Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
ssymmetry SS Article
Energy and Spectral Efficiency Balancing Algorithm for EnergySaving in LTE Downlinks
Mamman Maharazu * , Zurina Mohd Hanapi * and Mohamed A. Alrashah * (cid:1)(cid:2)(cid:3)(cid:1)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8) (cid:1) (cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7) Citation:
Maharazu, M.; Hanapi, Z.M.;Alrashah, M.A. Energy and Spectral Ef-ficiency Balancing Algorithm for EnergySaving in LTE Downlinks.
Symmetry , , 0. https://dx.doi.org/Received:Accepted:Published: Publisher’s Note:
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Copyright: © 2020 by the authors. Li-censee MDPI, Basel, Switzerland. Thisarticle is an open access article distributedunder the terms and conditions of theCreative Commons Attribution (CC BY)license (https://creativecommons.org/licenses/by/4.0/). Department of Computer Science, Federal College of Education, College of Education, Katsina P.M.B. 2041Katsina State, Nigeria Department of Communication Technology and Network, Universiti Putra Malaysia,Serdang 43400 UPM, Selangor D.E., Malaysia * Correspondence: [email protected] (M.M.); [email protected] (Z.M.H.);[email protected] (M.A.A.)
Abstract:
In wireless network communication environments, Spectral Efficiency (SE) and Energy Effi-ciency (EE) are among the major indicators used for evaluating network performance. However, giventhe high demand for data rate services and the exponential growth of energy consumption, SE andEE continue to elicit increasing attention in academia and industries. Consequently, a study of thetrade-off between these metrics is imperative. In contrast with existing works, this study proposesan efficient SE and EE trade-off algorithm for saving energy in downlink Long Term Evolution (LTE)networks to concurrently optimize SE and EE while considering battery life at the Base Station (BS).The scheme is formulated as a Multi-objective Optimization Problem (MOP) and its Pareto optimalsolution is examined. In contrast with other algorithms that prolong battery life by considering theidle state of a BS, thereby increasing average delay and energy consumption, the proposed algorithmprolongs battery life by adjusting the initial and final states of a BS to minimize the average delay andthe energy consumption. Similarly, the use of an omni-directional antenna to spread radio signals tothe user equipment in all directions causes high interference and low spatial reuse. We propose usinga directional antenna instead of an omni-directional antenna by transmitting signals in one directionwhich results in no or low interference and high spatial reuse. The proposed scheme has been extensivelyevaluated through simulation, where simulation results prove that the proposed scheme is efficientlyable to decrease the average response delay, improve SE, and minimize energy consumption.
Keywords: battery life; delay; energy-efficient; power saving; downlink; LTE
1. Introduction
Long Term Evolution (LTE) is an emergent wireless network technology that aims toprovide low latency, peak data rate, wide coverage, and seamless mobility for different traffictypes ranging from residential users to small businesses [1]. The mobility features that enableUser’s Equipment (UE) to move at vehicular speeds are included in the first LTE (Release 8),followed by several incremental improvements in Releases 9 and 10 [2]. The mobility of UEsposes numerous issues because the UE is not only powered by a rechargeable battery, but alsohas a narrow capacity for providing the required consumption. Consequently, an efficientpower-saving scheme is necessary to prolong the battery lifetime of the UE at a radio BaseStation (BS), denoted as eNodeB, before recharging.The discontinuous reception (DRX) power-saving algorithm [3] adjusts the idle thresholdto improve the Energy Efficiency (EE) and Spectral Efficiency (SE) of eNodeB. Such adjustmentis overwhelmed by the remaining energy at eNodeB. Consequently, eNodeB quickly transitsinto sleep-mode to save energy, which increases average response delay because eNodeB has
Symmetry , a r X i v : . [ c s . N I] F e b ymmetry , , 0 2 of 19 to wait for the next listening window to receive a message. The idle threshold is adjustedusing exponential and general distribution methods, which yields frequent transitions intothe listening mode when the packet arrival rate at eNodeB is small, where frequent transitionsmay lead to high energy consumption. Moreover, dynamically adjusting the idle thresholdis difficult because of time-varying traffic. Therefore, an efficient SE and EE Trade-off (SET)algorithm is proposed in this study; this algorithm mitigates the ineffectiveness of the DRXalgorithm. The major difference between the proposed SET algorithm and the DRX algorithmis the manner in which the sleep-mode is adjusted: initial and final state. The packet arrivalinterval and transmission time of the DRX algorithm are adjusted using exponential andgeneral distribution methods, respectively, whereas that of the proposed SET algorithm areadjusted in accordance with the packet arrival pattern.In this study, an enhancement to the DRX