Joint Precoding and Load Balancing Optimization for Energy-Efficient Heterogeneous Networks

Abstract

Abstract-This paper considers a downlink heterogeneous network, where different types of multiantenna base stations (BSs) communicate with a number of single-antenna users. Multiple BSs can serve the users by spatial multiflow transmission techniques. Assuming imperfect channel state information at both BSs and users, the precoding, load balancing, and BS operation mode are jointly optimized for improving the network energy efficiency. We minimize the weighted total power consumption while satisfying quality-of-service constraints at the users. This problem is nonconvex, but we prove that for each BS mode combination, the considered problem has a hidden convexity structure. Thus, the optimal solution is obtained by an exhaustive search over all possible BS mode combinations. Furthermore, by iterative convex approximations of the nonconvex objective function, a heuristic algorithm is proposed to obtain a suboptimal solution of low complexity. We show that although multicell joint transmission is allowed, in most cases, it is optimal for each user to be served by a single BS. The optimal BS association condition is parameterized, which reveals how it is impacted by different system parameters. Simulation results indicate that putting a BS into sleep mode by proper load balancing is an important solution for energy savings.Index Terms-Energy efficiency, heterogeneous networks, load balancing, precoding design.

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

“…Because 80% of the power in current networks is consumed at the BSs [11], the BS technology needs to be redesigned to reduce the power consumption as the wireless traffic grows. Many researchers have investigated how the physical layer transmissions can be optimized to reduce the transmit power, while maintaining the quality-of-service (QoS); see [11]- [16] and references therein. In particular, the precoding vectors and power allocation were jointly optimized in [12] under perfect channel state information (CSI).…”

“…In contrast, [14] showed that most joint power allocation and BS-user association problems with power constraints are NP-hard. The recent papers [15], [16] consider a relaxed problem formulation where each user can be associated with multiple BSs and show that these problems can be solved by convex optimization.…”

“…The papers [12]- [16] are all optimizing power with respect to the small-scale fading, which is very computationally demanding since the fading coefficients change rapidly (i.e., every few milliseconds). It is also unnecessary to compensate for bad fading realizations by spending a lot of power on having a constant QoS, since it is typically the average QoS that matters to the users.…”

Resource Allocation for Energy Efficiency in 5G Wireless Networks

“…Therefore, optimizing EE performance for downlink transmission in HetNets has attracted a lot of research interest recently and mixed deployment of HetNets has been shown to have a higher EE. To save the HetNets energy, some actice/sleep regimes for conventional HetNets and massive MIMO HetNets were proposed in [3,4,28,51]. It is importantly noted that the maximizing EE performance does not try to minimize the beamformer power since EE merit is a ratio of the network sum-rate and the total power consumption.…”

Adapting Downlink Power in Fronthaul-Constrained Hierarchical Software-Defined RANs

“…Without the fronthaul capacity constraint (9c), the optimal solution can be efficiently obtained [16], [17]. For (14), a brute force approach can be applied to achieve the optimal power adaptation.…”