1. Introduction
Massive growth in the number of mobile users and mobile user equipment (UE) has sown the seeds of data hunger and severely high traffic congestion, especially in cellular networks. This problem has been aggravated by the emerging 5G technologies, which leads to increasingly dense network deployments to cater for growing user demands. The densification of base stations (BSs) within a geographical area has led to the emergence of a new network architecture known as a heterogeneous network (HetNet), which employs multiple BSs with different transmit power capabilities [
1]. The unprecedented network densification through HetNets deployment has resulted in a scarcity of radio resources (particularly spectrum) and a tremendous increase in energy consumption. Over the years, many radio resource management techniques have been proposed to better utilise radio resources, which has cost the mobile service providers (MSPs) billions of dollars yearly. Recently, it has been reported that, other than the costly radio resources, the MSPs have also spent as much as half of their annual operating expense on energy costs [
2]. Hence, making HetNets spectrally and energyefficient not only can result in a tangible positive environmental impact, but also help MSPs in attaining longterm profitability.
Undoubtedly, the energy consumption of cellular systems is highly dependent on the mobile users’ characteristics. The user activities and data usage patterns in a large cellular network can be modelled statistically using a Markov model with trinonnegative matrix factorisation [
3]. This work has demonstrated that data usage across mobile users are severely uneven for a cellular network. From this report, we can infer that the data usage and user activities for a HetNet will follow this pattern and may demonstrate a more uneven and unpredictable trend due to irregular cell sizes in HetNet. One observation from this work implies that aroundtheclock, not every hour is considered a peak hour in terms of data usage. Indeed, during nonpeak hours, the excess radio resources are being wasted together with the energy used to power the various networking devices in a HetNet [
4]. Studies suggest that most of the power consumption (about 58%) of a communication system is mainly contributed by the base station (BS) [
5]. Thus, to reduce wastage of radio resources and save energy, certain smallcell BSs with low spectrum utilisation in a HetNet can be switched off; however, it is noteworthy that it does not mean there are no mobile users at all during offpeak hours. On the other hand, there might be a small number of users who still need to access the services of the BSs; therefore, shutting down any smallcell BS in this scenario is unwise and it may lead to a coverage hole where the affected smallcell users could lose connection to any BS services permanently. Fortunately, HetNet architecture can fill the coverage hole by allowing macro BS (MBS) to serve the affected smallcell users [
6].
Nevertheless, deployment of multiple cells within a HetNet leads to intercell interference (ICI) since the coverage area of smallcell BSs overlaps with the coverage of MBS. The ICI effect can be mitigated by using a coordinated multipoint jointprocessing (CoMPJP) technique [
7]. In a HetNet equipped with a CoMPJP mechanism, the MBS and smallcell BSs share their channel state information (CSI) and data to coordinate the transmission process and hence avoiding interference. As specified in the CoMP standards of 3GPP Release 9, 10 and Beyond [
8], apart from increasing a particular UE’s date rate, the appropriate coordination of signals received from multiple interfering cells also improves the performance of the overall network with enhanced coverage area. Anyhow, the BSs that are isolated geographically should have proper synchronisation to avoid performance collision.
Switching off BSs in a HetNet may not be sufficient to reduce energy consumption; therefore, a more energyefficient solution needs to be sought urgently because increasing energy consumption due to the massive deployment of HetNets has contributed significantly to greenhouse gases emission (GHGE) as well as pollutants related to global warming. According to the studies performed by the authors in [
9], GHGE contribution by information and communication technology (ICT) would surpass half of the current transportation sector contribution by 2040 and about 14% of the total global carbon footprint. Thus, substituting nonrenewable energy with renewable energy (energy harvesting) in HetNets is seen as one of the possible solutions to reduce brown energy consumption by HetNets, leading to lower carbon emission; therefore, energy sustainability is important in order to maintain the equilibrium between the degrading environmental factor and increasing HetNet user demand. In addition, energy harvesting also improves the energy efficiency (EE) of a HetNet by reducing the grid power consumption (GPC) as suggested by the studies conducted in [
10]. Undoubtedly, GPC is also regarded as one of the factors contributing to increasing carbon footprints.
Apart from energy efficiency, hybrid power source also greatly contributes to communication during emergency situations. Wireless communication by itself is considered an emergency communication strategy [
11]. Nevertheless, depending solely on grid power source can backfire during grid power outages caused by natural or manmade disasters, i.e., earthquake, landslide, war and so on; therefore, it is very crucial to have a backup plan, such as renewable energy sources during emergency situations.