power-saving algorithm, namely SET, isproposed for saving energy in downlink LTE networks. The Radio Resource Control (RRC)idle state in the DRX algorithm is analytically enhanced. In contrast with the DRX algorithm,the proposed SET algorithm dynamically adjusts the RRC at initial and final states on thebasis of downlink stochastic arrival pattern. In addition, an improved sleep-mode scheme isproposed to minimize numerous transitions to listening mode, which consequently reducesthe high energy consumption when the traffic arrival is less. The simulation results show thatthe SET algorithm considerably outperforms the DRX algorithm in terms of SE, SET, averageresponse delay, and energy consumption.The major contributions of this study are fourfold. Firstly, SE and EE are concurrentlyoptimized as a Multi-objective Optimization Problem (MOP). Secondly, the MOP is trans-formed to a Single-objective Optimization Problem (SOP) by using a weighted sum technique,thereby proving that the scheme is quasi-concave while its Pareto optimal solution is alsoexamined. Thirdly, an efficient algorithm namely SET is proposed to prolong the battery life,which reduces energy consumption at a BS. Lastly, the performance of the proposed SETalgorithm is evaluated via simulation, where the results clearly demonstrate the considerablegain of the SET algorithm compared to other methods.The rest of this paper is organized as follows: Section 2 provides an overview of relatedworks, Section 3 presents the proposed SET algorithm, Section 4 illustrates the performanceevaluation and the benchmark of the proposed SET algorithm, and finally, Section 5 concludesthe entire work.
2. Related Work
Several algorithms proposed for LTE downlinks are reviewed in this section. The reviewfocuses on how these algorithms use SE, EE, SET and sleep-mode operations to save energy,where the advantages and limitations of each algorithm are highlighted. Recently, severalstudies have been conducted to investigate the relationship between SE and EE. In [4], anew paradigm was presented for SET by considering different bandwidth requirements. Thescheme analyzed resource efficiency for Orthogonal Frequency-Division Multiple Access(OFDMA) and proved that it utilizes the trade-off between EE and SE by balancing thepower consumption and the occupied bandwidth. However, this scheme did not provide anappropriate weight factor to utilize the available bandwidth and power. Later, the trade-offbetween EE and SE in a delay-constrained wireless system was proposed in [5]. The approachused a generic closed-form approximation to investigate the impact of Quality of Service(QoS) and circuit power consumption on EE and SE, respectively, by applying a curve-fittingmechanism. This approach proved that EE function is a quasi-convex problem that can besolved using a two-step binary search algorithm. However, the scheme lacked a closed-formpower allocation approach and a mathematical formulation for the trade-off between EE and ymmetry , , 0 3 of 19 Effective Capacity (EC). SET trade-off behavior has been widely considered using variousinterference-level scenarios.SET with filter optimization in multiple access was analyzed in [6] using two conflictingmetrics: throughput maximization and power consumption minimization. An energy-efficientdesign for an OFDMA network was proposed in [7] based on Wu’s effective capacity approachto maximize system throughput, which is subject to delay QoS requirements. Furthermore,effective EE and EC trade-off were utilized through fractional programming, which convertedquasi-concave optimization to a subtractive optimization problem by adopting Dinkelbach’stechnique. However, this algorithm was not optimal for maximizing EC-based statistical delayprovisioning.In [8], a general problem was formulated to minimize total consumption cost whilesatisfying area SE requirements. The problem was further decomposed into a deploymentproblem at peak time and an operational problem at off-peak time. However, networkperformance was jeopardized due to the high energy consumption of the scheme. In [9], aMOP was adopted to examine SET in downlink OFDMA systems with fairness constraints.The scheme uses a weighted sum method to obtain Pareto sets that provides a quantitativeinsight into SET with various fairness levels. However, the scheme did not achieve trade-off between EE and fairness because the backhaul energy consumption was completelydisregarded.Reference [10] proposed a scheme that jointly maximizes overall system EE and SEtowards green heterogeneous networks under QoS constraints. The MOP was modeled todynamically adjust the trade-off parameters of network providers. To obtain a Pareto optimalsolution, the problem was transformed into an SOP using a weighted sum method. However,this scheme disregarded the total signaling power of the transmitters, transmission time andpower constraints of the antennas. In [11], SET was investigated for a downlink OFDMAsingle-cell network. The scheme simultaneously optimized EE and SE, which were thenformulated as a MOP. It used a weighted linear sum technique to convert the MOP to an SOPwhere Pareto optimal sets were analyzed. The converted SOP