Successive interference cancellation in downlink heterogeneous cellular networks

Zhang, Xinchen; Haenggi, Martin

doi:10.1109/glocomw.2013.6825075

Cited by 11 publications

(1 citation statement)

References 13 publications

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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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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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Performance analysis of partial interference cancellation in multi-antenna UDNs

Atzeni

Kountouris

2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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Abstract-The employment of partial zero-forcing (PZF) receivers at the base stations represents an efficient and lowcomplexity technique for uplink interference management in cellular networks. In this paper, we focus on the performance analysis of ultra-dense networks (UDNs) in which the multiantenna receivers adopt PZF. We provide both integral expressions and tight closed-form approximations for the probability of successful transmission, which can be used to accurately evaluate the optimal tradeoff between interference cancellation and array gain. Numerical results show that no more than half of the available degrees of freedom should be used for interference cancellation.

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Power Allocation in Downlink Non-orthogonal Multiple Access IoT-enabled Systems: A Particle Swarm Optimization Approach

Pliatsios

Sarigiannidis

2019

2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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Successive interference cancellation in downlink heterogeneous cellular networks

Cited by 11 publications

References 13 publications

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

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

Performance analysis of partial interference cancellation in multi-antenna UDNs

Power Allocation in Downlink Non-orthogonal Multiple Access IoT-enabled Systems: A Particle Swarm Optimization Approach

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