The energy consumption rate of information and communication technology (ICT) has increased rapidly over the last few decades owing to the excessive demand for multimedia services. Wireless networks are considered one of the main sources of energy consumption in the ICT arena [1]. Among the wireless network components, base stations (BSs) are considered a paramount source of energy consumption, accounting for 57% of the total energy used [2]. In the last decade, wireless network operators are focused on providing a high data transfer rate with large radio coverage to satisfy the demand of subscribers. Therefore, wireless network operators have largely increased the BS numbers to deliver a high data rate to enormous wireless devices and access point nodes. Consequently, both the energy consumption and operational expenditure (OPEX) have notably increased i.e., linearly scaled with the augmentation of intensive user requirements [3]. Recently, the massive connectivity of IoT devices incorporated with wireless access networks led to tremendous energy consumption; it is expected to increases enormously in the future due to the diverse type of IoT applications [4]. Thus, the primary focuses of wireless network operators are became provisioning a high data rate with attention to energy-efficient issue simultaneously. Moreover, the increasing concerns of the wireless network operators about energy consumption not only to increase savings in OPEX but also due to the growing awareness of global warming issues and ecological concerns [5]. Therefore, the researchers and experts are endeavoring to determine the optimum approaches to diminish the energy consumption rate notably for the wireless networks while persisting with desired data services.

Researchers are focused on numerous distinctive approaches to reduce the energy consumption into wireless networks such as energy-efficient hardware components, selective operation of components, efficient use of radio transmission process, deploying heterogeneous cells, and implementing renewable energy resources (RESs) [6]. RESs are considered the most attractive practices in designing energy-efficient wireless networks over the long term in a cost-efficient way in the existing infrastructure [7]. Nonetheless, the commissioning of RES-powered BSs is based on the availability of wind profile/solar irradiation that hinges on the latitude, seasonal disparities, and ecological circumstances. Besides, cannot ignore the amount of energy that is wasted due to the lack of efficient utilization of resources. Whereas, the infrastructures of the BSs have been designed to support daytime traffic i.e., (peak time). However, the traffic loads during the daytime differ from those during the night [8]. Therefore, the BSs must effectively deal with the resources to achieve efficient energy utilization and better saving.

Considering all these inferences, researchers have insufficient descriptions for the total OPEX savings including optimum extent efficient solution of RESs. Therefore, this study examines as a major contribution achieve a balance between network performance and energy efficiency via examines the feasibility of incorporating solar-powered BSs technique with a dynamic cell zooming strategy according to the traffic load conditions in Korea to determine the net OPEX savings. The contributions of this work are summarized as follows:

To propose and determine the technical feasibility of an adaptive standalone solar power solution incorporating a dynamic cell zooming strategy based on resource susceptibility to obtain a long-tenure energy balance.

The rest of this work is organized as follows; Section 2 presents the cell zooming concept and performance metrics. Section 3 block diagram of BS hardware elements and mathematical modeling. Section 4 shows the proposed solar power system and mathematical formulations. The cost optimization formula is given in Section 5. Section 6 presents the simulation configurations. Then, results and discussion are described in Section 7; and the economic feasibility of the proposed solar system is discussed. Lastly, Section 8 concludes the work by highlighting the attained results.

The principle cell zooming strategy depends on the capability of allowing for the adjustment of the size of the cell according to the traffic load. When congestion occurs in a cell due to an increase in the number of users, the congested cell could “zoom-in,” while neighboring cells with a smaller amount of traffic could “zoom-out” to provide coverage for the users that cannot be served by the congested cell [9]. The incoming traffic arrival intensity depends on the tempo-spatial domains as it changes over time and space owing to the user mobility. For example, the commercial area experiences high traffic demand whereas, lower in the residential area particularly in the day time. The scenarios are getting reverse during the evening period. Cell zooming is an effective method for improving energy efficiency by adjusting traffic load. The centralized control server coordinates traffic demand and make zooming out decision accordingly. If the traffic intensity of the considered BS is higher than the threshold value, the control server sends information to zoom in the cell size. On the other hand, the BS under test moves to sleep mode if UEs are shifted to neighboring BSs or when the traffic intensity becomes much lower. Under this case, the surrounding BSs zooms out to serve new incoming users via a control server through cooperation. Thereby, the sleep mode provision governed by the cell zooming technique enables to reduce of additional energy consumption. Fig. 1 summarises the proposed cell zooming strategy. The infrastructures of the cellular networks have been designed to support daytime traffic i.e., (peak time). However, the traffic loads during the daytime differ from those during the night, as shown in Fig. 2. Thus, the BSs must effectively deal with the traffic load to achieve efficient energy utilization and better saving.