illustrated that the scheme isneither quasi-concave nor quasi-convex. Thereafter, the scheme was solved using particleswarm optimization, which decreased the total transmission power and improved the EE.However, this scheme is neither convex nor concave, thus, finding its optimal solution isdifficult and also requires extra processing that consumes more power.In [12], the authors proposed an energy-aware power management scheme that dynami-cally updates sleep parameters in accordance with the remaining energy of Inter-Arrival Time(IAT) to prolong battery life. The scheme achieved minimum response delay under sufficientenergy but resulted in high energy consumption under insufficient energy. Reference [13]investigated the EE aspects of the BS deployment mechanism for cellular networks. Themechanism explored the impact of power consumption on the basis of deployment strategies.In addition, the concept of area power consumption was introduced. The scheme achievedan average throughput improvement but resulted in high energy consumption. After that, ascheme for power allocation and antenna port selection in Orthogonal Frequency-DivisionMultiplexing (OFDM) distributed antenna systems was proposed in [14]. Two schemes wereinvestigated to maximize the downlink received signal. Firstly, the scheme selected the dis-tributed antenna on the basis only of large-scale fading and power allocation. Secondly, thescheme selected the distributed antenna using large- and small-scale fading coupled withoptimal power. The scheme achieved low complexity but disregarded delay performance,which resulted in a waste of energy.Energy-efficient link adaptation with transmitter Channel State Information (CSI), whichwas proposed in [15], aimed to minimize the total energy consumption of a mobile terminal.Flat and frequency-selective fading channels were used to determine the consumed energy. ymmetry , , 0 4 of 19 The algorithm achieved a decrease in energy consumption with an increase in bandwidth,however, it could not guarantee the user’s QoS. In [16], an energy-efficient subcarrier and bitallocation for multi-user OFDMA systems were proposed. SET was analyzed by consideringfairness constraints among users. The algorithm was formulated as an optimization problemfor integer fractional programming. Moreover, an iterative programming method was used tosolve the optimization problem by minimizing system complexity. However, the algorithmresulted in poor energy efficiency because it assumed that CSI is always perfect. In fact, CSIcan be corrupted when the scheduled data rate surpasses the maximum channel size.The authors of [17] investigated SET in a fading communication link. An optimizationproblem to maximize ergodic SE with a constraint of minimum ergodic EE was introduced.The scheme achieved an improvement in SE while minimizing channel circuit power, but it didnot consider the impact of the user’s data rate, which resulted in inefficient resource utilization.To alleviate this problem, Reference [18] proposed SET for a heterogeneous network with QoSconstraints. This scheme aimed to simultaneously maximize SE and EE while satisfying thedata rate requirement of each user. It was designed on the basis of three stages. Firstly, the cellcenter radius was selected using fractional frequency reuse. Secondly, frequency resourceswere allocated to satisfy user data requirements. Lastly, the Levenberg–Marquardt approachwas used to solve the power allocation sub-problem. This scheme achieved higher outageprobabilities because of increased inter-cell interference, however, it resulted in high energyconsumption.The authors of [19] proposed SET in cellular networks. The algorithm developed a theo-retical framework that is applicable to OFDMA networks based on transmission power andoptimal resource allocation. Initially, the algorithm focused on using a single-cell scenario butwas later extended to a multi-cell scenario using the stochastic geometry approach. It achievedtractable outcomes but using a large number of antennas resulted in high energy consumption.SET in an interference-limited algorithm was proposed in [20], which concurrently optimizesEE and SE. Firstly, the scheme was formulated as a MOP by applying the constraint of thetransmission power limit. A weighted linear sum technique was used to convert the MOPto an SOP. The scheme achieved a balance between EE and SE but it led to inefficient use ofnetwork resources.The joint evaluation of the EE downlink scheduling algorithm was recently proposed in[21]. The algorithm aimed to optimize energy and bandwidth resources while guaranteeingQoS at the downlink by considering partial feedback. At eNodeB, SE and EE were optimizedby amending the downlink scheduler in accordance with the packet prediction mechanism.The algorithm achieved an EE improvement of up to 79% but generated considerable overhead,which resulted in high energy consumption.In this paper, we propose a new energy-efficient algorithm, namely SET, to mitigatethe aforementioned problems. The algorithm analytically adjusts the initial and final sleepwindows by considering the downlink stochastic packet arrival pattern and introducing animproved sleep-mode.
