The wireless channel is impaired by random fluctuations in signal level across space, time, and frequency dimensions known as fading. This signal fluctuation (also called diversity) can be exploited by the use of multiple transmissions/ receptions distributed across time or space with appropriate encoding to enhance and stabilize the signal level at the receiver. Such a paradigm is called space-time communication. The multiple (ideally independent) views (also called diversity branches) of the signal ensure that the link reliability is improved since the probability of all of them being in a fade at the same time reduces sharply with the number of views. In this paper, we consider the use of cooperative transmissions in multihop wireless networks to achieve Virtual Multiple Input Single Output (VMISO) links. the different virtual array approaches, VMISO requires the lowest coordination effort because it can leverage the broadcast property of the wireless channel to distribute information to the cooperating transmitters with a single transmission. This is unlike VSIMO or VMIMO, where multiple information exchanges are required at the receiver to decode information. Second, although VMISO allows improved data rates, the coordination overhead and complexity of channel state information and processing are significant challenges in VMIMO which do not affect VMISO. VMISO is an essential step toward realizing VMIMO and the principles developed in this work can be used as a building block for realizing various distributed space-time communication approaches. The importance of jointly optimizing the link rate and the hop distance to achieve performance improvements using VMISO transmissions. Specifically, a simple approach of optimizing the throughput of links followed by optimizing the range, can greatly reduce the aggregate throughput of flows compared to jointly optimizing the link rates and the hop distances . The majority of the works involving smart antennas or distributed space-time communication , use a fixed cluster size throughout the network, a strategy is suboptimal using both simulations and analysis we also identify several approaches for adapting the rate, range, and cluster size and establish the limitations of each of them. The insights are used to identify the best approach to joint optimization of all three parameters and incorporated in a routing protocol.