Nevertheless, irregular patterns of energy harvesting should be taken into consideration while evaluating their contribution to the EE of a HetNet. In spite of improving just a singlecell EE, sharing the excess harvested energy (HE) among the coordinating BSs would further enhance the EE of the overall HetNet [
12]. This can be explained by comparing the available energy per BS and its user strength and /or distribution, which rarely compensates one another. Several technologies, such as power lines and smart grids, are being studied for energy cooperation among the connected BSs. The research conducted in [
13] discusses safe and reliable architectures and current implementations of the smart grid; therefore, it is clear that utilising this technology in this work has the potential for significant and reliable contribution. In recent works [
14], less complex and simplified smart grid applications are designed specifically for communication systems. In this work, a smart grid is considered to coordinate the transfer of the excess HE among different BSs and also as an energy storage medium. As the case studies carried out in [
15] suggest, energy storage management for smart grids is a vast separate topic and was not taken into consideration in this work.
The remainder of this paper is structured as follows.
Section 2 describes some existing work related to this research and the contributions of this work.
Section 3 illustrates the CoMPbased hybridpowered HetNet model together with an explanation of the problem. In
Section 4, the proposed algorithm is described in detail and the principal algorithm is explained in parts for better insight. Next,
Section 5 presents the performance evaluation results and the discussions. Finally, the conclusions and potential future research directions are drawn in
Section 6.
2. Related Work and Contributions
The survey carried out by authors in [
16] highlights the predominant contribution of BSs to the total power consumption of cellular networks. This work also studies the integration of smart grids to the REpowered cellular networks, including analyses on architecture and challenges that could be faced in the implementation; however, BSSw is not considered as an additional factor to enhance the EE of the system. Similarly, research conducted in [
17] discusses the feasibility and benefits of integrating RE resources into the smart grid but again disregarding BSSw. There are also several works that study realtime BS control or BSSw to maximise energy savings. For instance, in [
18], the authors employed the Markov decision process (MDP)based algorithm to develop such a system. The operational mode of the BSs switches according to the capricious state (arrival and departure) of the active UEs; however, frequent and unexpected switching of BSs may not be efficient in practice [
19].
BSSw is also studied from the perspective of energy cost in [
20] where the energy expenditure can be reduced at the expense of poorer QoS. Moreover, the main concern in this work is communication cost reduction based on realtime energy pricing. Similarly, the studies in [
21] focus on monetaryrelated issues such as operational and energy cost saving among the mobile network operators. In this work, a Markovian model is used as an RE generator and manager. The work in [
5] studies BS coordinationbased switching design for a green cellular network by making use of a selfadaptive scheduling algorithm without utilising energy harvesting. This work also suggests that BSs are the prime contributors to the total power consumption of the cellular network through the chart shown in
Figure 1. Authors in [
22] also studied the methods to reduce the grid power consumption of cellular networks through renewable energy without affecting user service quality by utilising a coalitional technique to manage the association between the user and the BS; however, BSSw was not considered in this work.
Thus far, the existing works related to BSSw for HetNets have not been integrated with RE, smart grid, power control and data cooperation under a single objective. A general review of some recent relevant works that employ BSSw is presented in
Table 1. The work in [
23] optimises energy performance and offloads macrocell traffic without considering power allocation and energy sharing. In [
24], the system EE is maximised by incorporating power control and data cooperation but there is no RE harvesting facility. On the other hand, the work in [
25] looks into minimising the total power consumption by utilising power management and RE sharing disregarding data cooperation. The work in [
26] contributes to maximising ongrid energy savings through energy sharing but no data cooperation or power allocation has been considered. Furthermore, the authors of [
27] have enhanced the network EE by employing data cooperation discounting energy harvesting and sharing. Unlike the existing work, the proposed algorithm in this paper maximises the energy efficiency by utilising data cooperation and power allocation that mitigates the interference while employing renewable energy source to reduce the grid power consumption. Subsequently, excess energy is shared among the BSs through smartgrid and BSSw is applied to manage the underutilised BSs, which causes unnecessary energy and resource wastage.
