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Register a new userUAV-Enabled Uplink Non-Orthogonal Multiple Access System: Joint Deployment and Power Control
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Added by
Yuntian Wang
Publication date
2019
fields
Electronic Engineering
and research's language is
English
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In order to overcome the inherent latency in multi-user unmanned aerial vehicle (UAV) networks with orthogonal multiple access (OMA). In this paper, we investigate the UAV enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by ground users. In order to maximize the sum rate of all users and to meet the quality of service (QoS) requirement, we formulate an optimization problem, in which the UAV deployment position and the power control are jointly optimized. This problem is non-convex and some variables are binary, and thus it is a typical NP hard problem. In this paper, an iterative algorithm is proposed with the assistance of successive convex approximate (SCA) technique and the penalty function method. In order to reduce the high computational complexity of the iterative algorithm, a low complexity approximation algorithm is then proposed, which can achieve a similar performance compared to the iterative algorithm. Compared with OMA scheme and conventional NOMA scheme, numerical results show that our proposed algorithms can efficiently improve the sum rate.
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In this paper, we investigate the combination of two key enabling technologies for the fifth generation (5G) wireless mobile communication, namely millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA). In particular, we consider a typical 2-user uplink mmWave-NOMA system, where the base station (BS) equips an analog beamforming structure with a single RF chain and serves 2 NOMA users. An optimization problem is formulated to maximize the achievable sum rate of the 2 users while ensuring a minimal rate constraint for each user. The problem turns to be a joint power control and beamforming problem, i.e., we need to find the beamforming vectors to steer to the two users simultaneously subject to an analog beamforming structure, and meanwhile control appropriate power on them. As direct search for the optimal solution of the non-convex problem is too complicated, we propose to decompose the original problem into two sub-problems that are relatively easy to solve: one is a power control and beam gain allocation problem, and the other is an analog beamforming problem under a constant-modulus constraint. The rational of the proposed solution is verified by extensive simulations, and the performance evaluation results show that the proposed sub-optimal solution achieve a close-to-bound uplink sum-rate performance.
This article proposes a novel framework for unmaned aerial vehicle (UAV) networks with massive access capability supported by non-orthogonal multiple access (NOMA). In order to better understand NOMA enabled UAV networks, three case studies are carried out. We first provide performance evaluation of NOMA enabled UAV networks by adopting stochastic geometry to model the positions of UAVs and ground users. Then we investigate the joint trajectory design and power allocation for static NOMA users based on a simplified two-dimensional (2D) model that UAV is flying around at fixed height. As a further advance, we demonstrate the UAV placement issue with the aid of machine learning techniques when the ground users are roaming and the UAVs are capable of adjusting their positions in three-dimensions (3D) accordingly. With these case studies, we can comprehensively understand the UAV systems from fundamental theory to practical implementation.
The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging the time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.
This paper investigates practical 5G strategies for power-balanced non-orthogonal multiple access (NOMA). By allowing multiple users to share the same time and frequency, NOMA can scale up the number of served users and increase spectral efficiency compared with existing orthogonal multiple access (OMA). Conventional NOMA schemes with successive interference cancellation (SIC) do not work well when users with comparable received powers transmit together. To allow power-balanced NOMA (more exactly, near power-balanced NOMA), this paper investigates a new NOMA architecture, named Network-Coded Multiple Access (NCMA). A distinguishing feature of NCMA is the joint use of physical-layer network coding (PNC) and multiuser decoding (MUD) to boost NOMA throughputs. We first show that a simple NCMA architecture in which all users use the same modulation, referred to as rate-homogeneous NCMA, can achieve substantial throughput improvement over SIC-based NOMA under near power-balanced scenarios. Then, we put forth a new NCMA architecture, referred to as rate-diverse NCMA, in which different users may adopt different modulations commensurate with their relative SNRs. A challenge for rate-diverse NCMA is the design of a channel-coded PNC system. This paper is the first attempt to design channel-coded rate-diverse PNC. Experimental results on our software-defined radio prototype show that the throughput of rate-diverse NCMA can outperform the state-of-the-art rate-homogeneous NCMA by 80%. Overall, rate-diverse NCMA is a practical solution for near power-balanced NOMA.
Non-orthogonal multiple access (NOMA) is envisioned to be one of the most beneficial technologies for next generation wireless networks due to its enhanced performance compared to other conventional radio access techniques. Although the principle of NOMA allows multiple users to use the same frequency resource, due to decoding complication, information of users in practical systems cannot be decoded successfully if many of them use the same channel. Consequently, assigned spectrum of a system needs to be split into multiple subchannels in order to multiplex that among many users. Uplink resource allocation for such systems is more complicated compared to the downlink ones due to the individual users power constraints and discrete nature of subchannel assignment. In this paper, we propose an uplink subchannel and power allocation scheme for such systems. Due to the NP-hard and non-convex nature of the problem, the complete solution, that optimizes both subchannel assignment and power allocation jointly, is intractable. Consequently, we solve the problem in two steps. First, based on the assumption that the maximal power level of a user is subdivided equally among its allocated subchannels, we apply many-to-many matching model to solve the subchannel-user mapping problem. Then, in order to enhance the performance of the system further, we apply iterative water-filling and geometric programming two power allocation techniques to allocate power in each allocated subchannel-user slot optimally. Extensive simulation has been conducted to verify the effectiveness of the proposed scheme. The results demonstrate that the proposed scheme always outperforms all existing works in this context under all possible scenarios.
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