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Trade-Off Energy and Spectral Efficiency in 5G Massive MIMO System

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Trade-Off Energy and Spectral Efficiency in 5G Massive MIMO System Trade-off Energy and Spectral Efficiency in 5G Massive MIMO System Adeb Salh 1*, Nor Shahida Mohd Shah2*, Lukman Audah1, Qazwan Abdullah1, Norsaliza Abdullah3, Shipun A. Hamzah1, Abdu Saif1 1Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia. 2Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh, Muar, Johor, Malaysia. 3Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh, Muar, Johor, Malaysia. *Corresponding authors’ e-mail: [email protected], [email protected] to multiple single active users (UEs) in same time-frequency resource. The EE and SE cannot be simultaneously optimized. ABSTRACT It is still quasi-concave due to noise amplifier, a large number of antennas, and cost of hardware, which decreases the radio A massive multiple-input multiple-output (MIMO) system is frequency chains (RF) [7]-[15]. A large number of antennas in very important to optimize the trade-off energy-efficiency both linear precoder and decoder are needed to select the (EE) and spectral-efficiency (SE) in fifth-generation cellular optimal antenna to limit the noise with uncorrelated networks (5G). The challenges for the next generation depend interference. In addition, deploying more antennas causes on increasing the high data traffic in the wireless higher circuit power consumption. From the perspective of communication system for both EE and SE. In this paper, the energy-efficiency, the increasing base stations in the cellular trade-off EE and SE based on the first derivative of transmit networks reported that the total consumed energy in the whole antennas and transmit power in downlink massive MIMO network approximately 60-80% [16]-[20]. Adopted power system has been investigated. The trade-off EE-SE by using scaling at transmitting signal from BS to users depends on multi-objective optimization problem to decrease transmit reducing the interference. It takes into account the constraints power has been analyzed. The EE and SE based on constraint of the transmit power allocation. Massive MIMO system helps maximum transmits power allocation and a number of to reduce transmit power created from high power gain and antennas by computing the first derivative of transmit power provides the higher EE. Moreover, distributed users in every to maximize the trade-off EE – SE has been improved. From cell and spectrum allocation are needed to improve spectral the simulation results, the optimum trade-off between EE and efficiency. From [21]-[26], the user association is optimized SE can be obtained based on the first derivative by selecting to enhance the EE by decreasing the transmit power the optimal antennas with low cost of transmit power. consumption, while the area of spectral efficiency is linear Therefore, based on an optimal optimization problem is based on a fixed number of users and an optimal number of flexible to make trade-offs between EE-SE for distinct antennas according to [27]. The degree of freedom in a preferences massive MIMO system maximizes the SE by utilizing all available resources such as an available antenna that Key words : Massive MIMO, energy efficiency, spectral maximizes high data rate and transmit power. The area of SE efficiency, 5G. growths linearly with a large number of transmit antennas, while with respect to user’s association, the SE becomes a 1. INTRODUCTION concave function. The future cellular networks must collect the explosive demand to increase the high data rate with low Growing demand to achieve high data traffic, high-resolution complexity of energy consumption. video streams and smart communication in cellular networks From the existing works, the author in [28] studied the depends on a promising candidate massive MIMO system. maximizing trade-off EE and SE based on user’s association, The massive MIMO technique is a key to improve the energy- number of antennas, power coordination and takes into efficiency (EE) and spectral-efficiency (SE) in the fifth- account backhaul capacity to improve the performance EE- generation (5G) wireless communication networks. The SE. Ensuring good rate fairness, lower-level power with user’s deployment of cellular networks has a great potential to association improves the performance of EE and SE in the improve the low power base station (BS) and enhance the EE [29]. Meanwhile, the author in [30] investigated the EE and in a cellular network [1]-[6]. Due to the increasing attention in SE based on K-tier heterogeneous networks by offloading data cellular networks, the performance of 5G depends on traffic to small cell massive MIMO system. The improving evaluating the EE in the massive MIMO system. A massive trade-off EE and SE