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This paper investigates the impact of limited feedback on the uplink of a wireless network with multiple access points. The network adopts universal frequency re-use with each access point serving a set of users using orthogonal signalling. All wireless nodes are multiple-input-multiple-output-capable. Block Diagonalization, a popular technique for interference cancellation, is performed at each transmitter...
Machine learning algorithms have recently attracted much interest for effective link adaptation due to their flexibility and ability to capture more environmental effects implicitly than classical adaptation algorithms. However, past applications are limited to rather simple configurations such as identifying channel condition or link adaptation in fixed or slowly varying channels. Recently, more...
In MIMO-OFDM systems, by matching transmitter parameters such as modulation order and coding rate, link adaptation can increase the throughput significantly. However, creating a tractable mathematical mapping model from environmental variables to transmitter parameters that allows the latter to be optimized in any sense, presents serious challenges due to the large number of variables involved, as...
This paper investigates the impact of limited feedback on user throughput in the uplink of a cellular system. We consider scenarios where the base-station has limited feedback resources, which it needs to allocate across the users it serves. We propose a general model that captures the effect of feedback allocation on the achievable rates for a user, which allows us to characterize the rate region...
MIMO-OFDM wireless systems require adaptive modulation and coding based on channel state information (CSI) to maximize throughput in changing wireless channels. Traditional adaptive modulation and coding attempts to predict the best rate available by estimating the packet error rate for each modulation and coding scheme (MCS) by using CSI, which has shown to be challenging. This paper considers supervised...
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