Fluctuating interference has a negative impact on link adaptation (i.e., selection of modulation and coding schemes, scheduling, etc).
While deploying MRs and IGWs, intricate relationships among antenna types, wireless links with adaptive modulation and coding, MAC scheduling, routing, and equipment cost render the network planning an extremely complex problem.
To further increase the data rate, the HSDPA system introduced new features [5] such as adaptive modulation and coding and fast scheduling.
A more fundamental limit to be considered is the channel's coherence time, as centralized functionalities such as adaptive modulation and coding scheme (MCS) selection, precoding, and scheduling require up-to-date channel information in order to perform adequately.
Intricate relationships among antenna type used, wireless links with adaptive modulation and coding (AMC) scheme, MAC scheduling, routing, and equipment cost render the problem of optimal WMN planning extremely complex to address.
To provide the essential information for spatial scheduling and modulation and coding selection (MCS), each user needs to feed back the estimated channel quality information (CQI) to the BS, which should be signal‐to‐noise and interference ratio (SINR) in multi‐user MIMO (MU‐MIMO) systems [4].
Furthermore, the LTE technology is highly complex due to the inclusion of advanced features such as OFDMA and SC-FDMA, adaptive modulation and coding (AMC), dynamic MAC scheduling, and hybrid automatic repeat request (HARQ) [14]; hence, it is much more difficult to predict the actual system capacity in a given scenario than it was for previous mobile technologies.
Further work is oriented to study modulation-constrained resource allocation for the uplink, user's grouping and scheduling strategies, and coordination of transmissions between cell clusters.
