Secure and Green SWIPT in Distributed Antenna Networks with Limited Backhaul Capacity

Abstract: This paper studies the resource allocation algorithm design for secure information and renewable green energy transfer to mobile receivers in distributed antenna communication systems. In particular, distributed remote radio heads (RRHs/antennas) are connected to a central processor (CP) via capacity-limited backhaul links to facilitate joint transmission. The RRHs and the CP are equipped with renewable energy harvesters and share their energies via a lossy micropower grid for improving the efficiency in conveying information and green energy to mobile receivers via radio frequency (RF) signals. The considered resource allocation algorithm design is formulated as a mixed non-convex and combinatorial optimization problem taking into account the limited backhaul capacity and the quality of service requirements for simultaneous wireless information and power transfer (SWIPT). We aim at minimizing the total network transmit power when only imperfect channel state information of the wireless energy harvesting receivers, which have to be powered by the wireless network, is available at the CP. In light of the intractability of the problem, we reformulate it as an optimization problem with binary selection, which facilitates the design of an iterative resource allocation algorithm to solve the problem optimally using the generalized Bender's decomposition (GBD). Furthermore, a suboptimal algorithm is proposed to strike a balance between computational complexity and system performance. Simulation results illustrate that the proposed GBD based algorithm obtains the global optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. Besides, the distributed antenna network for SWIPT with renewable energy sharing is shown to require a lower transmit power compared to a traditional system with multiple co-located antennas.

• The paper proposes resource allocation algorithms for secure and green SWIPT in distributed antenna networks with limited backhaul and renewable energy.
• It introduces a Generalized Bender's Decomposition (GBD) algorithm for optimal solutions and a low-complexity suboptimal algorithm using d.c. programming.
• Simulation results show significant power savings with the distributed antenna setup and renewable energy sharing compared to traditional systems.

Overview of Secure and Green SWIPT in Distributed Antenna Networks with Limited Backhaul Capacity

The paper presented by Derrick Wing Kwan Ng and Robert Schober examines the challenging problem of resource allocation in distributed antenna networks where the aim is to simultaneously facilitate secure information and efficient power transfer, while also conserving energy. This is achieved through the mechanism of Simultaneous Wireless Information and Power Transfer (SWIPT) in the presence of constraints imposed by limited backhaul capacity, which necessitates advanced algorithmic solutions for effective operation.

Problem Statement and System Model

The core problem addressed revolves around designing a resource allocation algorithm for distributed Antenna Networks involving Remote Radio Heads (RRHs), which are constrained by capacity-limited backhaul links. These RRHs, along with a central processor, are equipped with renewable energy harvesters. The study’s innovation lies in considering the simultaneous transfer of information and power in such a setup where energy can be shared using a micropower grid. The crux of this formulation involves minimizing the total network transmit power while maintaining quality of service (QoS) for secure communication, even when relying on imperfect Channel State Information (CSI).

Methodology and Techniques

The paper addresses the non-convex and combinatorial nature of the problem by reformulating it into an optimization problem that involves binary selection criteria. A primary contribution of the study is the development of two algorithms:

1. Generalized Bender's Decomposition (GBD) Based Algorithm: This approach optimally solves the problem by iterating between a primal problem (finding continuous variables with fixed binary selections) and a master problem (adjusting binary variables based on the primal solution). This manipulation takes advantage of the GBD theory to facilitate convergence to a globally optimal solution.
2. Suboptimal Algorithm: The authors also propose a low-complexity alternative leveraging the difference of convex functions (d.c.) programming, which balances computation complexity against optimal performance loss, achieving near-optimal solutions efficiently.

Results and Implications

The simulation results demonstrate that the proposed GBD-based algorithm reaches the global optimal solution, and the suboptimal algorithm remarkably approaches this performance with reduced computational requirements. It is marked that the distributed antenna setup, which includes renewable energy sharing via the micropower grid, showcases significant power savings over traditional systems with centralized, co-located antennas. This illustrates not only practical efficiency but also underlies theoretical advancements in tackling such non-convex optimization challenges.

Future Directions

Potential expansions of this study include dynamic adaptations in the resource allocation scheme to account for more volatile energy harvesting environments or integration with larger scale smart grid infrastructures. Moreover, extensions to handle additional scenarios involving passive eavesdroppers or enhanced security protocols offer promising research avenues. These continuous improvements and incorporations aim at more sustainable, secure, and cost-effective wireless communication networks, pivotal to meeting future technological demands.

In conclusion, this paper contributes to the fields of energy-efficient wireless networks and SWIPT, proposing innovative algorithmic solutions that are capable of sustaining secure and green communication in an increasingly complex operational landscape.