Motivated by the above mentioned problems, in this paper, we propose an algorithm to efficiently allocate transmit powers to all BSs and/or switchoff an optimal set of BSs to improve the EE of a HetNet consisting of CoMPenabled hybridpowered BSs subject to a minimum qualityofservice (QoS) for all mobile users. Hybrid power in this context implies that the BSs are powered by both the grid power source as well as the energy harvesting source. Energy exchange is enabled via the smart grid among different BSs so that BSs with excess harvested energy can channel out the energy to other BSs demanding for extra energy. To the best of our knowledge, this is the first work to incorporate base station switching (BSSw) into a CoMPenabled hybridpowered BS connected to a smart grid. Unlike the work in [
12], which proposes a joint power allocation and energy cooperation for a CoMPenabled HetNet, this work extends the previous works further by considering the BSSw and power allocation in a CoMPenabled HetNet, which employs smart grid for energy cooperation. The problem formulated is more challenging by taking BSSw into account because the strategy to switch off the BS has a direct impact on the power allocation and energy cooperation. In short, the main contributions of this paper are summarised as follows:
Develop a HetNet infrastructure with CoMPenabled hybridpowered BSs framework by incorporating BSSw technique to improve the EE of the smart grid based HetNet and hence reducing the GPC and mitigating interference among the cells.
Formulate an EE optimisation problem for a CoMPenabled hybridpowered HetNet that allows BSs to cooperatively allocate their transmit powers and optimally switch off subject to the minimum QoS requirement.
Design an optimal binomial and two suboptimal heuristic optimisation methods to solve BSSw problem in order to maximise EE and simultaneously reduce the total GPC by utilising the harvested renewable energy (RE).
Propose an energyefficient joint power allocation and BSSw algorithm by incorporating the subgradient method into the proposed binomial or heuristic methods to reduce computational complexity. Along with it, Dinkelbach’s algorithm is adopted additionally to ensure faster convergence speed while solving the EE problem.
3. System Model and Problem Formulation
Figure 2 illustrates the downlink transmission of hybridpowered HetNet infrastructure considered in this work, which consists of a macrocell overlaid by multiple small cells. Each cell is served by a BS capable of harvesting RE; therefore, it can be powered by both grid power and an RE source. The power lines of all the BSs are connected to a smart grid that facilitates the energy exchange among the BSs. More specifically, if there is excess HE at any BS, the excess HE will be transferred to the smart grid to share. It is also assumed that the BSs coordinate among each other to eliminate interference through CoMPJP technique to serve the users.
For simplicity,
Table 2 lists the notations of the main variables and parameters used throughout the paper. The data rate of user
i and the total grid power consumed are denoted by
${R}_{i}$ and
${P}^{Total}$, respectively, which can be represented as follows:
where
${a}_{ji}$ is a Boolean value indicating association of user
i with BS
j where
${a}_{ji}$ = 1 if the user
i is associated with BS
j, and otherwise. Similarly,
${b}_{j}$ is also a Boolean value representing the on/off status of BS
j where
${b}_{j}$ = 1 if BS
j is on, and otherwise.
${E}_{j}$ represents the HE of BS
j and
$\rho $ denotes the HE efficiency. The EE is maximised by optimising the transmit power allocation and BS switching of SBSs. It is noteworthy that MBS is always ON, and therefore,
${b}_{1}$ is always 1.
The system EE is maximised by formulating an optimisation problem aiming to reduce total GPC. In this case, EE is defined as ratio of system total throughput to the total grid power consumption. Hence, the objective function and the constraints are shown as follows:
Constraint (3b) limits the transmit power of the BS to ${P}_{j,max}$. Constraint (3c) guarantees that every user can achieve a minimum SNR denoted by the threshold $SN{R}_{i,thr}$. Constraint (3d) ensures that each UE is served by at least one BS. Constraint 5 enforces the BSSw condition by evaluating the ratio of harvested energy to BS transmit power. Only if the SBS fulfils the condition, where the ratio equals to at least the threshold ${E}_{j,thr}^{R}$ value, it will be allowed to be switched on.
4. Proposed Technique
The problem formulation given in (3a) is a nondeterministic polynomialtime (NP)hard combinatorial optimisation problem where determining its optimal solution within a given time is very challenging. Performing a direct exhaustive search at each BS would incur a prohibitive computational burden, which is not feasible due to the rapid variations of wireless channels. In certain cases, it may be solvable in polynomial time, but would require using an impractically slow algorithm; therefore, for practical implementation, the problem formulation in (3a) can be decomposed into two subproblems, i.e., (1) power allocation (PA) problem and (2) BSSw problem. Binomial or heuristic methods can be used to solve the former problem while the latter can be modelled as a Lagrange dual problem.
