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Link Adaptation is a core feature introduced in gNodeB (gNB) for Adaptive Modulation and Coding (AMC) scheme in new generation cellular networks. The main purpose of this is to correct the estimated Signal-to-Interference-plus-Noise ratio (SINR) at gNB and select the appropriate Modulation and Coding Scheme (MCS) so the User Equipment (UE) can decode the data successfully. Link adaptation is necessary for mobile communications because of the diverse wireless conditions of the channel due to mobility of users, interference, fading and shadowing effects, the estimated SINR will always be different from the actual value. The traditional link adaptation schemes like Outer Loop Link Adaptation (OLLA) improve the channel estimation by correcting the estimated SINR with some correction factor dependent on the Block Error Rate (BLER) target. But this scheme has a low convergence i.e., it takes several Transmission Time Intervals (TTIs) to adjust to the channel variations. Reinforcement Learning (RL) based framework is proposed to deal with this problem. Deep Deterministic Policy Gradient (DDPG) algorithm is selected as an agent and trained with several states of the channel variations to adapt to the changes. The trained model seems to show an increase in throughput for cell edge users of about 6-18% when compared to other baseline models. The mid-cell user throughput is increased up to 1-3%. This RL model trained is constrained with average BLER minimization and throughput maximization which makes the model perform well in different radio conditions. / Länkanpassning är en kärnfunktion som introduceras i gNB för adaptiv modulering och kodningsschema (AMC) i den nya generationens cellulära nätverk. Den huvudsakliga syftet med detta är att korrigera det uppskattade signal-till-störning-plus-bruset ratio (SINR) vid gNodeB (gNB) och välj lämplig Modulation och Coding Scheme (MCS) så att användarutrustningen (UE) kan avkoda data framgångsrikt. Länkanpassning är nödvändig för mobil kommunikation eftersom av de olika trådlösa förhållandena för kanalen på grund av användarnas mobilitet, störnings-, bleknings- och skuggeffekter, kommer den uppskattade SINR alltid skiljer sig från det faktiska värdet. De traditionella länkanpassningssystemen som Outer Loop Link Adaptation (OLLA) förbättra kanaluppskattningen med korrigera det uppskattade SINR med någon korrigeringsfaktor beroende på Mål för Block Error Rate (BLER). Men detta system har en låg konvergens det är det krävs flera TTI för att anpassa sig till kanalvariationerna. Förstärkning Ett lärande (RL)-baserat ramverk föreslås för att hantera detta problem. Djup Deterministic Policy Gradient (DDPG) algoritm väljs som en agent och tränas med flera tillstånd av kanalvariationerna för att anpassa sig till förändringarna. Den tränade modellen verkar visa en ökning i genomströmning för cellkantanvändare på cirka 6-18% jämfört med andra basmodeller. Mittcellsanvändaren genomströmningen ökas upp till 1-3%. Denna RL-modell utbildad är begränsad med genomsnittlig BLER-minimering och genomströmningsmaximering vilket gör modell fungerar bra i olika radioförhållanden.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / An implementation of a Pseudo Error Detection (PSED) system is presented and its performance in conjunction with smart antenna and smart diversity systems tested and evaluated. Non redundancy, instant response and relative simplicity make the Pseudo Error Detectors excellent real time error monitoring systems in smart antenna and smart diversity systems. Because of the Non-redundant Error Detection mechanism in Pseudo Error Detectors, we can monitor the error quality without any coding or overhead. The output of the pseudo error detector in AWGN, selective fading Doppler shift and other interference environments is directly correlated to the BER and BLER. This direct correlation makes it a great tool for online error monitoring of a system and can have numerous applications In a PSED the Eye diagram from the demodulator is sampled once per symbol. By monitoring and comparison of the eye at sampled intervals at different thresholds, we would know if an error has occurred. By integrating this result over a period of time we can get the averaged error level. The results provided in this paper were obtained and verified by both MatLab simulations using dynamic simulation techniques and hardware measurements over dynamic channels.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / An improved performance smart diversity was invented to improve the signal performance in a combined selective fading, Additive White Gaussian Noise (AWGN), Co-channel interference (CCI) and Doppler shift environment such as the LEO satellite channel. This system is also applicable to aeronautical and telemetry channels. Smart diversity is defined here as a mechanism that selects at each moment the best branch in a n-branch diversity system based on the error quality with no default branch and no prioritization. The predominant novelty of this discovery is the introduction of multi level analog based Pseudo Error Detectors (PSED) in every branch. One of the advantages of PSED is that it is a non redundant error detection system, with no requirement for overhead and no need for additional valuable spectrum. This research was motivated by problems in LEO satellite systems due to low orbit and high relative speed with respect to the ground stations. The system is independent of the modulation techniques and is applicable to both coherent and non-coherent detections. The results from simulations using dynamic simulation techniques and hardware measurements over dynamic channels show significant improvement of both the Bit Error Rate (BER) and the Block Error Rate (BLER).

