{"id":166049,"date":"2012-04-01T00:00:00","date_gmt":"2012-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/compressive-broadcast-in-mimo-systems-with-receive-antenna-heterogeneity\/"},"modified":"2019-08-22T18:37:03","modified_gmt":"2019-08-23T01:37:03","slug":"compressive-broadcast-in-mimo-systems-with-receive-antenna-heterogeneity","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/compressive-broadcast-in-mimo-systems-with-receive-antenna-heterogeneity\/","title":{"rendered":"Compressive broadcast in MIMO systems with receive antenna heterogeneity"},"content":{"rendered":"

The key challenge in a broadcast system is receiver heterogeneity, where the weakest receiver typically constrains the entire system performance. Traditionally, this arises from heterogeneous channel SNRs at different receivers. In Multiple-Input Multiple-Output (MIMO) systems, a further heterogeneity is caused by different antenna numbers across receivers. We propose a compressive broadcast framework to address both types of heterogeneity. By layering compressive sensing (CS) over MIMO transmissions, our framework ensures a received source quality commensurate with the channel SNR and the MIMO channel dimension. Compared with a conventional framework, our framework achieves more smooth rate increase with channel SNR and much higher performance for multi-antenna receivers.<\/p>\n","protected":false},"excerpt":{"rendered":"

The key challenge in a broadcast system is receiver heterogeneity, where the weakest receiver typically constrains the entire system performance. Traditionally, this arises from heterogeneous channel SNRs at different receivers. In Multiple-Input Multiple-Output (MIMO) systems, a further heterogeneity is caused by different antenna numbers across receivers. We propose a compressive broadcast framework to address both 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