If the traffic density in a particular BS is below the threshold value, then the particular BS shifts its loads to the neighboring BSs according to their current traffic status and the BS goes into sleep mode to reduce energy consumption. In such a case, the accepting BSs can zoom out to serve the new arrivals in a cooperative manner. Note that the provision of sleep mode is appropriate for low traffic density under off-peak duration. However, the neighboring BSs can only serve the incoming BSs based on their unoccupied resource blocks and green energy availability without sacrificing its own performance. In other words, the acceptor BSs adjust their transmit power to extend the coverage distances allowing the low-density BSs to enter dormant conditions. On the other hand, the BSs experiencing high traffic demand exceeding their capacity will share the load to surrounded low densified BSs according to the proposed heuristic algorithm. The two different heuristic cell zooming algorithms are described in the following.

Set of m users k={UE1,UE2,…,UEm}; number of resource blocks N_RB. Binary variable χi,j signifies the traffic demand at ith BS (BS_i) of jth user, as in the following,

(1) Xi,j={1,ifUEjisservedbyBSi0,otherwise;∀i∈N,∀j∈U

In the cell zooming algorithm, Si,j=S1,S2,…, SZi = set of BSs in operating mode whose traffic is to be shared under shortest path between UE-BS and availability of green energy; neighboring BS is Ci,j={Ci,1,Ci,2…Ci,Mi}; acceptor candidate BS set Ci,ja (i.e., handle new arrivals from S_Zi); traffic distribution it owns set of operating BSs Zi,j. On the other hand, Ci,js=βi goes to sleep mode when it can redistribute its all traffic among surrounded BSs (Ci,ja) in S_{i, j}, χi,j=χ1,χ2…χk, Fi, j=βi searches the best combination BSs (Fij) in receiving shifted load, Ci,ja or Ci,js. However, the notations used in the algorithm section is presented in Tab. 1.

Energy Consumption Gain (ECG): ECG metrics accounts the energy required for sending requested data transmission over a particular duration. ECG in green communications can be defined as the ratio of the ECR metrics of the proposed system to the reference schemes under the specified network settings. For instance, the network architecture without cell zooming is considered as a reference baseline and cell zooming enabled green-powered cellular system is the proposed scheme recognized as a more energy-efficient architecture.

A system with lower ECG identified as more energy efficient as it consumes low power to transmit the same amount of data transmission.

(2) ECR=ECRproposedECGbaseline×100%

However, ECR is an equipment level standard that evaluates the achievable throughput of the entire radio access network per given power expenditure. On the other hand, ERG metrics are sometimes preferable for energy efficiency analysis in the area of green cellular networking. The ERG can be expressed as

(3) ERG=(1-1ECG)×100%

Energy Saving Index (ESI): ESI metric measures the energy-saving gain introducing the cell zooming concept under different zoom out range. It can define as

(4) ESI=Eα-EμEα×100%

where Eα indicates the power consumption without incorporating cell zooming technique and Eμ denote the power requirement with cell zoom out for the entire cellular architecture respectively.

Load factor (δ): δ represents the ratio of the number of resource blocks occupied (RB_O) to the available resource block (RB_T) in a given system bandwidth. Without loss of generality, we presume one RB is occupied by a single active user and the occupancy of RB allocation linearly scaled with incoming traffic profile. As the number of demand request increases led to occupied more number of resource blocks in order to satisfy the QoS. Under the proposed framework, when the total available RBs are occupied in a particular BSs, the next available users can be associated to the neighboring BSs without call service drop. However, δ can be expressed as

(5) δ=RBoRBT

A RB is the least unit of physical resource allocation assigned to every user. Resource block allocation decide the number of simultaneous users connected to the LTE base stations. In LTE, one physical RB occupied 10 kHz bandwidth, having 12 subcarriers (each 15 kHz) and 14 symbols over 1 msec duration. Typically, 10% RBs are kept reserved in LTE system in order to avoid overlapping issue.