3. The Proposed SET Algorithm
This section introduces a new energy-efficient algorithm that prolongs battery lifetime ateNodeB, which is able to adaptively adjust the network parameters.
Reference [3] analytically investigated the efficiency of the DRX mechanism and the effectof RRC state transmission on saving battery life. The performance of this scheme has beenevaluated by considering only idle-state RRC. However, this scenario causes an increase inpacket delay that might result in high energy consumption. ymmetry , , 0 5 of 19 The major difference between the proposed scheme and the DRX power-saving algorithmis the manner in which the sleep-mode of RRC is adjusted: the initial state ( initial state ) andfinal state ( f inal state ) . Furthermore, the performance of the proposed algorithm is evaluatedanalytically and through experimental simulation. Moreover, packet arrival interval andtransmission time in DRX are adjusted on the basis of exponential and general distributions,respectively. In contrast with DRX, the proposed SET algorithm dynamically adjusts threeparameters on the basis of the packet arrival pattern. The basic concept behind the sleepoperation is to improve power consumption by minimizing energy. In general, eNodeB goesinto sleep-mode when UE has no request for packet processing.The communication between UE and eNodeB with their corresponding sleep operationhas been illustrated in Figure 1, where the eNodeB remains in the idle state unless it receives amessage from UE that a packet has to be transmitted. Consequently, the eNodeB transits froma listening state to an aware mode by sensing and then responding to the UE that is ready toreceive an incoming packet for transmission. Figure 1.
SET algorithm operation with awake and sleep-modes.
The time that the incoming packet arrived at the eNodeB scheduler and the time spentbefore it is being transmitted are recorded. When the current time (spent-time–arrival-time) islonger than 1 ms, the packet is in the transmission state. During the transmission state, theInitial Energy Consumption (IEC) and the Final Energy Consumption (FEC) are computedusing Equations (1) and (2), respectively. After all the packets are transmitted, eNodeB returnsto sleep-mode while waiting for a request from the UE to transmit another incoming packet.This process is repeated continuously until an optimal value is obtained for IEC and FEC.Given that the remaining energy at eNodeB is consumed, the act of eventually savingenergy is crucial. IEC has an impact on energy consumption and packet response delay, whichshould be adjusted by considering the remaining energy in eNodeB as indicated in Equation(1):
IEC = max ( E total − E remained E total ) ∗ E max (1)where E total denotes the total energy of eNodeB, E remained represents the remaining energy,and E max is the maximum energy. Similarly, FEC is calculated using Equation (2): FEC = a × E max + b × E min (2)where E min is the minimum energy, and a + b =
1, which represents the weight of each valueand bases the estimates of the current IAT on the past IAT.The primary modification and improvement of the proposed SET algorithm have beenillustrated in Figure 2, in which the packet processing time is set to ≥ ymmetry , , 0 6 of 19 Figure 2.
Control flow diagram of the SET algorithm.
The corresponding algorithm is presented in Algorithm 1, which illustrates how thetwo parameters are dynamically adjusted based on the downlink stochastic packet arrivalpattern. Firstly, the idle, initial, and final states initialized at the UE, which begins in normalsleep-mode operation and waits for a short period; if no packet is received, then it sends arequest message to the eNodeB to enter into sleep-mode.The eNodeB computes the initial and final sleep windows for the UE using Equations (1)and (2), and then sends the results back to the UE for it to proceed to sleep-mode. Thereafter,the request to terminate sleep-mode (i.e., request to wake up from sleep) is measured; if itis not satisfied, then the UE returns to normal operation mode; otherwise, the operation isterminated.
