Effective Capacity in Multiple Access Channels with Arbitrary Inputs

In this paper, we consider a two-user multiple access fading channel under quality-of-service (QoS) constraints. We initially formulate the transmission rates for both transmitters, where the transmitters have arbitrarily distributed input signals. We assume that the receiver performs successive decoding with a certain order. Then, we establish the effective capacity region that provides the maximum allowable sustainable arrival rate region at the transmitters' buffers under QoS guarantees. Assuming limited transmission power budgets at the transmitters, we attain the power allocation policies that maximize the effective capacity region. As for the decoding order at the receiver, we characterize the optimal decoding order regions in the plane of channel fading parameters for given power allocation policies. In order to accomplish the aforementioned objectives, we make use of the relationship between the minimum mean square error and the first derivative of the mutual information with respect to the power allocation policies. Through numerical results, we display the impact of input signal distributions on the effective capacity region performance of this two-user multiple access fading channel

Performance Analysis of Energy-Detection-Based Massive SIMO

Recently, communications systems that are both energy efficient and reliable are under investigation. In this paper, we concentrate on an energy-detection-based transmission scheme where a communication scenario between a transmitter with one antenna and a receiver with significantly many antennas is considered. We assume that the receiver initially calculates the average energy across all antennas, and then decodes the transmitted data by exploiting the average energy level. Then, we calculate the average symbol error probability by means of a maximum a-posteriori probability detector at the receiver. Following that, we provide the optimal decision regions. Furthermore, we develop an iterative algorithm that reaches the optimal constellation diagram under a given average transmit power constraint. Through numerical analysis, we explore the system performance

Effective Capacity in Cognitive Radio Broadcast Channels

In this paper, we investigate effective capacity by modeling a cognitive radio broadcast channel with one secondary transmitter (ST) and two secondary receivers (SRs) under quality-of-service constraints and interference power limitations. We initially describe three different cooperative channel sensing strategies with different hard-decision combining algorithms at the ST, namely OR, Majority, and AND rules. Since the channel sensing occurs with possible errors, we consider a combined interference power constraint by which the transmission power of the secondary users (SUs) is bounded when the channel is sensed as both busy and idle. Furthermore, regarding the channel sensing decision and its correctness, there exist possibly four different transmission scenarios. We provide the instantaneous ergodic capacities of the channel between the ST and each SR in all of these scenarios. Granting that transmission outage arises when the instantaneous transmission rate is greater than the instantaneous ergodic capacity, we establish two different transmission rate policies for the SUs when the channel is sensed as idle. One of these policies features a greedy approach disregarding a possible transmission outage, and the other favors a precautious manner to prevent this outage. Subsequently, we determine the effective capacity region of this channel model, and we attain the power allocation policies that maximize this region. Finally, we present the numerical results. We first show the superiority of Majority rule when the channel sensing results are good. Then, we illustrate that a greedy transmission rate approach is more beneficial for the SUs under strict interference power constraints, whereas sending with lower rates will be more advantageous under loose interference constraints.Comment: Submitted and Accepted to IEEE Globecom 201

Design of a Cognitive VLC Network with Illumination and Handover Requirements

In this paper, we consider a cognitive indoor visible light communications (VLC) system, comprised of multiple access points serving primary and secondary users through the orthogonal frequency division multiple access method. A cognitive lighting cell is divided into two non-overlapping regions that distinguish the primary and secondary users based on the region they are located in. Under the assumption of equal-power allocation among subcarriers, each region is defined in terms of its physical area and the number of allocated subcarriers within that region. In this paper, we provide the lighting cell design with cognitive constraints that guarantee fulfilling certain illumination, user mobility, and handover requirements in each cell. We further argue that, under some conditions, a careful assignment of the subcarriers in each region can mitigate the co-channel interference in the overlapping areas of adjacent cells. Numerical results depict the influence of different system parameters, such as user density, on defining both regions. Finally, a realistic example is implemented to assess the performance of the proposed scheme via Monte Carlo simulations

Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation

This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal Component Analysis and Naive Bayes algorithms. The presented system is able to classify patients and control subjects with an accuracy of up to 100\%. The outcomes can be saved on the device or transmitted to treating physicians for later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed autonomous quality metric solution bears great potential to be used and deployed to support digital healthcare and therapy.Comment: 5 Page

Measurement of head-related transfer functions : A review

A head-related transfer function (HRTF) describes an acoustic transfer function between a point sound source in the free-field and a defined position in the listener's ear canal, and plays an essential role in creating immersive virtual acoustic environments (VAEs) reproduced over headphones or loudspeakers. HRTFs are highly individual, and depend on directions and distances (near-field HRTFs). However, the measurement of high-density HRTF datasets is usually time-consuming, especially for human subjects. Over the years, various novel measurement setups and methods have been proposed for the fast acquisition of individual HRTFs while maintaining high measurement accuracy. This review paper provides an overview of various HRTF measurement systems and some insights into trends in individual HRTF measurements