The total annual energy extraction from the PV arrangement (E_PV) is derived as [11],

(7) EPV=PCPV×PSH×DFPV×365day/year,

where PC_PV states a peak capacity of the PV array (kW); PSH denotes peak solar hour and is calculated based on the equivalent average daily solar irradiation. DF_PV states a derating factor of PV array, considering the impact of dust, losses, temperature variations, and other potential factors that can distress the output power of the panel.

The BESS capacity of the BS merely depends on the depth of discharge (DOD) and must be evaluated before commissioning. It can be expressed as [11],

(8) DOD=1-SOCmin100

where SOC_min denotes the minimum state of charge (SOC). The computation of days of autonomy (Abatt) is essential to determine the performance of the fully charged batteries, i.e., the number of days fully charged batteries can supply the power to the load. It is derived as [6],

(9) Abatt=Nbatt×Vnom×Qnom(1-SOCmin100)(24h/d)Lprim-avg(1000Wh/kWh)

where the terms N_batt and V_nom are the total number of battery units in the BESS and a nominal voltage of a single battery unit (V), respectively. The terms Q_nom and L_{prim, ave} are the nominal capacity of a single battery (Ah) and average daily BS load (kWh), respectively.

The lifetime of the battery plays a crucial role. The lifetime of a battery can be predicted based on the operating conditions. More specifically, the DOD during each diurnal charge-discharge cycle displays a foremost role in the battery lifetime. It can be computed as [11],

(10) Rbatt= min(Nbatt×QlifetimeQthrpt,Rbatt,f)

where the term Q_lifetime represents the lifetime throughput of a single battery in kWh, Q_thrpt denotes the annual battery throughput in kWh per year, and the term R_battf states a battery float life in years.

The total capacity of the inverter (C_inv) is calculated as [11],

(11) Cinv=(LACηinv)×σsf

where the term L_AC represents the available maximum AC load; ηinv and σsf is the inverter efficiency and safety factor, respectively.

The optimal capacity of the PV array, as determined by the HOMER, is 2.5 kW. The designed PV array has 10 Sharp modules, and each proposed module is rated at 250 W with a nominal voltage of 29.80 V, nominal current of 8.40 A, open-circuit voltage of 38.3 V, and short circuit current of 8.9 A. The yearly energy output of the PV is calculated using Eq. (7): the total energy output of the PV is calculated as 3180 kWh (2.5 kW × 4.1 h × 0.85 × 365 days/year). In addition, the generating energy increase up to 34.01%, i.e., 4262 kWh more annually using a dual-axis tracking system. This energy yield also notably meets the losses incurred in the system battery and inverter loss of approximately 453 and 12 kWh, respectively, and supplies the power to BS load (3582 kWh), and results in annual excess energy up to 680 kWh, i.e., 15.95% of the total energy generation. in August.

The ratios of annual energy output and input of the BESS, i.e., 2625 and 3077 kWh, respectively. Roughly, the BESS supplies the power to the load about 237 h, specifically during the malfunction of the PV array. The seasonal statistics that the maximum energy contribution of the BESS is in August, while the minimum energy contribution in April. Besides, the frequency histogram of the state of charge (SOC) shown that the state of charge stretched to 42%. The average hourly energy generation of the PV, BESS, unmet load, and excess electricity is presented in Fig. 6. It is observed that the lower rate of energy contribution from PV array is in August; therefore, the higher rate of energy contribution from the BESS is in the same period.

The net capacity of the inverter unit is 0.1 kW, and its efficiency is computed between the input (236 kWh) and output energy (224 kWh) annually and observed as 95%. The total operating hours are 8,759 h/year (24 h × 365 days/year).

A cash flow summary for Seoul city is presented in Fig. 7 (project lifespan). A detail of the breakdown of the cash flow summary is as follows.