To increase link throughput in multi-input multi-output (MIMO) orthogonal frequencydivision multiplexing (OFDM) systems, transmission parameters such as code rate andmodulation order are required to be set adaptively. Therefore, block error rate (BLER)becomes a crucial measure which illustrates the quality of the link, thus being used in LinkAdaptation (LA) to determine the transmission parameters. However, existing methods topredict BLER are only valid for linear detectors, e.g. Minimum Mean Square Error (MMSE)detector [1]. In this thesis, we show that signal-to-interference-plus-noise ratio (SINR)exists in MIMO-OFDM system with MLD (maximum likelihood detection). Then, a machinelearning based method with Deep Neural Network (DNN) is proposed to analyze therelation between input features (channel matrix, modulation and coding scheme (MCS),signal-to-noise ratio(SNR)) and labels (CRC). Results shows that the classification of DNNis good. However, there is still deviation when compared output of DNN with thesimulated BLER. / För att öka länkhastigheten i MIMO-OFDM system bör överföringsparametrar somkodhastighet och moduleringsordning ändras dynamiskt. Blockfelfrekvensen (BLER) är enviktig komponent i kommunikationssystem som representerar hela länkkedjans status ochkan användas i länkanpassning (LA) för att avgöra överföringsparametrarna. Befintligametoder för att beräkna BLER är endast giltiga för linjära detektorer eg. MMSE. I dennarapport visar vi at SINR existerar i MIMO-OFDM system med MLD. Sedan föreslås enmaskininlärningsmetod baserad på djupa neuronnät (DNN) för att analysera förhållandetmellan olika delar av indata (kanal matrix, MCS, SNR) och utdata (CRC). Resultatet visar attDNN klassificerar CRC bra. Utdata från DNN avviker dock vid jämförsele med simuleradBLER.

The upcoming 5G mobile communications system promises to enable use cases requiring ultra-reliable and low latency communications. Researchers therefore require more detailed information about aspects such as channel coding performance at very low block error rates. The simulations needed to obtain such results are very time consuming and this poses achallenge to studying the problem. This thesis investigates the use of hardware acceleration for performing fast simulations of turbo code performance. Special interest is taken in investigating different methods for generating normally distributed noise based on pseudorandom number generator algorithms executed in DSP:s. A comparison is also done regarding how well different simulator program structures utilize the hardware. Results show that even a simple program for utilizing parallel DSP:s can achieve good usage of hardware accelerators and enable fast simulations. It is also shown that for the studied process the bottleneck is the conversion of hard bits to soft bits with addition of normally distributed noise. It is indicated that methods for noise generation which do not adhere to a true normal distribution can further speed up this process and yet yield simulation quality comparable to methods adhering to a true Gaussian distribution. Overall, it is show that the proposed use of hardware acceleration in combination with the DSP software simulator program can in a reasonable time frame generate results for turbo code performance at block error rates as low as 10−9.