Cross-layer link adaptation for goodput optimization in MIMO BIC-OFDM systems

This work proposes a novel cross-layer link performance prediction (LPP) model and link adaptation (LA) strategy for soft-decoded multiple-input multiple-output (MIMO) bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) systems employing hybrid automatic repeat request (HARQ) protocols. The derived LPP, exploiting the concept of effective signal-to-noise ratio mapping (ESM) to model system performance over frequency-selective channels, does not only account for the actual channel state information at the transmitter and the adoption of practical modulation and coding schemes (MCSs), but also for the effect of the HARQ mechanism with bit-level combining at the receiver. Such method, named aggregated ESM, or αESM for short, exhibits an accurate performance prediction combined with a closed-form solution, enabling a flexible LA strategy, that selects at every protocol round the MCS maximizing the expected goodput (EGP), i.e., the number of correctly received bits per unit of time. The analytical expression of the EGP is derived capitalizing on the αESM and resorting to the renewal theory. Simulation results carried out in realistic wireless scenarios corroborate our theoretical claims and show the performance gain obtained by the proposed αESM-based LA strategy when compared with the best LA algorithms proposed so far for the same kind of systems

A novel queue-aware wireless link adaptation mechanism and its fixed-point analytical model

A point-to-point (PTP) wireless link is studied that carries long-lived TCP flows and is controlled with active queue management (AQM). A cross-layer queue-aware adaptive modulation and coding (AMC)-based link adaptation (LA) mechanism is proposed for this wireless link to improve the TCP-level throughput relative to the case where AMC decisions are made based solely on the physical layer (PHY) parameters. The proposed simple-to-implement LA mechanism involves the use of an aggressive modulation and coding scheme (MCS) with high spectral efficiency and high block error rates when the queue occupancy exceeds a certain threshold, but otherwise a relatively conservative MCS with lower spectral efficiency and lower block error rates. A fixed-point analytical model is proposed to obtain the aggregate TCP throughput attained at this wireless link and the model is validated by ns-3 simulations. Numerical experimentation with the proposed analytical model applied to an IEEE 802.16-based wireless link demonstrates the effectiveness of the proposed queue-aware LA (QAWLA) mechanism in a wide variety of scenarios including cases where the channel information is imperfect. The impact of the choice of the queue occupancy threshold of QAWLA is extensively studied with respect to the choice of AQM parameters in order to provide engineering guidelines for the provisioning of the wireless link. © 2015, Ozturk and Akar

Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation

This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined. The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies

Modeling BLER Performance of Punctured Turbo Codes

In this thesis the author constructed a mathematical model using a continuous and differentiable expression, that approximates the BLER (Block Error Rate) performance of punctured turbo codes for different (SNR) Signal to Noise ratios, block lengths and MCS (Modulation and Coding Schemes) rates. The model can be used for two different purposes. First, the model can be used to replace look-up tables in system level simulators; bringing the advantage of introducing a continuous set of block lengths and MCS rates, not available in look-up tables. Second, with the help of this function communications systems can be optimized using analytical methods based on differentiation. The construction of the mathematical model was developed for the particular case of the Turbo Code (13, 15)8 and QPSK modulation; but the derivation of the model can be generalized to other Turbo Codes and modulation types. The model was also applied to a different link configuration using 16 QAM modulation

Joint Source Channel Coding in Broadcast and Relay Channels: A Non-Asymptotic End-to-End Distortion Approach

The paradigm of separate source-channel coding is inspired by Shannon's separation result, which implies the asymptotic optimality of designing source and channel coding independently from each other. The result exploits the fact that channel error probabilities can be made arbitrarily small, as long as the block length of the channel code can be made arbitrarily large. However, this is not possible in practice, where the block length is either fixed or restricted to a range of finite values. As a result, the optimality of source and channel coding separation becomes unknown, leading researchers to consider joint source-channel coding (JSCC) to further improve the performance of practical systems that must operate in the finite block length regime. With this motivation, this thesis investigates the application of JSCC principles for multimedia communications over point-to-point, broadcast, and relay channels. All analyses are conducted from the perspective of end-to-end distortion (EED) for results that are applicable to channel codes with finite block lengths in pursuing insights into practical design. The thesis first revisits the fundamental open problem of the separation of source and channel coding in the finite block length regime. Derived formulations and numerical analyses for a source-channel coding system reveal many scenarios where the EED reduction is positive when pairing the channel-optimized source quantizer (COSQ) with an optimal channel code, hence establishing the invalidity of the separation theorem in the finite block length regime. With this, further improvements to JSCC systems are considered by augmenting error detection codes with the COSQ. Closed-form EED expressions for such system are derived, from which necessary optimality conditions are identified and used in proposed algorithms for system design. Results for both the point-to-point and broadcast channels demonstrate significant reductions to the EED without sacrificing bandwidth when considering a tradeoff between quantization and error detection coding rates. Lastly, the JSCC system is considered under relay channels, for which a computable measure of the EED is derived for any relay channel conditions with nonzero channel error probabilities. To emphasize the importance of analyzing JSCC systems under finite block lengths, the large sub-optimality in performance is demonstrated when solving the power allocation configuration problem according to capacity-based formulations that disregard channel errors, as opposed to those based on the EED. Although this thesis only considers one JSCC setup of many, it is concluded that consideration of JSCC systems from a non-asymptotic perspective not only is more meaningful, but also reveals more relevant insight into practical system design. This thesis accomplishes such by maintaining the EED as a measure of system performance in each of the considered point-to-point, broadcast, and relay cases

The Interplay between Computation and Communication

In this thesis, a comprehensive exploration into the integration of communication and learning within the massive Internet of Things (mIoT) is undertaken. Addressing one of the fundamental challenges of mIoT, where traditional channel estimation methods prove inefficient due to high device density and short packets; initially, a novel approach leveraging unsupervised machine learning for joint channel estimation and signal detection is proposed. This technique utilizes the Gaussian mixture model (GMM) clustering of received signals, thereby reducing the necessity for exhaustive channel estimation, decreasing the number of required pilot symbols, and enhancing symbol error rate (SER) performance. Building on this foundation, an innovative method is proposed that eliminates the need for pilot symbols entirely. By coupling GMM clustering with rotational invariant (RI) coding, the model maintains robust performance against the effects of channel rotation, thereby improving the efficiency of mIoT systems. This research delves further into integrating communication and learning in mIoT, specifically focusing on federated learning (FL) convergence under error-prone conditions. It carefully analyzes the impact of factors like block length, coding rate, and signal-to-noise ratio on FL's accuracy and convergence. A novel approach is proposed to address communication error challenges, where the base station (BS) uses memory to cache key parameters. Closing the thesis, an extensive simulation of a real-world mIoT system, integrating previously developed techniques, such as the innovative channel estimation method, RI coding, and the introduced FL model. It notably demonstrates that optimal learning outcomes can be achieved even without stringent communication reliability. Thus, this work not only achieves comparable or superior performance to traditional methods with fewer pilot symbols but also provides valuable insights for optimizing mIoT systems within the FL framework

D21.3 Analysis of initial results at EuWIN@CTTC

Deliverable D21.3 del projecte europeu NEWCOM#The nature of this Deliverable of WP2.1 (“Radio interfaces for next-generation wireless systems”) is mainly descriptive and its purpose is to provide a report on the status of the different Joint Research Activities (JRAs) currently ongoing, some of them being performed on the facilities that are available at EuWInPeer ReviewedPreprin