The primary objective of this section is to concurrently optimize EE and SE to obtain anoptimal solution. Assume that adaptive coding and modulation scheme is used to attain theShannon rate limit, the SE of the k t h UE can be illustrated as: SE ( p k ) = log ( + β k ) (3) ymmetry , , 0 7 of 19 where p k denotes the transmission power and β k is the instantaneous signal-to-noise ratio ofthe k th UE, and k ∈ { · · · , K } . While, the SE of all UEs can be represented as: SE ( P ) = K ∑ k = log ( + β k ) (4)where P = { p , p , · · · , p K } . Furthermore, EE is defined as the ratio of SE over the total powerconsumption, which can be described as: EE = SE ( P ) K ∑ k = ( p k + p (cid:48) k ) (5)where p (cid:48) k represents the remaining percentage of circuit power of the k th UE.Hence, the problem is formulated as a MOP, which can be described as in Equation (6):
MOP = max { EE , SE } (6)subject to 0 ≤ p k ≤ p max , ∀ k ∈ K , where p max denotes the maximum transmission powerallocated to each eNodeB and SE = SE ( P ) as in Equation (4).To solve the SET in the aforementioned MOP, the concept of Pareto optimality [23,24] isused, which results in the conversion of the MOP to an SOP using weighted linear summationtechnique [25], as shown in Equation (7): SOP = ( θ × SE ) + (( − θ ) × EE ) (7)subject to 0 ≤ p k ≤ p max , ∀ k ∈ K , where Equation (7) represents the SET optimization problem(objective function), and θ is the trade-off weighting parameter, such that 0 ≤ θ ≤
1, whichenables the flexibility to achieve the trade-off between EE and SE.
Recently, global warming and CO emission have been extensively studied from theperspectives of environmental and economic impacts [26]. In this regard, energy consumptionat eNodeB has become a key issue in cellular networks. Studies have determined that eNodeBoperations are accountable for a major part of cellular energy consumption [27] wherein upto 80% of energy is consumed. Therefore, minimizing the power consumption of eNodeBcan considerably reduce overall network energy consumption. The scheme proposed in thissection relies on the path loss and energy consumption of eNodeB. Accordingly, we adoptand modify the energy consumption model in references [28,29], as shown in Figure 3. Figure 3.
The adopted energy consumption model. ymmetry , , 0 8 of 19 Algorithm 1:
SE and EE Trade-off (SET) Algorithm for Saving Energy in DownlinkLTE Networks Input: PPT : Packet processing time State : System State Initialization: PPT = State = Sleep-mode; while State = Sleep-mode do if termination request is received then State = Terminate ; else if incoming packet is available then if PPT ≥ then SE ( p k ) = log ( + β k ) ; SE ( P ) = K ∑ k = log ( + β k ) ; EE = SE ( P ) K ∑ k = ( p k + p (cid:48) k ) ; MOP = max { EE , SE } ; SOP = ( θ × SE ) + (( − θ ) × EE ) ; while SOP is not within optimal range do Select
SOP ; Compute test values using
SOP method; end end PPT++; end end end From Figure 3, the total energy consumption can be formulated as: E Total = i ( E ( R TX ) + E ( S TX )) (8)where i denotes the number of users, E Total denotes the total energy consumption, R TX and S TX denote the transmission and receiving power levels measured in iJoules/bits, respectively.
4. Performance Evaluation of the Proposed SET algorithm
This section primarily aims to perform elaborate evaluations of the proposed SET algo-rithm to validate its performance compared to the DRX power-saving algorithm.
The simulation scenario in this section is based on two types of networks served by asingle eNodeB; (1) A non-congested network with a number of UE less than 100, where thenetwork density is under normal non-congested condition [22]. (2) A congested network witha number of UE greater than 100 [30]. The total energy consumed by the entire network isthe summation of energy dissipated during the transmission and receiving processes, whichcomprises energy spent in the control and data messages in both modes. ymmetry , , 0 9 of 19 A cellular network that comprises homogeneous macro-cells with a hexagonal tessellationis presented as in Figure 4. The eNodeB is placed at the center of each cell, and UE is assumedto be served by the nearby eNodeB. Spectrum resources and power are assumed to be allocatedequally to all cells in the network. Two distinct eNodeB antenna configurations are used inthis section: (1) omni-directional and (2) directional antennas. An omni-directional antennatransmits radio links in all directions but has limited network capacity because of the highinterference.
Figure 4.
Omni-antenna in cellular network with interference.
To alleviate interference caused by an omni-directional antenna, cell sectoring, whichdivides cells into sectors (as 120 ◦ or 60 ◦ ) using a directional antenna, is proposed. Thetransmission and receipt of radio signals in a certain direction result in low interference, highspatial reuse, long transmission range and improved network capacity. The cell sectoring byantenna patterns has been shown in Figure 5. Figure 5.
Antenna patterns.
