EJES TEMÁTICOS

In Fig. 4.4, simulation results of Case-2 with T = 2, T = 3 and T = 4 are presented to analyze the effect of time window T . From Fig. 4.4 we can see that higher time steps in the time window T can enhance the performance of the approach. In this case, T = 2 is not sufficient as decisions for managing storage is myopic. That is, there is not enough knowledge of future trends of cost/harvested energy to guide our current behavior. Time window lengths of T = 3 and T = 4 both seem to provide reasonably intuitive results. As T increases beyond 4, we observe that there is no significant improvement in performance. It is important to realize that computational cost does increase with increasing T . Thus, identifying the most suitable time window for a specific system is a design parameter that needs to be carefully selected.

In summary, the effectiveness of the proposed two-stage BS operation is illustrated by numerical results of Case-1, Case-2 and Case-3. Simulation results also indicate that while a larger time window T can enhance the performance of the approach, the improvement would have a diminishing return as well.

4.6

Summary

This chapter investigates, for the first time, management of energy harvesting BSs in HCNs from a techno-economic point of view. We consider real-time electricity price, QoS of users and harvested energy profile in identifying methods to minimize energy cost. To solve the formulated problem in a simple and practical manner, we divide it into two subproblems and

0 5 10 15 20 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

harvested energy / energy usage / electricity price

M P H x*u*(T=2) x*u*(T=3) x*u*(T=4) electricity price harvested energy energy usage (T=2) energy usage (T=4) energy usage (T=3)

Figure 4.4: Effect of Time Window

sequentially solve them via an optimization approach and a control algorithm, respectively. The proposed two-stage optimization/control approach provides a method to manage both transmit power and stored energy usage of HCN BSs to reduce on-grid electricity expendi- ture. Numerical results confirm the effectiveness of the scheme.

With D2D technology, mobile users in proximity can directly communicate to each other without BSs. This can significantly bring down the energy cost. However, interference brought by D2D users has become a challenging problem. In next chapter, we address energy-aware capacity evaluation and optimization for D2D underlay cellular networks.

Chapter 5

Power-aware Performance Analysis

and Optimization in D2D Underlay

Networks

Device-to-device (D2D) technology exploits direct communication between two users within a short range [121] and is regarded as one of the key technologies for 5G wireless commu- nication system. It has great potential to improve both spectral and energy efficiency due to spectral reuse gain and the proximity of communication parties, respectively. However, introducing an underlay of D2D users presents many challenges to the long-standing cellular architecture. One of the main consequences involves new sources of interference: intra-cell and inter-cell interference between cellular users (CUs) and D2D users (DUs), and inter- ference among D2D users. Therefore, it is critical to carefully design system parameters (density of D2D users, cellular BSs, transmit power etc.) to make D2D underlay operation beneficial while guaranteeing the performance of cellular networks.

This chapter provides some unique and novel perspectives in terms of modeling, design and analysis of D2D underlay networks. Our goal is to analyze a realistic D2D network. Unlike the work in [72], we consider a network model, where distances between D2D trans-

mitting user (DTU) and D2D receiving user (DRU) are treated as a random variable. First of all, we seek to address a fundamental open question that is important to address as we transition to 5G wireless systems. Is there a critical set of system parameters (density of D2D users, cellular BSs, transmit power etc.) that can ensure that the benefits of D2D underlay operation can outweigh its drawbacks? In the quest to answer this question for a realistic D2D network model, we also uncover new analytical results. With D2D links characterized as Rician fading channels, we derive, for the first time, upper and lower bounds for ergodic capacity of a cellular network, and recursive closed-form expression and closed-form ap- proximation for ergodic capacity of D2D communication for the case of path loss coefficient 4. These results are shown to better approximate ergodic capacity related to the previous result presented in [72]. Additionally, we identify the D2D user density and transmit power that maximizes global ergodic capacity of the network. Specifically, a two-phase scheme is proposed to optimize ergodic capacity while minimizing overall power consumption. Results from this work provide a framework to uncover desirable system design parameters that of- fer the best gains in terms of ergodic capacity. Secondly, we analyze the average achievable throughput of D2D underlay cellular networks. Closed-form analytical results are provided for average achievable throughput in a D2D underlay cellular network. Finally, we provide closed-form ergodic capacity results (when path loss exponent is 4) for two other cases of the distribution of DRU: (1) distance between a DU pair follows a uniform distribution and (2) a DRU is distributed uniformly in the circular area around its serving DTU.

This chapter includes several distinct sections. Section 5.1 describes the system model. Closed-form results of ergodic capacity are derived in Section 5.2. Section 5.3 discusses optimization of ergodic capacity and total power consumption of a D2D underlay cellular network. Then Section 5.4 analyzes the average achievable throughput of D2D underlay cellular networks. Section 5.5 provides closed-form ergodic capacity results for two other cases of the distribution of DRU. Finally, conclusions and future work are presented in Section 5.6.

5.1

System Model

In this section, the D2D underlay cellular network model is introduced. Then, based on the SIR metric, we provide success probability expressions for both CUs and D2D users.