gains, although as we are arguing these gains may be realised as spectral efficiency gain or network efficiency gain rather than per user power-efficiency gain. LTE microscheduling and macroscheduling both rely on channel-feedback mechanisms (channel-quality indication and channel state indication) but are implemented at different time resolution, as small as a millisecond for microscheduling and typically a few seconds or several minutes for handover, depending on how fast the user is moving and the cell geometry and cell density. Packet scheduling (microscheduling) is realised in both the frequency and time domain. The CQI is computed from frequency-domain and time-domain reference symbols that are seeded into the channel data stream. The CQI measurements then determine whether QPSK, 16 QAM or 64 QAM modulation is used, the level of error coding, whether MIMO will be used and whether and how frequency selective scheduling is used. All of these will be influenced by how fast the user is moving. Frequency selective scheduling would typically be choosing say 25% of the subcarriers with the highest CQI. In terms of e node B energy consumption, increased sensitivity in a user device operating in a noise-limited environment will result in a higher reported downlink CQI that will translate into a higher allocation of downlink time and frequency subcarrier resource blocks. A better link budget also translates into being able to support higher-order modulation and lighter error protection/correction overheads, which means that the ratio of information bits to error protection bits improves. Thus, any improvement in sensitivity will result in greater information throughput for a given amount of input energy. Note that channel coding and other adaptive physical layer mechanisms mitigate the impact of poor user equipment sensitivity or selectivity, but the bandwidth and power cost is effectively transferred to other users. The relationship of user equipment performance to overall network QOS and QOE is therefore direct and