with Rayleigh fading channel is based on antenna array at the BS is able to provide the high connection using the different types of theoretical power consumption model and realistic power consumption model accordingly The received signal 푦푘 of users 퐾푡ℎ inside 푗푡ℎ cell can be [31]. Another author investigated that the maximization of written as EE-SE in downlink massive MIMO system depends on 퐾 analyzing the signal to interference noise ratio and optimum 휌 휌 transmission power in each cell when the number of BSs for 푦 = √ 푑 횑 퓋 푥 + √ 푑 ∑ 횑 퓋 푥 + 퓃 푘 퐾₣ 푘 푘 푘 퐾₣ 푘 푗 푗 푘 SE is high [32]. Achieving high sum-rates by using many 푗=1,푗≠푘 transmit antennas, the balancing of EE-SE depends on admitting an optimal number of distributed users and active 휌 푦 = √ 푑 푥 + 퓃 (2) transmit antenna with low cost of power consumption [33]. 푘 퐾₣ 푘 푘 The author in [34] focused on reducing the high complexity hybrid beamforming and power consumption for radio where 횑푘 is the Hermitian transpose channel matrix, and 휌푑 frequency chains by utilizing Butler phase shifter to enhance is the transmit power in the downlink. The transmit signal energy efficiency. From [35], the optimal design of EE-SE vector 휙 ∈ ∁푀×1 from BS 푗 a 푀 × 1 preceded vector 휙 = improved by employing the uplink and downlink high data 푗 푘 rates under imperfect channel state information (CSI) 퓋푘푥푘, where each UE receives the signal vector from each 푀×퐾 combined with maximum ratio, matched filter and zero- BS, 퓋푘 ∈ ∁ is the linear precoding matrix, 푥푘 ∈ 퐾 forcing. Maximum EE-SE trade-off in massive MIMO system ∁ ~풞풩(0, 퐼퐾) is the data transmitted from BS to UEs. ₣ is is achieved by equipping a great number of active users with the normalization factor of 퐾푡ℎ UEs, which is expressed as 2 2 less power and pilot training signal. ₣ = ‖혝‖푓⁄퐾, ‖. ‖푓 represents the Frobenius norm and 퓃푘 is In this paper, accurate CSI depend on used time-division the received noise with zero mean and variance. duplexing (TDD). Also, the goal of solving multi-objectivity problems is to find a set of trade-off solutions, by derivation 2.1 Power Consumption of the EE-SE trade-off in closed form. To decrease the complexity based on computing, the essential derivative of EE Reducing the transmit power depends on improving transmit and SE are in terms of power. antenna in a massive MIMO system. The circuit power consumption is established depend on selecting the optimal 2. SYSTEM MODEL antennas at the BSs created due to antenna architectures considered not only from a power amplifier, but also from Considering a downlink multiuser massive MIMO system, it power consumption such as baseband processing, DAC, and is assumed that every cell contains one base station, every BS filter. The total power consumption can be written as contains many transmit antenna 푀and 퐾 single users. It is assumed that 푀 ≫ 퐾. The channel matrix between BS and 푄푚푎푥 = 휌푑 + 푄퐶 (3) 푇 푇 푇 푇 1×푀 users ℋ = [횑1 , 횑2 , . 횑퐾] ∈ ∁ , as shown in Fig.1, with the assumption that the BS has perfect channel state The circuit power consumption in cellular networks is information. continuously increasing based on a large distributed UEs and . higher traffic demands. The circuit power consumption in 횑 11 [23]-[27],[36] is used and modeled as 푥 횑12 푦 1 1 푄퐶 ≈ 푁(푄퐷퐴퐶 + 푄푚푖푥 + 푄푓푖푙푡푒푟) + 2푄푠푦푛 + 푄퐿푁퐴 + 푄퐼퐹퐴 + 푥 푦 K- 푄푓푖푙푡푒푟 + 푄푚푖푥 + 푄퐴퐷퐶 (4) 2 2 BS with . UEs 푀 . data where 푄퐷퐴퐶 , 푄푚푖푥, 푄푓푖푙푡푒푟, 푄푠푦푛, 푄퐿푁퐴 + 푄퐴퐷퐶 , 푄푓푖푙푡푒푟 and antennas 푥푀 Channel 푦푀 Matrix 푄퐼퐹퐴 are the power consumption value for the DAC, the ℋ mixer, the filter in the transmitter, frequency synthesizer, the power consumption for soft noise amplifier, the power for the transitional frequency amplifier, and power consumed due to analog to digital converter, respectively. To simplify the Transmitted Received power consumption, 푄퐶 = 푄1 + 푀푡푄2, where 푄1 = 2푄푠푦푛 + vector 푦 vector 푥 푄퐿푁퐴 + 푄퐼퐹퐴 + 푄푓푖푙푡푒푟 + 푄푚푖푥 + 푄퐴퐷퐶 and 푄2 = 푄퐷퐴퐶 + 푄푚푖푥 + 푄푓푖푙푡푒푟 . Then, the total power consumption can be Figure 1: System model for multi-user massive MIMO system. simplified as According to the increase in Shannon capacity, high data rate 푄 = 휌 + 푄 = 휌 + 푄 + 푁푄 (5) can be achieved by increasing the transmit power of channel 푚푎푥 푑 퐶 푑 1 2 to users. The zero-forcing beamforming precoding is used to 2.2 Trade-off Energy Efficiency and Spectral Efficiency mitigate the inter-user interference. The zero-forcing beamforming혝 = [퓋1, 퓋2, … … . 퓋퐾] can be written as In this section, the objectives are to optimize EE-SE of multi- 혝 = ℋ퐻(ℋℋ퐻)−1 (1) user in terms of transmitting a huge number of antennas and transmit power. From (2), it is assumed that the capacity of transmitting antennas 푁 is fixed [26]-[29],[38]. In addition, 퐾푡ℎ UEs is multiplied by a bandwidth written as from (11) the performance of SE is obtained with a small and constant number of selected antennas 푁.
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