4.1. Combinatorial Optimisation Algorithm
Furthermore, the formulated EE problem in (3a) is classified as a nonlinear fractional programming, thus, solving the problem by applying general mathematical approaches greatly increases its complexity; therefore, a significantly lesscomplex iterative algorithm, based on Dinkelbach’s method can be utilised to obtain the solution as presented in Algorithm 1. Dinkelbach’s theorem is generally used to simplify the nonlinear complex fractional problem into a simpler iterative algorithm [
28]. First, the objective function is converted from fractional into a nonfractional function with a parameter
$\tau $ (refer to
Appendix A for derivation). Initially, the parameter
$\tau $ is set to 0 as stated in step 1 of Algorithm 1. Along with it, the maximum number of iterations and convergence condition are also initialised accordingly. The optimal EE
${\tau}^{*}$ is only achievable upon fulfilment of convergence condition, otherwise, the obtained EE value
$\tau $ is used in the following iteration to solve the problem as shown in steps 10–15 of Algorithm 1.
The Lagrangian function is evaluated in each iteration as indicated in step 7 of Algorithm 1 based on the BS On/Off statuses and updated transmit power allocation values. Along with the dual decomposition method, steps 4–9 in Algorithm 1 show the combined application of subgradient method to obtain the optimal power allocation value. Simple characteristics, i.e., predefined constant step size, of subgradient method make it more preferable to be applied compared to the gradient method, which uses a computed instantaneous step size [
29]. Once the formulated problem is divided into subproblems with the aid of Lagrange duality, each subgradient is updated iteratively with a constant step size
$\delta \left(n\right)$ as indicated in step 8 of Algorithm 1. Consequently, each of the Lagrange multiplier values is also updated in every iteration and the convergence condition is evaluated each time. The condition is violated only if the difference between the previous and current values of the Lagrangian function drops below the convergence threshold as can be seen in step 9 of the algorithm.
Algorithm 1 PABSSw algorithm. 
 1:
Set maximum number of iterations ${T}_{out}$; convergence condition $\u03f5$ & ${\tau}^{\left(1\right)}=0$  2:
Set iteration index, $n=1$  3:
Set the ${b}_{j}$ status of each base station using one of the BSSw methods:  i
Optimal (Binomial theorem)  ii
Heuristic 1 (Distancebased)  iii
Heuristic 2 (Random)
 4:
for$1\le n\le {T}_{out}$do  5:
Initialise Lagrange multipliers to an arbitrarily large positive value.  6:
Update power allocation (obtained from dual decomposition), Equation ( 9).  7:
Evaluate Lagrangian function (of the primal objective function) $f\left(n\right)$, Equation ( 5).  8:
Update Lagrange multipliers with small positive step size $\delta \left(n\right)$: ${\alpha}_{j}^{(n+1)}={[{\alpha}_{j}^{\left(n\right)}\delta \left(n\right)\ast ({P}_{j,max}{\sum}_{i}{a}_{ji}{b}_{j}{p}_{ji})]}^{+}$ ${\beta}_{i}^{(n+1)}={[{\beta}_{i}^{\left(n\right)}\delta \left(n\right)\ast (\frac{{\sum}_{j}{a}_{ji}{b}_{j}{p}_{ji}{g}_{ji}}{{B}_{o}{N}_{o}}SN{R}_{i,thr})]}^{+}$  9:
Repeat steps 68 until $\leftf\right(n+1)f(n\left)\right\le \u03f5$ or maximum number of iterations is reached.  10:
if $R({p}^{\left(n\right)},{b}^{\left(n\right)}){\tau}^{\left(n\right)}{P}^{Total}({p}^{\left(n\right)},{b}^{\left(n\right)})<\u03f5$ then  11:
$set\phantom{\rule{3.33333pt}{0ex}}{\tau}^{*}={\tau}^{\left(n\right)}$  12:
break  13:
else  14:
$set\phantom{\rule{3.33333pt}{0ex}}{\tau}^{(n+1)}=\frac{R({p}^{\left(n\right)},{b}^{\left(n\right)})}{{P}^{Total}({p}^{\left(n\right)},{b}^{\left(n\right)})}$  15:
$n=n+1$  16:
end if  17:
end for

4.2. BSSw Techniques
While the dual decomposition method is applied to solve the PA part of the formulated problem, three other methods are proposed to determine the BS switching status. One of them evaluates an optimal set of sleeping SBSs by adopting binomial theorem to maximise the EE function, whereas the other two proposed heuristic methods (i.e., distancebased and random BSSw schemes) provide a suboptimal solution as shown in step 3 of Algorithm 1. The computational complexity of the optimal technique (binomial theorem) increases exponentially as the number of SBSs is increased while the heuristic techniques have a constant and smaller computational complexity. Since the BSSw and PA are intertwined problems where their optimisations are interdependent, the PA and BSSw schemes can be integrated as a single algorithm (PABSSw). Depending on the BS on/off status attained through the BSSw method, the transmit power allocation is evaluated. Thereafter, the EE is determined through Dinkelbach’s method. These processes are carried out until an optimal EE value is achieved through the iterative algorithm as shown in step 10–16 of Algorithm 1.
