Power allocation and signal labelling on physical layer security

PhD ThesisSecure communications between legitimate users have received considerable attention recently. Transmission cryptography, which introduces secrecy on the network layer, is heavily relied on conventionally to secure communications. However, it is theoretically possible to break the encryption if unlimited computational resource is provided. As a result, physical layer security becomes a hot topic as it provides perfect secrecy from an information theory perspective. The study of physical layer security on real communication system model is challenging and important, as the previous researches are mainly focusing on the Gaussian input model which is not practically implementable. In this thesis, the physical layer security of wireless networks employing finite-alphabet input schemes are studied. In particular, firstly, the secrecy capacity of the single-input single-output (SISO) wiretap channel model with coded modulation (CM) and bit-interleaved coded modulation (BICM) is derived in closed-form, while a fast, sub-optimal power control policy (PCP) is presented to maximize the secrecy capacity performance. Since finite-alphabet input schemes achieve maximum secrecy capacity at medium SNR range, the maximum amount of energy that the destination can harvest from the transmission while satisfying the secrecy rate constraint is computed. Secondly, the effects of mapping techniques on secrecy capacity of BICM scheme are investigated, the secrecy capacity performances of various known mappings are compared on 8PSK, 16QAM and (1,5,10) constellations, showing that Gray mapping obtains lowest secrecy capacity value at high SNRs. We propose a new mapping algorithm, called maximum error event (MEE), to optimize the secrecy capacity over a wide range of SNRs. At low SNR, MEE mapping achieves a lower secrecy rate than other well-known mappings, but at medium-to-high SNRs MEE mapping achieves a significantly higher secrecy rate over a wide range of SNRs. Finally, the secrecy capacity and power allocation algorithm (PA) of finite-alphabet input wiretap channels with decode-and-forward (DF) relays are proposed, the simulation results are compared with the equal power allocation algorithm

Quadrature spatial modulation aided single-input multiple-output-media based modulation: application to cooperative network and golden code orthogonal super-symbol systems.

Doctoral Degree. University of KwaZulu-Natal, Durban.SIMO-MBM (single-input multiple-output media-based modulation) overcomes the limitations of SIMO (single-input multiple-output) systems by reducing the number of antennas required to achieve a high data rate and improved error performance. In this thesis, the quadrature dimension of the spatial constellation is used to improve the overall error performance of the conventional SIMO-MBM and to achieve a higher data rate by decomposing the amplitude/phase modulation (APM) symbol into real and imaginary components, similar to quadrature spatial modulation (QSM). The average bit error probability of the proposed technique is expressed using a lower bound approach and validated using the results of Monte Carlo simulation (MCS). The proposed system also investigates the effect of antenna correlation in combination with channel amplitude to select a sub-optimal mirror activation pattern. The results of MCS show a 3.5dB improvement at 10b/s/Hz with m =2 and a 7dB improvement at 12b/s/Hz with =2 over the traditional SIMO-MBM scheme. The effect of imperfect channel estimation on the proposed scheme is investigated, with a trade-off of 2dB in coding gain due to channel estimation errors. Cooperative Networking (CN) improves wireless network reliability, link quality, and spectrum efficiency by collaborating among nodes. The decode and forward relaying technique is used in this thesis to investigate the performance of QSM aided SIMO-MBM in a Cooperative Network (CN). This technique uses two source nodes that simultaneously transmit a unique message block on the same time slot to the relay node, which then decodes the received message block from both transmitting nodes before re-encoding and re-transmitting the decoded message block in the next time slot to the destinations in order to significantly improve the QSM aided SIMO-MBM’s error performance. Using network coding (NC) techniques, each Node can decode the data of the other Node. To enhance network performance, complexity, robustness, and minimize delays, data is encoded and decoded in NC; algebraic techniques are applied to the detected message to collect the various transmissions. The proposed scheme's theoretical average error probability was defined using a lower bound technique, and the results of Monte Carlo simulation (MCS) validated the result. The MCS results achieved exhibit a significant improvement of 8 dB at 6 b/s/Hz and 12 dB at 8 b/s/Hz over the conventional QSM aided SIMO-MBM scheme. The media-based modulation (MBM) technique can achieve significant throughput, increase spectrum efficiency, and improve bit-error-rate performance (BER). In this thesis, the use of MBM in single-input multiple-output systems is examined using radio frequency (RF) mirrors and Golden code (GC-SIMO). The goal is to lower the system's hardware complexity by maximizing the linear relationship between RF mirrors and spectral efficiency in MBM in order to achieve a high data rate with less hardware complexity. The GC scheme's encoder uses orthogonal pairs of the super-symbol, each transmitted via a separate RF mirror at a different time slot to achieve full rate full diversity. In the results of MCS obtained, at a BER of 10−5, the GC-SIMO-MBM exhibits a significant performance of approximately 7dB and 6.5 dB SNR gain for 4 b/s/Hz and 6 b/s/Hz, respectively, compared to GC-SIMO. The proposed scheme's derived theoretical average error probability is validated by the results of the Monte Carlo simulation

