Successive interference cancellation techniques for LTE downlink

Axnas, Johan; Wang, Y.-P. Eric; Kamuf, Matthias; Andgart, Niklas

doi:10.1109/pimrc.2011.6139817

Cited by 19 publications

(4 citation statements)

References 10 publications

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“…The first approach mitigates the interference using linear filers with the interference models, whereas the latter approach, MUD, involves signal processing in the decoding process. The interference cancellation promises considerable improvement in the system performance and can either be successive interference cancellation (SIC) [21] or parallel interference cancellation (PIC) [22]. The precise channel estimation capability of the interference cancellation has the potential to enhance the channel performance to that of the AWGN (Additive White Gaussian Noise) channel.…”

Section: A Interference Cancellationmentioning

confidence: 99%

A Review of Inter Cell Interference Management in Regular and Irregular Geometry Cellular Networks

Ullah

Khalid

et al. 2021

Jurnal Teknologi

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This paper reviews the inter-cell interference (ICI) mitigation approaches in the OFDMA based multicellular networks with more emphasis on the frequency reused based ICI coordination schemes in the downlink systems. The geometry of the network severely affects the Signal to Interference and Noise Ratio (SINR); therefore, the wireless cellular systems are strongly dependent on the spatial BSs configuration and topology of a network. ICI mitigation techniques for both regular and irregular geometry networks are analyzed and a qualitative comparison along with the future research directions are presented.

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Section: A Interference Cancellationmentioning

confidence: 99%

A Review of Inter Cell Interference Management in Regular and Irregular Geometry Cellular Networks

Ullah

Khalid

et al. 2021

Jurnal Teknologi

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“…where h s,k denotes the channel gain coefficient of fading channel and w s,k denotes the additive white Gaussian noise (AWGN). The users with better channel gain, denoted by h s,k , perform the SIC process ( [37,38]) in order to separate their respective signal, considering the other user signals as interference. For example, assuming that N s = 2 and h 2 s,1 > h 2 s,2 , the first user performs SIC and removes the second user's signal, while the second user considers the first user's signal as noise.…”

Section: Description Of the Noma Scheme With Sicmentioning

confidence: 99%

Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Pliatsios

Sarigiannidis

2019

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The ever-increasing requirement of massive connectivity, due to the rapid deployment of internet of things (IoT) devices, in the emerging 5th generation (5G) mobile networks commands for even higher utilization of the available spectrum. Non-orthogonal multiple access (NOMA) is a promising solution that can effectively accommodate a higher number of users, resulting in increased spectrum utilization. In this work, we aim to maximize the total throughput of a NOMA system, while maintaining a good level of fairness among the users. We propose a three-step method where the first step matches the users to the channels using a heuristic matching algorithm, while the second step utilizes the particle swarm optimization algorithm to allocate the power to each channel. In the third step, the power allocated to each channel is further distributed to the multiplexed users based on their respective channel gains. Based on extensive performance simulations, the proposed method offers notable improvement, e.g., 15% in terms of system throughput and 55% in terms of user fairness.

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“…It requires an extra signal processing and latency [7]. The second approach requires the deployment of multiple antennas UE aiming at reducing the interference effects based on spatial diversity (beamforming) [10,11]. In this case, the receiver can select the best quality signal among the various received signals.…”

Section: Introductionmentioning

confidence: 99%

Interference Mitigation Through Inter-Cell Interference Coordination Using Virtual PRB Allocation in 4G Networks

Hassen

Afif

Tabbane

2016

Wireless Pers Commun

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This paper proposes a distributed and coordinated resource allocation algorithm in multi-cell MIMO-OFDMA systems. Firstly, each cell classifies mobile users into celledge and cell-center users based on a dynamic threshold aiming to avoid throughput loss. Secondly, each cell independently and dynamically allocates physical resource blocks (PRBs) to its cell-center users using a recursive resource allocation algorithm where the effective SINR metric is investigated. After that, each cell aggregates the unused subcarriers by its cell-center users to construct virtual PRBs. These latter are intended to be allocated to cell-edge users. Here, each cell informs its neighboring cells, via messages exchange, about its unused sub-carriers and their corresponding SINRs. If two or more cells share the same unused sub-carrier, a new collision avoidance algorithm is applied to avoid the inter-cell interference. Then, each cell constructs its virtual PRBs based on radio resource exchange between eNodeBs. After virtual PRB allocation, a power allocation procedure is performed and an adaptive modulation and coding algorithm is executed. Simulation results demonstrate that the proposed scheme permits to enhance the system performance in cell-edge and cell-center region compared to well-known algorithms.

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Successive interference cancellation techniques for LTE downlink

Cited by 19 publications

References 10 publications

A Review of Inter Cell Interference Management in Regular and Irregular Geometry Cellular Networks

A Review of Inter Cell Interference Management in Regular and Irregular Geometry Cellular Networks

Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access

Interference Mitigation Through Inter-Cell Interference Coordination Using Virtual PRB Allocation in 4G Networks

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