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Ain Ul Aisha

dblp:143/2902 · DBLP profile ↗
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5ranked-venue papers
5as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 first-authorTheory of computation · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
3 papers
Physical-layer communications · 100%
Theoretical computer science
1 paper
Information theory · 100%

Topics — the 16 heaviest of 16, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications
optical wireless communication
0.522017
Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017
Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015
Physical-layer communications › channel modeling › stochastic channel model
poisson channel
0.522017
Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017
Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015
Physical-layer communications › MIMO › multiuser MIMO
MIMO multiple-access channel
0.312017
Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017
Physical-layer communications › multiple-antenna systems
MISO
0.312017
On the Sum-Rate Capacity of Poisson MISO Multiple Access Channels · IEEE Trans. Inf. Theory 2017
Physical-layer communications › multiple access
multiple access channel
0.312017
Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017
Physical-layer communications › multiple-antenna systems
multiple-antenna communication
0.312017
On the Sum-Rate Capacity of Poisson MISO Multiple Access Channels · IEEE Trans. Inf. Theory 2017
Physical-layer communications › information theory
sum-rate capacity
0.312017
Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017
Information theory
channel capacity
0.312017
On the Sum-Rate Capacity of Poisson MISO Multiple Access Channels · IEEE Trans. Inf. Theory 2017
Information theory › network information theory › multiuser capacity
multiple-access channel capacity
0.312017
On the Sum-Rate Capacity of Poisson MISO Multiple Access Channels · IEEE Trans. Inf. Theory 2017
Physical-layer communications › information theory › capacity analysis
channel capacity
0.212015
Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015
Physical-layer communications › information theory › capacity analysis
ergodic capacity
0.212015
Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015
Physical-layer communications
power allocation
0.212015
Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015
Physical-layer communications
MIMO
0.112017
Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017
Physical-layer communications › MIMO
multiple antennas
0.112017
Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017
Physical-layer communications
fading channels
0.112015
Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015
Physical-layer communications › channel modeling › fading channel modeling
poisson fading channel
0.112015
Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015

Methods — techniques the papers use, named apart from their topics

non-convex optimization · 0.9convex optimization · 0.8channel transformation · 0.3outage probability analysis · 0.2
YearPublicationVenuePosition
2017 Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels
abstract
In this paper, we analyze the sum-rate capacity of two-user Poisson multiple input multiple output multiple-access channels (MACs), when both the transmitters and the receiver are equipped with multiple antennas. Although the sum-rate capacity of Poisson MISO MAC when the receiver is equipped with a single antenna has been characterized by us, the inclusion of multiple antennas at the receiver makes the problem more challenging and requires the development of new analytical tools. We first characterize the sum-rate capacity of the Poisson MAC when each transmitter has a single antenna and the receiver has multiple antennas. We obtain the optimal input that achieves the sum-rate capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-rate capacity, and for certain channel parameters, it is optimal for both users to transmit. We then characterize the sum-rate capacity of the channel where both the transmitters and the receiver are equipped with multiple antennas. We show that the sum-rate capacity of the Poisson MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter, and has thus been characterized by the former case. We show this by developing a novel channel transformation argument.
Ain Ul Aisha, Lifeng Lai, Yingbin Liang, Shlomo Shamai
IEEE Trans. Commun.1
2017 On the Sum-Rate Capacity of Poisson MISO Multiple Access Channels
abstract
In this paper, we analyze the sum-rate capacity of two-user Poisson multiple access channels (MAC), when the receiver is equipped with single antenna. We first characterize the sum-rate capacity of the non-symmetric Poisson MAC when each transmitter has a single antenna. While the sum-rate capacity of the symmetric Poisson MAC with single antenna at each transmitter has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-rate capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-rate capacity. This is in sharp contrast to the Gaussian MAC, in which both users must transmit, either simultaneously or at different times, in order to achieve the sum-rate capacity. We then characterize the sum-rate capacity of the Poisson multiple-input single-output (MISO) MAC with multiple antennas at each transmitter and single antenna at the receiver. By converting a non-convex optimization problem with a large number of variables into a non-convex optimization problem with two variables, we show that the sum-rate capacity of the Poisson MISO MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter.
Ain Ul Aisha, Lifeng Lai, Yingbin Liang, Shlomo Shamai
IEEE Trans. Inf. Theory1
2016 On the sum-rate capacity of non-symmetric Poisson multiple access channel
abstract
In this paper, we characterize the sum-rate capacity of the non-symmetric Poisson multiple access channel (MAC). While the sum-rate capacity of the symmetric Poisson MAC has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-rate capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-rate capacity. This is in sharp contrast to the Gaussian MAC, in which all users must transmit, either simultaneously or at different times, in order to achieve the sum-rate capacity.
Ain Ul Aisha, Yingbin Liang, Lifeng Lai, Shlomo Shamai
ISIT1
2015 Interference Performance Evaluation of Secondary Users in Cognitive Radio Networks
abstract
In this paper, we analyze the probability of error for secondary users employed in a cognitive radio network operating within the vicinity of several primary users. Secondary( unlicensed) users must coexist with primary(licensed) users, ensuring that the legacy rights of the primary user transmissions are respected. Consequently, the secondary users must either sense the channel periodically, which has some detection delay, or must check a spectrum availability database, which may not be updated in real-time. Hence, there will be times when the primary user and the secondary user will occupy the same band and interfere with each other. The aim of this paper is to quantify the impact of the probability of error of the secondary user when it does not react quickly enough to the presence of a primary user. Several numerical examples are provided in order to illustrate the analytical results.
Ain Ul Aisha, Nikita Mayekar, Alexander M. Wyglinski
VTC Spring1
2015 Optimal Power Allocation for Poisson Channels With Time-Varying Background Light
abstract
In this paper, we study Poisson fading channels with time-varying background light. Different from most of the existing work on fading Poisson channel that focus on the case with time-varying channel gain, our model is motivated by indoor optical wireless communication systems, in which the noise level is affected by the strength of the background light. We study both the single-input single-output and the multiple-input and multiple-output channels. For each channel, we consider scenarios with and without delay constraints. For the case without a delay constraint, we characterize the optimal power allocation scheme that maximizes the ergodic capacity. For the case with a strict delay constraint, we characterize the optimal power allocation scheme that minimizes the outage probability. We also provide several numerical examples to demonstrate the analytic results.
Ain Ul Aisha, Lifeng Lai, Yingbin Liang
IEEE Trans. Commun.1