Opportunistic Interference Alignment for Random Access Networks

Energy-Efficient Opportunistic Interference Alignment

We introduce an energy-efficient distributed opportunistic interference alignment (OIA) scheme that greatly improves the sum-rates in multiple-cell uplink networks while reducing the transmit power consumption compared to the conventional OIA scheme. In the proposed scheme, each user employs optimal transmit vector design and power control strategy in the sense of minimizing the amount of gener...

Cache-enabled Wireless Networks with Opportunistic Interference Alignment

Both caching and interference alignment (IA) are promising techniques for future wireless networks. Nevertheless, most of existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is fo...

Opportunistic Two - Way Protocols for Random Access Networks

Iterative Transceiver Design for Opportunistic Interference Alignment in MIMO Interfering Multiple-Access Channels

—In this paper, we propose an iterative transceiver design algorithm for opportunistic interference alignment in MIMO interfering multiple-access channels. The proposed algorithm iteratively optimizes the transmit beamforming vectors, the receive matrices and the user selection set, which digs into the multiuser diversity gain. Specifically, data transmission can be operated in each iteration ...

Opportunistic Random Access with Temporal Fairness in Wireless Networks

User diversity arises when wireless users experience diverse channel conditions. This paper proposes three opportunistic random access algorithms that favor users with the best channel conditions in channel access. These algorithms operate on the contention mechanism of CSMA/CA and compute the contention window based on channel conditions. In one algorithm, the contention windows of all users s...