The net NPC is calculated by adding up the aggregated cash flows for each year and found to be $26,887 i.e., $21,740 (IC) + $6660 (O&M prices) − $1513 (salvage).

A quantitative comparison of the ECG identifying the impact of two different cell zooming techniques is clearly presented in Fig. 8. As expected, the performance of both schemes remains unchanged under no cell zooming conditions. As the cell zooms out more, less energy consumed by the entire RAN architecture. According to the definition, the distance-traffic based cell zooming scheme consumes less amount of power for the given value of zoom out level. As a consequence, the distance-traffic aware cell zooming method attains about 4.8% less energy consumption in comparison to the green energy aware as evident from the Fig. 8. On the other hand, the energy consumption is about 27% lower when the cell zooms out entirely than the 20% zooming level.

Fig. 9 compares the ERG metric with the percentage of cell zooming under 10 MHz system bandwidth. Note that ERG performance can be derived with the help of ECG in accordance with the definition. ERG defines the reduction of total power consumption to transmit a single bit of information. Distance-traffic aware based cell zooming scheme shows improved performance by about 5% in comparison with green energy-based zooming technique.

A detailed comparison of two different BS zooming options such as distance-traffic aware and green energy availability is demonstrated in Fig. 10. The distance-traffic aware scheme keeps its optimistic nature in terms of average energy. Further, Fig. 10 depicts that distance traffic-aware cell zooming mechanism achieve 6.5% more energy savings in their counterpart and exhibits superior performance for the higher level of zooming.

Figs. 11–13 depicted the average energy-saving performance comparing between the distance-traffic and green energy availability aware schemes with the BS sleeping hours per day, level of cell zooming, and percentage of shifted traffic to the collocated BSs respectively. As observed, the distance-traffic aware technique keeps its optimistic nature in all these three figures by means of a considerable amount of energy savings. Notably, the energy savings gap between the two aforementioned schemes of cell zooming is more apparent for the higher values of the zoom-out level, BS sleeping hours, and shifted load demand. On the other hand, the separation of energy-saving performance curves follows a similar pattern for the lower values of variable parameters. This is because, for the lower values of zooming level, shifted load, and sleep mode duration, the improvement of energy savings keep remains identical between the two methods. In contrast, the green energy aware technique shows inferior performance as the available energy at the on-site BS may not sufficient to guarantee all the traffic demand. Whereas, the distance-traffic aware demonstrates comparatively reliable performance for a prolonged period since this scheme does not depends solely on the green power supply. In a nutshell, owing to the random fluctuations of RE energy production on-site BSs, a solar energy-based cell zooming scheme is not a preferable choice for achieving a greater level of system performance.

Fig. 14 illustrates the percentage of energy-saving versus the resource blocks occupancy i.e., load factor. Both the curves follow the identical patterns to reach their least values with the increment of δ. The energy-saving curve for the proposed system outperforms the conventional one as evident from the graph. Here, the conventional schemes signify the cellular system operating without considering cell zooming options. The curves move toward the downward direction with the highest occupancy of resource block numbers as explained in the performance metric section. However, the green curve in the Fig. 14 is derived from the distance-traffic aware cell zooming scheme. Notably, the new traffic arrivals will be associated with the closest neighboring BSs once the contemplated active BS has no empty resource block, and thereby, the quality of service remains uninterrupted. From the aforementioned analysis, it can be concluded that incorporating the cell zooming concept with the traditional cell networks can achieve a greater level of energy savings performance satisfying the quality of experience.

The key priority of the mobile operators is to increase the profit with reduced OPEX in the cellular network. The economic viability with the PV system over conventional energy resources are as follows;

In remote areas, i.e., an off-grid station, the Diesel generator (DG) is usually employed to power the BS. The DG rating should be around 3.5 kW that can be figured between the ratio of maximum BS and 30% DG efficiency × converter efficacy of 95%. The net NPC is computed and recorded as $50,736. It is observed using the summation process of $2,310 (IC costs) + $41,496 (O&M costs) + $6,930 (replacement costs). The detailed description of the above costs are as follows;

The net NPC of the solar system is approximately $26,887. Applying the proposed solar system, total OPEX savings $23,849 which is 47% compared with the conventional power sources.