Demo Data from EDBT 2026 / reviewers in the wild / expert
Ain Ul Aisha
dblp:143/2902
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications
optical wireless communication |
0.5 | 2 | 2017 | 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 |
0.5 | 2 | 2017 | 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 |
0.3 | 1 | 2017 | Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017 |
Physical-layer communications › multiple-antenna systems
MISO |
0.3 | 1 | 2017 | On the Sum-Rate Capacity of Poisson MISO Multiple Access Channels · IEEE Trans. Inf. Theory 2017 |
Physical-layer communications › multiple access
multiple access channel |
0.3 | 1 | 2017 | Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017 |
Physical-layer communications › multiple-antenna systems
multiple-antenna communication |
0.3 | 1 | 2017 | On the Sum-Rate Capacity of Poisson MISO Multiple Access Channels · IEEE Trans. Inf. Theory 2017 |
Physical-layer communications › information theory
sum-rate capacity |
0.3 | 1 | 2017 | Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017 |
Information theory
channel capacity |
0.3 | 1 | 2017 | 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 |
0.3 | 1 | 2017 | 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 |
0.2 | 1 | 2015 | Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015 |
Physical-layer communications › information theory › capacity analysis
ergodic capacity |
0.2 | 1 | 2015 | Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015 |
Physical-layer communications
power allocation |
0.2 | 1 | 2015 | Optimal Power Allocation for Poisson Channels With Time-Varying Background Light · IEEE Trans. Commun. 2015 |
Physical-layer communications
MIMO |
0.1 | 1 | 2017 | Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017 |
Physical-layer communications › MIMO
multiple antennas |
0.1 | 1 | 2017 | Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels · IEEE Trans. Commun. 2017 |
Physical-layer communications
fading channels |
0.1 | 1 | 2015 | 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 |
0.1 | 1 | 2015 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Sum-Rate Capacity of Poisson MIMO Multiple-Access ChannelsabstractIn 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 ChannelsabstractIn 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. Theory | 1 |
| 2016 | On the sum-rate capacity of non-symmetric Poisson multiple access channelabstractIn 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 |
ISIT | 1 |
| 2015 | Interference Performance Evaluation of Secondary Users in Cognitive Radio NetworksabstractIn 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 Spring | 1 |
| 2015 | Optimal Power Allocation for Poisson Channels With Time-Varying Background LightabstractIn 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 |