To evaluate the performance of any cellular network, an important metric that should beconsidered is the signal-to-interference-plus-noise ratio (SINR). Thus, the SINR of user’s cellsin a region served by cell k is calculated using Equation (9) based on Reference [31]: SI NR = P k ∗ C k , nI ∑ i =
1, except i = k P i ∗ C i , n + P n (9)where P k denotes the transmission power of the serving cell k, C k , n represent the channel gainfrom the serving cell k to n number of UE, I is the set of all interfering cells in the region.Moreover, P i is the transmission power of each i th neighboring cell, C i , n represents the channelgain from the i th cell to n number of UE, P n is the white Gaussian noise power, where thechannel gain in this research considers both transmission and receiving antenna gains alongwith their path loss gain.The energy saving algorithm consists of one eNodeB and the UEs that are uniformlydistributed around the eNodeB as presented in Figure 6. All the simulation experiments setuphave been adopted from LTE Release 8 [32]. These experiments have been conducted using the ymmetry , , 0 10 of 19 Vienna system-level simulator [33], in which the Multi-Input Multi-Output (MIMO) antennasand a system bandwidth of 5 MHz have been used. The simulation time in all experiments is100 seconds, which is more than enough to show all stages of the system. As for updatingthe transmission power, it has been done at the beginning of each transmission time interval(TTI), where each second has 1000 TTIs, while the thermal noise density is set to -174 dBm/Hzduring all simulation experiments.The network topology used in this work has been adopted from [16], which is usedto evaluate the energy saving algorithm. As shown in Figure 7, the topology consists ofone eNodeB with several Mobile Users (MUs) that are uniformly distributed in a congestednetwork including MU , MU , ..., MU N , where N > Figure 6.
Network topology in non-congested scenario.
Figure 7.
Network topology in congested scenario. ymmetry , , 0 11 of 19 Table 1.
Simulation parameters setup.
Parameter Value
System bandwidth 5 MHzNumber of resource blocks 25Active sector of concern eNodeB2-sector1TTI 1 ms; 1000 TTI per secondUE distribution UniformMacroscopic path loss model TS25814Simulation period 100 secondsSpeed of the user 4.16 m/sTransmission scheme 2X2 MIMO, OLSMCyclic prefix NormalTransmitter antenna gain 18 dBiReceiver antenna gain 0 dBiMaximum transmission power 20 WMaximum delay 20 msInter-eNodeB distance 500 m
In this experiment, a low density network with 100 pieces of UE has been used, wherethe UEs were uniformly distributed with an inter-eNodeB distance of 500 m. The objectivehere is to determine the network performance under an increasingly stressful scenario. Theresults of the SE, average response delay, energy consumption, and the SE-EE trade-off areillustrated in Figures 8–11, respectively. In Figure 8, a comparison between the SET algorithmand the DRX power-saving algorithm has been presented in terms of the SE, by varying thenumber of users from 10 to 100. The proposed algorithm significantly outperforms the DRXalgorithm in all cases because the larger share of resource blocks (RBs) is not lost due to usersbeing under good channel condition before allocating resource blocks. As the number of usersincreases, the competition among users’ resource blocks also increases. S pe c t r a l E ff i c i en cy ( bp s / H z ) Number of usersSETDRX Power Saving
Figure 8.
Spectral efficiency as the number of users increases. ymmetry , , 0 12 of 19 In addition, the use of transmission power to allocate resource blocks provides theproposed SET algorithm with a higher SE than that of the DRX power-saving algorithm.Furthermore, the DRX attempts to satisfy users while neglecting their conditions, whichdecreases its eNodeB performance.The average response delay of the proposed SET algorithm compared to the DRX power-saving algorithm has been illustrated in Figure 9. The average response delay of the DRXincreases rapidly when the number of users increases because this algorithm does not considerthe packet delay in resource block allocation. Furthermore, the proposed SET algorithmhas lower average response delay than the DRX due to choosing the larger window beforeswitching to the sleep-mode to accommodate incoming packets, which shows a significantimprovement in the average response time in all cases. A v e r age R e s pon s e D e l a y ( m s ) Number of usersSETDRX Power Saving
Figure 9.
Average response delay as the number of users increases.
The effect on energy consumption by considering the packet arrival pattern in a non-congested network has been presented in Figure 10. Compared to the DRX power-savingalgorithm, the energy consumption of the proposed SET algorithm is lower due to the ap-propriate adjustment of eNodeB in the initial mode. Such an adjustment is performed bypredicting the downlink packet arrival that is used to update the initial state. Consequently,minimizing the number of window sleep intervals reduces energy consumption. The pro-posed SET algorithm can prolong battery life by 81.92% compared to only 18.08% by the DRXalgorithm. ymmetry , , 0 13 of 19
62 63 64 65 66 67 68 69 1000 2000 3000 4000 5000 6000 7000 E ne r g y po w e r c on s u m p t i on ( J ou l e ) Packet arrival rate (1/ms)SETDRX Power Saving
Figure 10.