Spectral-energy efficiency trade-off of relay-aided cellular networks

Wireless communication networks are traditionally designed to operate at high spectral e ciency with less emphasis on power consumption as it is assumed that endless power supply is available through the power grid where the cells are connected to. As new generations of mobile networks exhibit decreasing gains in spectral e ciency, the mobile industry is forced to consider energy reform policies in order to sustain the economic growth of itself and other industries relying on it. Consequently, the energy e ciency of conventional direct transmission cellular networks is being examined while alternative green network architectures are also explored. The relay-aided cellular network is being considered as one of the potential network architecture for energy e cient transmission. However, relaying transmission incurs multiplexing loss due to its multi-hop protocol. This, in turn, reduces network spectral e ciency. Furthermore, interference is also expected to increase with the deployment of Relay Stations (RSs) in the network. This thesis examines the power consumption of the conventional direct transmission cellular network and contributes to the development of the relay-aided cellular network. Firstly, the power consumption of the direct transmission cellular network is investigated. While most work considered transmitter side strategies, the impact of the receiver on the Base Station (BS) total power consumption is investigated here. Both the zero-forcing and minimum mean square error weight optimisation approaches are considered for both the conventional linear and successive interference cancellation receivers. The power consumption model which includes both the radio frequency transmit power and circuit power is described. The in uence of the receiver interference cancellation techniques, the number of transceiver antennas, circuit power consumption and inter-cell interference on the BS total power consumption is investigated. Secondly, the spectral-energy e ciency trade-o in the relay-aided cellular network is investigated. The signal forwarding and interference forwarding relaying paradigms are considered with the direct transmission cellular network taken as the baseline. This investigation serves to understand the dynamics in the performance trade-o . To select a suitable balance point in the trade-o , the economic e ciency metric is proposed whereby the spectral-energy e ciency pair which maximises the economic pro tability is found. Thus, the economic e ciency metric can be utilised as an alternative means to optimise the relay-aided cellular network while taking into account the inherent spectral-energy e ciency trade-o . Finally, the method of mitigating interference in the relay-aided cellular network is demonstrated by means of the proposed relay cooperation scheme. In the proposed scheme, both joint RS decoding and independent RS decoding approaches are considered during the broadcast phase while joint relay transmission is employed in the relay phase. Two user selection schemes requiring global Channel State Information (CSI) are considered. The partial semi-orthogonal user selection method with reduced CSI requirement is then proposed. As the cooperative cost limits the practicality of cooperative schemes, the cost incurred at the cooperative links between the RSs is investigated for varying degrees of RS cooperation. The performance of the relay cooperation scheme with di erent relay frequency reuse patterns is considered as well. In a nutshell, the research presented in this thesis reveals the impact of the receiver on the BS total power consumption in direct transmission cellular networks. The relayaided cellular network is then presented as an alternative architecture for energy e cient transmission. The economic e ciency metric is proposed to maximise the economic pro tability of the relay network while taking into account the existing spectral-energy e ciency trade-o . To mitigate the interference from the RSs, the relay cooperation scheme for advanced relay-aided cellular networks is proposed