The binomial theorem is used to obtain the BS on/off statuses where all possible combinations (i.e.,
${2}^{SBS}1$) of SBS on/off is evaluated as shown in Algorithm 2. It is worth noting that the MBS is always on in this work, i.e.,
${b}_{j\u03f5J\setminus \left\{1\right\}}$. The best output that yields the highest EE is chosen from the results as indicated in step 6 of Algorithm 2; therefore, this method is regarded as the optimal BSSw technique.
Algorithm 2 Binomial method. 
 1:
Declare two arrays of (${2}^{SBS}1$), (i) combination & (ii) EE.  2:
for ∀ combination. do  3:
Off SBSs of the combination.  4:
Evaluate EE of the combination.  5:
end for  6:
Off combination of SBSs with highest EE.

In order to satisfy the target QoS of the user with higher channel gain, allocating less transmit power is sufficient [
30]. Consequently, the total power consumption will be decreased, leading to a higher EE. Higher channel gain is viable through decreased path loss, which is achievable through shorter distance between BS and user [
31]. The example shown in
Figure 3 consists of four small cells (SCs) located randomly within the coverage of the MBS. The distance of SBS
j from MBS is indicated by
${D}_{j}$, whereas
${d}_{i}$ represents the distance of user
i from MBS and
${\sum}_{i}{d}_{i}^{j}$ is the sum of distances of all users of the particular SBS
j to MBS. The SBS with lowest
${\sum}_{i}{d}_{i}^{j}$ value is considered to be switched off. As the number of users per cell increases, the possibility for the SBS to be switched off will be consequently reduced, in other words, the number of users per cell is taken into consideration while switching off the SBSs.
The proposed BSSw strategy employs two distancebased thresholds,
${D}_{j}^{t}hr$ and
${d}_{i}^{t}hr$, to determine the on/off state of an SBS.
If
${D}_{j}<{D}_{j}^{thr}$ or
${\sum}_{i}{d}_{i}^{j}<{d}_{i}^{thr}$ or both of the conditions are satisfied, then the SBSs that fall in the category and the combination of the SBSs are shortlisted to be switched off as shown in step 4 of Algorithm 3. Thereafter, the EE of those combinations are evaluated and the combination that provides the best EE among them is chosen to be switched off, i.e.,
${b}_{j}=0$. For instance, in
Table 3, the highlighted cells indicate the SBSs that fulfil the value less than the threshold value for the example shown in
Figure 3. From
Table 3, we can deduce that SBS 1 or SBS 2 or both SBS 1 and SBS 2 can be considered to be switched off. Thus, after evaluating the highest EE obtained by switching off the mentioned combinations of SBSs, the switching decision is made.
Algorithm 3 Distancebased method. 
 1:
Initialise $n=0$; Declare an empty array ${J}_{off}$  2:
for ∀ SBSs (j) do  3:
if ${D}_{j}<{D}_{j}^{thr}or{\sum}_{i}{d}_{i}^{j}<{d}_{i}^{thr}$ then  4:
${J}_{off}={J}_{off}$ + j  5:
$n=n+1$  6:
end if  7:
end for  8:
Apply binomial theorem, ${2}^{n}1$ for all combinations of ${J}_{off}$

This heuristic method is proven to be effective through analysis and observation of thousands of realisations (realisations here refers to the EE evaluation and comparison performed for the binomial and distancebased BSSw methods for different settings of SBSs and UEs distribution within the macrocell to prove the accuracy of the distancebased method), but for the sake of clarity, we only show 100 realisations in
Figure 4. The plot illustrates the comparison between the EE of binomial and distancebased switching techniques, where the binomial method is around 5% better than the latter. Furthermore,
Figure 4 also indicates the matching rate of distancebased method with the binomial method in terms of SBSs switching statuses. It is observed that around 80% realisations of distancebased method matches the binomial method (Boolean representation: the status “1” indicates that the switching decision of distancebased method matches that of the binomial technique and the status “0”, otherwise). Algorithm 4 shows the steps to obtain the accuracy or EE matching percentage. The EE of the distancebased method is displayed as a stacked line for a distinct comparison. The analyses were carried out using different numbers of SBSs and UEs per cell as shown in
Figure 4 to validate the performance of the proposed distancebased strategy. It is noteworthy that the EE difference and matching rate of all the considered scenarios are similar.