Energy consumption vs. packet arrival rate.
Indeed, the proposed SET is utilized to obtain the Pareto optimal points between the SETalgorithm and the DRX power-saving algorithm. In this manner, the MOP in Equation (6) istransformed to the SOP in Equation (7). As shown in Figure 11, optimal EE increases until thesaturation point with an increase in the SE. The proposed algorithm achieves considerablegain compared to the DRX algorithm, with optimal points of 1.9 bit/Hz and 1.75 bit/Hz,respectively. EE ( b i t/ J ou l e / H z ) Spectral Efficiency (bit/s/Hz) SETDRX Power Saving
Figure 11.
Effect of SE-EE trade-off in non-congested network.
In order to carry out this experiment, we implemented two scenarios: (1) varying networkloads on a congested network using an omni-directional antenna. (2) varying network loadson a congested network using directional antennas. Thus, the impact of using different typesof antennas, such as omni and tilted directional antennas are presented in Sections 4.3.1 and4.3.2, respectively.4.3.1. Impact of Varying Network Loads on a Congested Network Using an Omni-DirectionalAntennaAn omni-directional antenna that spreads radio signals in all directions has been used ina congested network scenario that contains a large number of UE >
100 [30]. The pieces ofUE are uniformly distributed with an inter-eNodeB distance of 500 m. The objective of imple-menting this scenario is to determine the network performance under an increasingly stressful ymmetry , , 0 14 of 19 condition, where Figures 12–15 present the results of the SE, delay, energy consumption, andSET, respectively.A comparison of the SE between the proposed SET algorithm and the DRX power-savingalgorithm has been shown in Figure 12. As explained in Section 3, the proposed SET algorithmallocates the RBs to users with good channel states, which exhibits better performance thanthe DRX algorithm. It worth noting that disregarding the selection of an appropriate channelstate by the DRX algorithm results in poor SE, which affects the performance of the eNodeB. S pe c t r a l E ff i c i en cy ( b i t/ s / H z ) Number of UEsSETDRX Power Saving
Figure 12.
SE as the number of UEs are increased in congested network for with omni-directionalantenna.
A comparison between the delay of the proposed SET algorithm to that of the DRXpower-saving algorithm has been presented in Figure 13. The DRX algorithm exhibits anincreased delay because the RBs allocation is established while disregarding the packet delayrequirements. Moreover, the proposed algorithm is able to minimize the delay because it usesan appropriate selection of window size. Given the frequent arrival of users and the spread ofradio signals in different directions by the omni-directional antenna, the competition amongusers to access the radio signals increases the delay in the case of the DRX algorithm that,consequently, increases the interference level. D e l a y ( m s ) Number of UEsSETDRX Power Saving
Figure 13.
Delay as the number of UEs are increased in congested network for with omni-directionalantenna.
Subsequently, the energy consumption of the eNodeB is investigated, where Figure 14shows that increasing the number of UE in the network environment will cause a rise in the ymmetry , , 0 15 of 19 interference, thereby increasing the energy consumption, in which the increase of interferencemakes it difficult to find an optimal power transmission. For this reason, the proposedalgorithm outperforms the DRX power-saving algorithm with the least energy consumptiondue to its ability to adjust eNodeB by predicting the arrival pattern of user requests. E ne r g y c on s u m p t i on ( J ou l e ) Number of UEsSETDRX Power Saving
Figure 14.
Energy consumption by increasing number of UEs in congested network for with omni-directional antenna.
The impact of increasing the number of UE pieces in a congested network using omni-directional antenna by utilizing SET has been presented in Figure 15, in which the increase inthe SE will always increase the EE. EE ( b i t/ J ou l e / H z ) Spectral Efficiency (bit/s/Hz) SETDRX Power Saving
Figure 15.
SE-EE trade-off of congested network with omni-directional antenna.
