Energy efficient power allocation strategy for 5G carrier aggregation scenario

Gao, Weidong; Ma, Lin; Chuai, Gang

doi:10.1186/s13638-017-0924-1

Cited by 7 publications

(7 citation statements)

References 11 publications

(12 reference statements)

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“…The proposed algorithms are evaluated based on different benchmark metrics. These metrics are commonly used to evaluate the performance of the conventional approaches in massive MIMO systems [4], [9], [11], [13], [14], [21], [22], [24], [38]. Figure 1 shows the EE versus the maximum transmitted power for different values of p c .…”

Section: Resultsmentioning

confidence: 99%

“…Meanwhile, the minimal data rate constraints are maximized by optimizing power allocation among UEs, the min and EE are impacted with greater number of UEs. When k ≤ p t , more transmit data rates are provided to users with high channel gain, which improves the EE [4], [21], [38]. It is difficult to reduce the cost of hardware and maximize the EE.…”

Section: Constrained Ee For Power Allocation and Joint User Associmentioning

confidence: 99%

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Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

et al. 2020

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Singular value decomposition is highly essential to achieve a higher performance in signal processing using massive multiple-input multiple-output (MIMO) systems. This paper aims to provide a solution to control power allocation problem identified as an essential metric in a massive MIMO system that maximizes energy efficiency (EE). The network performance was evaluated by measuring circuit power consumption to maximize EE. The computational efficiency to maximize EE power allocation is very important to fifth generation networks (5G). The study aims to maximize the non-convex EE in a downlink (DL) massive MIMO system using a proposed energy-efficient low-complexity algorithm (EELCA) that guarantees optimal power allocation solution based on Newton's methods and joint user's association based on the Lagrange's decomposition method. An optimal power allocation solution in closed form to decrease the complexity of the power subject to both the maximum power and minimum data rate constrained systems was derived. Then, the unconstrained EE power allocation to solve the unconstrained optimal power was used to select the optimal power allocation by computing a root of the first derivative of the EE based on differentiating the instantaneous power allocation to maximize EE was formulated. Simulation results showed that the proposed EELCA with a total transmitted power allocation provided maximum EE for a large number of antennas at the base station (BS), Generally, non-linear schemes outperformed linear schemes. Finally, the large cost of circuit power consumption increased at the BS due to the large loss of radio frequency (RF) chains at every antenna when the signals were transmitted to all users. The maximum EE = 5.9 Mbits/joule when the number of distributed users, K = 33, with (p c , M) = (1000mW, 200). The proposed low complexity algorithm provides the better result EE based on a training channel for a number of distributed users.

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Section: Resultsmentioning

confidence: 99%

Section: Constrained Ee For Power Allocation and Joint User Associmentioning

confidence: 99%

Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

et al. 2020

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“…Step 5: calculate the probability of the AP transmitted to node j(i � AP, j ≠ i): compute the transmit distance threshold from AP to node i using equation (3), and compute the connectivity probability of this link by using equation (21).…”

Section: Optimal Transmit Powermentioning

confidence: 99%

“…For example, the issue of the minimum total transmit power of the system from the perspective of satisfying QoS constraints was studied in [20]. An efficient energy allocation algorithm was studied based on binary search for 5G carrier aggregation scenario in [21]. Under the conditions of given QoS requirements, the optimization of transmit power allocation of full-duplex access core network was studied in [22].…”

Section: Introductionmentioning

confidence: 99%

Transmit Power Allocation with Connectivity Probability for Multi-QoS in Cluster Flight Spacecraft Network

Yan

et al. 2020

Wireless Communications and Mobile Computing

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In this paper, we investigate the transmit power allocation problem to minimize the average packet error rate at the access point in the cluster flight spacecraft network, which adopts the CSMA/CA channel access mechanism. First, the node mobility, nodal distance distribution, and probabilistic adjacency matrix were formulated for cluster flight spacecraft network based on twinsatellite mode. en, the optimization-theoretic model described the optimized transmit power allocation strategy and its implementation algorithm was proposed. And the problem of minimizing the packet error rate of the cluster flight spacecraft network system can be converted into maximizing the expectation of the binary probabilistic adjacency matrix, i.e., maximizing the sum of the nondiagonal elements in the probabilistic adjacency matrix. Due to discreteness of nodal distance distribution, Monte Carlo method was applied to solve the transmit power allocation problem. Yet importantly, the influence of node transmit power on the QoS performance of cluster flight spacecraft network was simulated and analyzed under the assumption of finite overall network transmit power and low traffic load. Finally, the results show that the pocket error rate increases with the provided traffic load, but the pocket error rate hardly changes with the same traffic load in different sequential time slots of any orbital hyperperiod or in the same time slot of different orbital hyperperiods, and by maximizing the sum of the nondiagonal elements in the probabilistic adjacency matrix, the pocket error rate minimum is achieved for a given total network transmit power at any time slot for cluster flight spacecraft network.

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“…The UEs at the cell-edge experience reduced transmission rates and QoS provisioning from the wireless communication network, due to the relative poor channel qualities created by the increased path loss (path attenuation), noise and interference from the neighbouring cells. Several innovative techniques, such as multiple-input multiple-output (MIMO) antenna carrier aggregation (CA) and cooperative communications (CCs) have been proposed to address this major challenge [3][4][5][6][7][8][9][10][11][12]. Efforts are currently being made to include these innovations as standard features in the next-generation wireless communication networks.…”

Section: Introductionmentioning

confidence: 99%

Wireless networks performance enhancement via buffered cooperative communications

2019

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Energy efficient power allocation strategy for 5G carrier aggregation scenario

Cited by 7 publications

References 11 publications

Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

Energy-Efficient Power Allocation and Joint User Association in Multiuser-Downlink Massive MIMO System

Transmit Power Allocation with Connectivity Probability for Multi-QoS in Cluster Flight Spacecraft Network

Wireless networks performance enhancement via buffered cooperative communications

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