} > \frac{D}{2}} p_{k} goodbreak= ((), 1 goodbreak- α) P_{T},

C 3 : \sum_{k : d_{k} < \frac{D}{2}} z_{k} goodbreak= α Z_{T},

C 4 : \sum_{k : d_{k} > \frac{D}{2}} z_{k} goodbreak= ((), 1 goodbreak- α) Z_{T},

C 5 : R_{k}^{\sec} \geq R_{T, k}; k goodbreak= 1, 2, \dots, K,

Section: The Proposed Pa Algorithmmentioning

confidence: 99%

Section: The Problem Formulationmentioning

confidence: 99%

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Secure energy efficient power allocation in massive multiple‐input multiple‐output systems with an eavesdropper using cell division technique

Pourrostam

2021

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“…Power usage takes in two main parts, including power utilized by users and fixed circuit power usage. The EE is formulated as follows 19,20 :

η_{E E} = \frac{{falsefalse}_{false\sum}^{i = 1} r_{i}}{\sum_{i = 1}^{K} p_{i} + \sum_{m = 1}^{M} P_{c, m}},

where

P_{c, m}

is the fixed circuit power of m th antenna from the BS, which is wasted.…”

Section: System Modelmentioning

confidence: 99%

“…Furthermore,

P_{T}

and

R_{T, i}

are the total transmission power and the minimum rate required for the i th user, respectively. It should be noted that Problem () is a constrained non‐convex optimization problem 19 …”

Section: The Proposed Scheme and Optimization Problemmentioning

confidence: 99%

An energy‐efficient power allocation scheme for millimeter‐wave MIMO‐NOMA systems

Liqvan

et al. 2022

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Summary There are many challenges in fifth generation (5G) telecommunication systems, due to the increasing demands and applications. The most important of which are need to have higher energy efficiency (EE) and spectral efficiency (SE). They are critical in the practical multiple‐input multiple‐output (MIMO) telecommunication systems. Non‐orthogonal multiple access (NOMA) methods and millimeter‐waves can be used in conjunction with MIMO systems to improve their EE and SE performance. In this paper, we investigate the application of NOMA and mm‐Wave transmission in the downlink of MIMO systems. Then, we formulate the optimization problem for users in MIMO‐NOMA systems to maximize the EE that is subject to minimum data rate to satisfy required quality of service (QoS) and maximum transmission power. To achieve the optimal power allocation for users, we reach a problem for the EE maximization that is non‐convex and solution of the optimization problem is not trivial. We exploit a lower bound of the data rate and the Lagrange dual function to convert it to a convex and unconstrained problem, which is easy to solve. In the next step, we derive a relation for determining the optimal power allocation of users. In addition, a numerical algorithm is presented that can be used to solve the problem. According to the simulation results of the proposed algorithm, our method performs better and provides higher EE than both orthogonal multiple access and equal power allocation schemes.

Energy efficient power allocation in MIMO relaying systems with the MRT precoding

Imani

2023

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Multiple-input multiple-output (MIMO) systems perform well from the energy efficiency (EE) and the spectral efficiency (SE) points of view in fifth generation (5G) communication systems. This paper considers the operation of a MIMO system with a relay. The optimization problem relates to the EE maximization. This problem has two types of limitations, which provide a maximum transmission power and a minimum data rate for users. The encountered objective function is in a fractional form and thus it is a nonconvex function. Besides, the problem is constrained. We utilize a lower bound analysis for the data rates, some properties of the linear programming, and the maximum ratio transmission (MRT) precoding scheme to obtain a convex objective function. Using the Lagrange dual function, we obliterate the constraints of the problem and then it is easy to solve. To improve system performance, users are divided into two groups based on their channel gains, and the maximum transmission power is reasonably divided between them. Two iterative algorithms are proposed to solve the optimization problem numerically, and finally we investigate performance of the proposed method.