Algorithm 4 Distancebased performance validation method. 
 1:
Initialise $counter=0$; Declare Match as Boolean value.  2:
for$1\le realisation\le 100$. do  3:
Difference = EE (binominal) – EE (distancebased).  4:
if Difference = 0. then  5:
Match = 1.  6:
$counter=counter+1$.  7:
else  8:
Match = 0.  9:
end if  10:
end for  11:
Accuracy(%) $=counter$.

Random SBS based on discrete uniform distribution is set to off as shown in Algorithm 5. This distribution follows an equal likelihood to switch off the SBSs (the probability of each SBS to be switched off is the same, e.g., if the number of SBS = 3, then the switch off probability of each SBS is 1/3).
Algorithm 5 Random method. 
 1:
n = number of SBSs.  2:
Off random (n) with probability 1/n.

4.3. Dual Decomposition
To obtain the optimal transmit power, the PA problem can be converted into Lagrangian optimisation and the problem can be solved using the Lagrangian dual method. It is worth noting that this method was used in the author’s previous work to solve a similar problem [
12]. First, the Lagrangian function of the PA optimisation problem is formed as in Equation (
5). Then, the dual problem is developed accordingly as shown in Equation (
6). Due to its convex characteristic, the dual decomposition method is used to solve the Lagrange function. Consequently, the dual problem is determined by solving the corresponding Karush–Kuhn–Tucker (KKT) conditions [
32] as in Equation (
8). Finally, the optimal transmit power, which is obtained by applying the dual decomposition method, is expressed in Equation (
9).
where
${\alpha}_{j}$ and
${\beta}_{i}$ are nonnegative Lagrange multipliers and
$\tau $ denotes EE based on Dinkelbach’s method [
28] (see
Appendix A).
To obtain an optimal power allocation for BS
j serving user
i, Equation (
5) is derived with respect to
${p}_{ji}$ and equated to zero:
where
${p}_{ji}^{*}$ represents the optimal power allocation.
6. Final Remarks
EE has recently attracted the attention of researchers in the mobile communication field due to the increasingly disastrous impact on the environment caused by less energyefficient applications. Generally, increasing the QoS of the users or throughput increases the grid power consumption of the communication system. On the other hand, a massive number of users leads to interference issues as well as increased energy demand. Further, the number of users is not always significant, leading to energy wastage during offpeak hours. Hence, it is crucial to switch off underutilised BSs and allocating UE power, which simultaneously solves throughput as well as power consumption issues. This work proposes an energyefficient joint BSSw and power allocation scheme for a CoMPbased HetNet architecture with hybrid power sources. By using a combinatorial (subgradientbinomial or subgradientheuristic) optimisation technique, joint BSSw and power allocation yields about 15–20% higher EE compared to the noncooperative or nonharvesting systems. The proposed distancebased BSSw method is proven to perform better in terms of EE compared to the random method in all tested scenarios. Though its EE performance is around 5% poorer than the optimal binomial method, the computational complexity is proven to be much lesser than the former. Overall, the distancebased BSSw method of the cooperative harvesting system performs the best in terms of both performance gain and computational complexity. The application of BSSw to MBS would greatly enhance the EE of the system, but in order to avoid a coverage hole, techniques such as cell zooming have to be employed, which is left for future work. Apart from that, the application of a smart grid is not limited to only energy sharing, it can be extended to more advanced timeseriesbased energy and traffic management. The energy harvesting and traffic model used in this work is based on instantaneous values. For more practical applications, the trending machine learning techniques can be utilised to learn the energy and traffic pattern sampled through the timeseries. Consequently, the overall system performance can be enhanced with reduced complexity caused by realtime computations.