[Paper Review] Optimization of the Energy-Efficient Relay-Based massive IoT Network

Tiejun Lv, Zhipeng Lin+2 more|arXiv (Cornell University)|May 31, 2018

Advanced MIMO Systems Optimization28 references3 citations

TL;DR

This paper proposes a low-complexity energy-efficient resource allocation strategy for a massive IoT network using a multi-pair decode-and-forward relay with massive MIMO. By deriving closed-form and integral-based expressions for energy efficiency (EE), it jointly optimizes the number of relay antennas, relay transmit power, and active device pair density under QoS constraints, achieving near-optimal EE with significant gains over conventional schemes.

ABSTRACT

To meet the requirements of high energy efficiency (EE) and large system capacity for the fifth-generation (5G) Internet of Things (IoT), the use of massive multiple-input multipleoutput (MIMO) technology has been launched in the massive IoT (mIoT) network, where a large number of devices are connected and scheduled simultaneously. This paper considers the energyefficient design of a multi-pair decode-and-forward relay-based IoT network, in which multiple sources simultaneously transmit their information to the corresponding destinations via a relay equipped with a large array. In order to obtain an accurate yet tractable expression of the EE, firstly, a closed-form expression of the EE is derived under an idealized simplifying assumption, in which the location of each device is known by the network. Then, an exact integral-based expression of the EE is derived under the assumption that the devices are randomly scattered following a uniform distribution and transmit power of the relay is equally shared among the destination devices. Furthermore, a simple yet efficient lower bound of the EE is obtained. Based on this, finally, a low-complexity energy-efficient resource allocation strategy of the mIoT network is proposed under the specific qualityof- service (QoS) constraint. The proposed strategy determines the near-optimal number of relay antennas, the near-optimal transmit power at the relay and near-optimal density of active mIoT device pairs in a given coverage area. Numerical results demonstrate the accuracy of the performance analysis and the efficiency of the proposed algorithms.

Motivation & Objective

• To address the critical challenge of achieving high energy efficiency (EE) and large system capacity in 5G-enabled massive IoT (mIoT) networks.
• To close the research gap in energy-efficient design for relay-based massive MIMO mIoT systems, where prior work has largely focused on spectral efficiency.
• To develop a tractable yet accurate EE expression under realistic device distribution and power sharing models.
• To propose a low-complexity resource allocation strategy that jointly optimizes relay antennas, transmit power, and active device density for near-optimal EE under QoS constraints.

Proposed method

• Derives a closed-form EE expression under the idealized assumption of known device locations for tractability and accuracy.
• Develops an exact integral-based EE expression assuming uniformly random device distribution and equal relay power sharing among destinations.
• Establishes a simple yet effective lower bound on EE using Jensen’s inequality and convexity of the logarithmic function.
• Proposes a low-complexity optimization framework that jointly determines the near-optimal number of relay antennas, relay transmit power, and active mIoT device pair density.
• Uses the derived EE lower bound as a surrogate objective to enable efficient numerical optimization under QoS constraints.
• Validates the accuracy of the EE expressions and the efficiency of the proposed strategy through numerical results.

Experimental results

Research questions

• RQ1 How can energy efficiency be accurately modeled in a relay-based massive MIMO mIoT network with randomly distributed devices?
• RQ2 What is the impact of device location randomness and equal relay power allocation on the achievable EE?
• RQ3 Can a tight and tractable lower bound on EE be derived to enable low-complexity optimization?
• RQ4 How does the joint optimization of relay antennas, transmit power, and active device density affect EE under QoS constraints?
• RQ5 To what extent does the proposed strategy outperform conventional resource allocation in terms of EE and system performance?

Key findings

• The proposed EE lower bound closely approximates the true EE, enabling reliable optimization with low computational complexity.
• The joint optimization of relay antennas, transmit power, and active device pair density achieves near-optimal energy efficiency.
• Numerical results confirm the accuracy of the derived EE expressions and the effectiveness of the proposed algorithm.
• The system achieves significant EE gains by dynamically adapting the number of antennas and power based on device density.
• The proposed strategy maintains QoS requirements while minimizing total network power consumption.
• The method demonstrates robustness and scalability in high-density mIoT scenarios, such as urban smart city deployments.