The proposed SET algorithm achieves the optimal point at 2.2 bit/s/Hz, which is higherthan that of the DRX power-saving algorithm whose optimal point was at 1.78 bit/s/Hz. Thisresult indicates that the proposed SET algorithm outperforms the DRX algorithm because itcan achieve optimal value with reduced energy consumption. ymmetry , , 0 16 of 19 ◦ or 120 ◦ has been used in this scenario. In fact, transmitting or receiving radio signalsin one direction or specific angle leads to a low level of interference, which consequently leadsto improving the network capacity and coverage that in turn increase its successful deliveryrate between UE and eNodeB/antenna.Moreover, Figure 16 shows how manipulating the number of UEs per eNodeB couldimprove the SE, where increasing the number of UEs increases the performance due to thecompetition. Since cell sectoring is performed by the proposed algorithm to enable radiosignals to converge at a certain degree, thus, cell edge users are served with RBs because theirchannel state is in good condition. For this reason, the proposed algorithm exhibits a highergain than the DRX power-saving algorithm due to its ability to rotate a directional antenna,which enables it to cope with the demands of cell edge users. S pe c t r a l E ff i c i en cy ( b i t/ s / H z ) Number of UEsSETDRX Power Saving
Figure 16.
SE as the number of UEs are increased in congested network for with directional antenna.
In Figure 17, a comparison between the delay of the proposed SET algorithm and that ofthe DRX power-saving algorithm has been presented, where the latter has increased delay dueto disregarding the packet delay requirements during RB allocation. The proposed algorithmwas able to reduce the delay due to selecting the appropriate window size. Moreover, thesectoring approach of a directional antenna considerably affects the delay metric due to theuse of a reduced coverage area, however, the proposed algorithm was able to minimize thedelay, which minimizes the interference. Meanwhile, the DRX algorithm increases the delaythat significantly led to high energy consumption. ymmetry , , 0 17 of 19 D e l a y ( m s ) Number of UEsSETDRX Power Saving
Figure 17.
Delay as the number of UEs are increased in congested network for with directional antenna.
The increase in the number of UE increases the amount of consumed energy due to thehigh interference, as shown in Figure 18. Consequently, the total performance is degradedwhen the energy consumption at eNodeB increases, which leads to a significant delay increasewith both algorithms. However, the proposed algorithm still able to outperform the DRXpower-saving algorithm due to its appropriate method to adjust the eNodeB to a certaindegree. E ne r g y c on s u m p t i on ( J ou l e ) Number of UEsSETDRX Power Saving
Figure 18.
Energy consumption as the number of UEs are increased in congested network for withdirectional antenna.
The impact of increasing the number of UEs in a congested network using a directionalantenna by utilizing SET has been shown in Figure 19. This figure shows that the increasein SE always leads to EE increase. The proposed algorithm achieves its optimal point at1.9 bit/s/Hz, which is higher than that of the DRX power-saving algorithm at 1.75 bit/s/Hz.This finding indicates that the proposed algorithm is able to outperform the DRX algorithmdue to achieving its optimal value with lower energy consumption. ymmetry , , 0 18 of 19 EE ( b i t/ J ou l e / H z ) Spectral Efficiency (bit/s/Hz) SETDRX Power Saving
Figure 19.
SE-EE trade-off of congested network with directional antenna.
5. Conclusions
This research addressed the high energy consumption problem in modern mobile com-munication systems by controlling the transmission power of eNodeB. The two parametersthat play a vital role in improving energy efficiency are the adaptive initial and final thresholds,where these two parameters were adjusted by considering a stochastic traffic arrival patternof UEs. In addition, the proposed SET algorithm was formulated as a MOP to determinethe trade-off between the SE and the EE. Similarly, an interference approach was developedby applying antenna patterns to provide efficient energy management at eNodeB. Severalsimulation experiments were conducted, which demonstrated the ability of the proposedalgorithm to outperform the DRX power-saving algorithm in terms of SE, average responsedelay, and energy consumption.
Author Contributions:
All authors of this article have contributed to the work as follows: conceptual-ization, validation, formal analysis, methodology, software, visualization, and original draft preparationhave been done by M.M.; technical review, proofreading, editing, and writing the final draft have beendone by M.A.A.; project administration, supervision, and funding acquisition have been done by Z.M.H.All authors have read and agreed to the published version of the manuscript.
Funding:
This work is funded by Universiti Putra Malaysia under Putra Berimpak Grant number(9659400).
Institutional Review Board Statement:
Not applicable, because the study does not involving humansor animals.
Informed Consent Statement:
Not applicable, because this study not involving humans.
Data Availability Statement:
Not applicable
Acknowledgments:
This work is supported by Universiti Putra Malaysia.
Conflicts of Interest:
The authors declare no conflict of interest.
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