Link adaptation (LA) has been included since the first full LTE release (Release 8). The basic idea of LA is that the terminal provides information about its current DL channel conditions to the network, which can then adapt its scheduling in the DL accordingly.
For example, if the channel conditions degrade, the network can reduce the coding rate, change the modulation order, or switch from a two-layer transmission back to a single layer transmission to reach a desirable point of operation in terms of block error rates (BLER) with a reasonable receiver complexity.
Figure 2-10 shows the basic relationship between complexity on the receiver side and throughput.
For high SNR, the higher the modulation and coding scheme (MCS), the higher is the achievable throughput. As mentioned earlier, this higher throughput is achieved by using a higher modulation order, that is, for instance, 64 QAM instead of QPSK, by increasing the number of streams, or by increasing the coding rate. As long as the SNR is high enough, any MCS can be decoded at the receiver side with reasonably low complexity. However, as the SNR decreases, decoding of a high MCS requires increasingly complex receiver algorithms, for example, a sphere decoder instead of an MMSE detector, or advanced joint channel/coding algorithms, leading eventually to an undesirable operating point. In order to limit the necessary receiver complexity, the network may reduce the MCS values for the DL as the SNR goes down. At very low SNR, only very robust modulation schemes like QPSK, a single layer, and a very robust coding scheme are used, allowing the receiver to decode the transmitted data bits with reasonable decoding complexity. In summary, link adaptation is a mechanism used to strike a balance between high throughput for high SNR and low receiver complexity at low SNR.
LTE link adaptation (LA) is based on measurement information that the terminals provide to the network: the channel quality indicator (CQI), the precoding matrix indicator/precoding type indicator (PMI/PTI), and the rank indicator (RI). Depending on the reporting mode, CQI and PMI can be signaled as wideband values, which are then interpreted by the network as best values if applied to the entire bandwidth, or as subband specific values, valid only in a specific range of the complete system bandwidth. Depending on the transmission mode the terminal reports one CQI value for both code words, or one CQI value per code word. The rank indicator informs the network on the number of spatial layers the terminal is able to receive under its current channel conditions. The network on the other hand is free to use or not use the received information for the selection of the DL MCS transmitted to the terminals.
Figure 2-10. Link adaptation: throughput versus receiver complexity
While the general idea and concept of LA is simple, providing close to optimal CSI information to the network is nontrivial. According to 3GPP, calibration of the terminal and its CSI reporting should be done in such a way that the block error rate for initial transmissions is 10 percent or less. If the LA is too aggressive the BLER goes up, resulting in a throughput loss due to decoding errors on the terminal side. On the other hand, a too conservative LA may cause the network to use transport formats with a code rate lower than necessary, also resulting in throughput loss. To achieve optimal throughput a well-calibrated link adaptation is therefore mandatory.
What makes implementation difficult is the large number of parameters in an LTE system: ten transmission modes, four periodic reporting modes, and six aperiodic reporting modes. Beyond the parameters of the LTE system itself, there are at least six 3GPP channel types that need to be considered, varying from low to high delay spread, and from static to 300 Hz Doppler channels in addition to a myriad of real-life channels.
The result of this large parameter space is a high control complexity, usually also leading to an increase in the required memory as all of these different modes must be handled slightly differently.
Another important factor to consider is that verification of the chipsets is done using several types of test equipment, such as Callbox testing and testing with real infrastructure hardware.
Callbox testing : Here the terminal is usually connected directly to the callbox via a rather ideal cable, and the callbox generates a channel model based on the selected parameters, such as Doppler bandwidth, delay spread, and so on. The callbox usually follows the reported terminal metrics, such as CQI, PMI, RI, directly without processing them. The received CSI information is directly mapped into suitable DL parameters, such as MCS format and is transmitted on the DL with the correct timing. For example, CQI values are mapped more or less directly into MCS values without filtering them and without checking whether the applied values provide performance close to the target BLER or not. Hence, when doing performance comparisons between two implementations based on callbox testing, it is very important to have a well-calibrated device that behaves well for SNR sweep tests under different fading profiles. CSI calibration errors are very visible in these kind of tests.
Infrastructure testing : When carrying out infrastructure tests, the terminal is also connected via cables to the eNB as in callbox testing. However, in contrast to a callbox the eNB usually applies some kind of outer loop link adaptation (OLLA) to the received CSI information before mapping it into a DCI transmitted to the terminal to achieve a certain target BLER. Besides this, the processed CSI metrics affect not only the selected DCI in the DL, but also the scheduling rate of the UE. In particular, infrastructure tests with multiple terminals providing sub-optimal CSI metrics may lead to scheduling the desired terminal at a low rate and, for example, an interfering terminal at a high rate. Calibration errors are usually not so visible in infrastructure tests, due to OLLA. On the other hand, optimal performance in infrastructure tests can only be achieved if the behavior of the eNB is known to the terminal. For example, does the eNB follow the rank decision of the terminal directly, or does the eNB apply filtering algorithms to the received CQI metrics?
To make things complex, different network vendors treat and process the CSI metrics in different ways, but one chipset has to work optimally, or close to optimally, in all networks.
Over the air (OTA) testing : In OTA tests the network equipment and the terminal are usually placed in an anechoic chamber where it is possible to carry out cable-less tests in a controlled way.
Field testing : The ultimate test: LA has to assume that certain parameters are set in a certain way and computes the CSI metric accordingly. Since a UE does not know what the eNB does with the metrics, it is difficult for the UE to decide whether eNB is following or not.
Sometimes a UE does certain things that are not specified in the standard. For example, with CQI filtering, the CQI should normally reflect the situation of one specific subframe. With some kinds of filtering, throughput can be improved in certain networks.
One more challenge is to tune the CSI in such a way that it works optimally with all eNB equipment manufacturers and also well with callbox testing.