Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work 1 ,  consider the joint precoding across K distant transmitters (TXs) towards K single-antenna receivers (RXs). In practical networks, cooperation beten TXs is limited by the constraints on the backhaul network and the common approach to limit the backhaul overhead is to form small disjoint clusters of cooperating TXs. Yet, this limits the performance due to interference at the cluster edge.  overcome this problem by directly optimizing the allocation of the user's data symbol without clustering but solely subject to a constraint on the total number of symbols allocated. Since the problem of optimal data symbol allocation is of combinatorial nature,  use a greedy approach and develop greedy algorithms having low complexity while incuring only small losses compared to the optimal data symbol allocation. Moreover, the algorithms are shown to be Multiplexing Gain (MG) optimal in many settings. Simulations results confirm that our approach outperforms dynamic clustering methods from the literature.
['Rajeev Gangula', 'Paul de Kerret', 'David Gesbert', 'Maha Al Odeh']
I am writing a research paper on a idea Optimized data symbol allocation in multicell MIMO channels
[{"from": "human", "value": "I am writing a research paper on a idea Optimized data symbol allocation in multicell MIMO channels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work 1 ,  consider the joint precoding across K distant transmitters (TXs) towards K single-antenna receivers (RXs). In practical networks, cooperation beten TXs is limited by the constraints on the backhaul network and the common approach to limit the backhaul overhead is to form small disjoint clusters of cooperating TXs. Yet, this limits the performance due to interference at the cluster edge.  overcome this problem by directly optimizing the allocation of the user's data symbol without clustering but solely subject to a constraint on the total number of symbols allocated. Since the problem of optimal data symbol allocation is of combinatorial nature,  use a greedy approach and develop greedy algorithms having low complexity while incuring only small losses compared to the optimal data symbol allocation. Moreover, the algorithms are shown to be Multiplexing Gain (MG) optimal in many settings. Simulations results confirm that our approach outperforms dynamic clustering methods from the literature."}]