Energy Efficiency Of Communication Systems Research Articles

Optimizing Secrecy Energy Efficiency in RIS-assisted MISO systems using Deep Reinforcement Learning

This article investigates the maximization of secrecy energy efficiency (SEE) in B5G mobile systems where a suite of reconfigurable intelligent surface (RIS) modules is incorporated. Taking into account the location information of legitimate users and eavesdroppers, we formulate the problem as a joint optimization of the phase shifts, physical orientations, and locations of the RIS modules, as well as resource allocation at the base station (BS). The problem is then solved by leveraging a deep reinforcement learning (DRL) approach proposed in this paper. The case study results demonstrate the effectiveness of the proposed scheme in improving the secrecy energy efficiency of communication systems using RIS.

ПРИМЕНЕНИЕ ТЕХНОЛОГИИ MIMO В СОВРЕМЕННЫХ СИСТЕМАХ БЕСПРОВОДНОЙ СВЯЗИ РАЗНЫХ ПОКОЛЕНИЙ

Multiple Input Multiple Output (MIMO) technology is widely used in modern IEEE radio access systems. There is a tendency to increase the number of antennas, which is also confirmed by the development of MIMO technology in mobile communication systems of 3GPP standards. Requirements for modern radio communication systems are constantly increasing. As the radio frequency spectrum becomes increasingly scarce, it becomes increasingly difficult to transmit large amounts of information by expanding the frequency channel bandwidth. Therefore, the use of MIMO technology to increase the spectral and energy efficiency of communication systems is relevant. In 5G systems, Massive MIMO technology is used, when using which the number of antennas is measured in tens and hundreds. The characteristics of various versions of MIMO technology implemented in the existing standards 802.11n, 802.11ac, 802.11ax, as well as in the promising standard 802.11be (6G systems) are described in detail. Technologies of directional transmission, spatial multiplexing, selection of antennas as particular cases of precoding are considered. Trends of MIMO technology development in wireless communication systems are shown.

Optimizing Resource Allocation in the Short Blocklength Regime for Ultra-Reliable and Low-Latency Communications

In this paper, we aim to find the global optimal resource allocation for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short. The achievable rate in the short blocklength regime is neither convex nor concave in bandwidth and transmit power. Thus, a non-convex constraint is inevitable in optimizing resource allocation for URLLC. We first consider a general resource allocation problem with constraints on the transmission delay and decoding error probability, and prove that a global optimal solution can be found in a convex subset of the original feasible region. Then, we illustrate how to find the global optimal solution for an example problem, where the energy efficiency (EE) is maximized by optimizing antenna configuration, bandwidth allocation, and power control under the latency and reliability constraints. To improve the battery life of devices and EE of communication systems, both uplink and downlink resources are optimized. The simulation and numerical results validate the analysis and show that the circuit power is dominated by the total power consumption when the average inter-arrival time between packets is much larger than the required delay bound. Therefore, optimizing antenna configuration and bandwidth allocation without power control leads to minor EE loss.

Maximizing Energy Efficiency of Cognitive Wireless Sensor Networks With Constrained Age of Information

A cognitive wireless sensor network is considered, where a cluster of secondary sensors utilize channel vacancies of a primary network and transmit their measurement samples to a common sink node. We propose a joint framing and scheduling policy that optimizes energy efficiency of communication system under strict constraints on the expected age of information. The age of information is defined as the timespan from the sampling epoch to the successful delivery of the samples to the sink node including framing time, queuing time, waiting time for channel vacancies, and transmission time. First, we develop a number-based framing policy to determine the number of samples bundled into data packets with constant header sizes. Then, we quantify the impact of this policy on the age of information and communication energy efficiency by characterizing the utilized queuing dynamics, packet discard rate, and retransmission probability. The derived closed-form expressions for the age of information and energy efficiency are used to regularize packet lengths based on the current sampling rate, channel quality, and channel utilization rate by primary users. The proposed method can be used to develop low-cost and energy-efficient network of unlicensed sensors for delay sensitive applications such as body area sensor networks.

Sparsity Enhancement for Sparse Channel Estimation Using Non-orthogonal Basis

Compressed sensing framework can be employed to improve channel estimation and enhance spectral and/or energy efficiency for communication systems. To take advantage of compressed sensing, special basis are required to model a multipath fading channel with sparse channel coefficients. To apply compressed sensing method a set of orthogonal basis are required, where the coefficients meet the sparsity criteria. However, the orthogonality condition for the basis may be a limiting factor to improve the sparsity of the channel coefficients. In this paper we relax the orthogonality condition and use a dictionary learning algorithm such as K-SVD to find a set of new basis with sparse coefficients. Using this method results in a channel model with improved sparsity and better MSE performance for channel estimation. An OFDM system is considered over a sparse doubly-selective channel and the proposed method is investigated through simulation. The simulation results show that the enhanced channel sparsity achieved based on the proposed method, leads to improved channel estimation performance.