Virtual-MIMO systems with compress-and-forward cooperation

Multiple-input multiple-output (MIMO) systems have recently emerged as one of the most significant wireless techniques, as they can greatly improve the channel capacity and link reliability of wireless communications. These benefits have encouraged extensive research on a virtual MIMO system where the transmitter has multiple antennas and each of the receivers has a single antenna. Single-antenna receivers can work together to form a virtual antenna array and reap some performance benefits of MIMO systems. The idea of receiver-side local cooperation is attractive for wireless networks since a wireless receiver may not have multiple antennas due to size and cost limitations. In this thesis we investigate a virtual-MIMO wireless system using the receiver-side cooperation with the compress-and-forward (CF) protocol. Firstly, to perform CF at the relay, we propose to use standard source coding techniques, based on the analysis of its expected rate bound and the tightness of the bound. We state upper bounds on the system error probabilities over block fading channels. With sufficient source coding rates, the cooperation of the receivers enables the virtual-MIMO system to achieve almost ideal MIMO performance. A comparison of ideal and non-ideal conference links within the receiver group is also investigated. Considering the short-range communication and using a channel-aware adaptive CF scheme, the impact of the non-ideal cooperation link is too slight to impair the system performance significantly. It is also evident that the practicality of CF cooperation will be greatly enhanced if a efficient source coding technique can be used at the relay. It is even more desirable that CF cooperation should not be unduly sensitive to carrier frequency offsets (CFOs). Thus this thesis then presents a practical study of these two issues. Codebook designs of the Voronoi VQ and the tree-structure vector quantization (TSVQ) to enable CF cooperation at the relay are firstly described. A comparison in terms of the codebook design complexity and encoding complexity is presented. It is shown that the TSVQ is much simpler to design and operate, and can achieve a favourable performance-complexity tradeoff. We then demonstrate that CFO can lead to significant performance degradation for the virtual MIMO system. To overcome it, it is proposed to maintain clock synchronization and jointly estimate the CFO between the relay and the destination. This approach is shown to provide a significant performance improvement. Finally, we extend the study to the minimum mean square error (MMSE) detection, as it has a lower complexity compared to maximum likelihood (ML) detection. A closed-form upper bound for the system error probability is derived, based on which we prove that the smallest singular value of the cooperative channel matrix determines the system error performance. Accordingly, an adaptive modulation and cooperation scheme is proposed, which uses the smallest singular value as the threshold strategy. Depending on the instantaneous channel conditions, the system could therefore adapt to choose a suitable modulation type for transmission and an appropriate quantization rate to perform CF cooperation. The adaptive modulation and cooperation scheme not only enables the system to achieve comparable performance to the case with fixed quantization rates, but also eliminates unnecessary complexity for quantization operations and conference link communication

Learning-based communication system design – autoencoder for (differential) block coded modulation designs and path loss predictions

Shannon’s channel coding theorem states the existence of long random codes that can make the error probability arbitrarily small. Recently, advanced error-correcting codes such as turbo and low-density parity-check codes have almost reached the theoretical Shannon limit for binary additive white Gaussian noise channels. However, designing optimal high-rate short-block codes with automatic bit-labeling for various wireless networks is still an unsolved problem. Deep-learning-based autoencoders (AE) have appeared as a potential near-optimal solution for designing wireless communications systems. We take a holistic approach that jointly optimizes all the components of the communication networks by performing data-driven end-to-end learning of the neural network-based transmitter and receiver together. Specifically, to tackle the fading channels, we show that AE frameworks can perform near-optimal block coded-modulation (BCM) and differential BCM (d-BCM) designs in the presence and absence of the channel state information knowledge. Moreover, we focus on AE-based designing of high-rate short block codes with automatic bit-labeling that are capable of outperforming conventional networks with larger margins as the rate R increases. We also investigate the BCM and d-BCM from an information-theoretic perspective. With the advent of internet-of-things (IoT) networks and the widespread use of small devices, we face the challenge of limited available bandwidth. Therefore, novel techniques need to be utilized, such as full-duplex (FD) mode transmission reception at the base station for the full utilization of the spectrum, and non-orthogonal multiple access (NOMA) at the user-end for serving multiple IoT devices while fulfilling their quality-of-service requirement. Furthermore, the deployment of relay nodes will play a pivotal role in improving network coverage, reliability, and spectral efficiency for the future 5G networks. Thus, we design and develop novel end-to-end-learning-based AE frameworks for BCM and d-BCM in various scenarios such as amplify-and-forward and decode-and-forward relaying networks, FD relaying networks, and multi-user downlink networks. We focus on interpretability and understand the AE-based BCM and d-BCM from an information-theoretic perspective, such as the AE’s estimated mutual information, convergence, loss optimization, and training principles. We also determine the distinct properties of AE-based (differential) coded-modulation designs in higher-dimensional space. Moreover, we also studied the reproducibility of the trained AE framework. In contrast, large bandwidth and worldwide spectrum availability at mm-wave bands have also shown a great potential for 5G and beyond, but the high path loss (PL) and significant scattering/absorption loss make the signal propagation challenging. Highly accurate PL prediction is fundamental for mm-wave network planning and optimization, whereas existing methods such as slope-intercept models and ray tracing fall short in capturing the large street-by-street variation seen in urban cities. We also exploited the potential benefits of AE framework-based compression capabilities in mm-wave PL prediction. Specifically, we employ extensive 28 GHz measurements from Manhattan Street canyons and model the street clutters via a LiDAR point cloud dataset and 3D-buildings by a mesh-grid building dataset. We aggressively compress 3D-building shape information using convolutional-AE frameworks to reduce overfitting and propose a machine learning (ML)-based PL prediction model for mm-wave propagation.EPSRC-UKRI fundin

Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

Coherent and Non-coherent Techniques for Cooperative Communications

Future wireless network may consist of a cluster of low-complexity battery-powered nodes or mobile stations (MS). Information is propagated from one location in the network to another by cooperation and relaying. Due to the channel fading or node failure, one or more relaying links could become unreliable during multiple-hop relaying. Inspired by conventional multiple-input multiple-output (MIMO) techniques exploiting multiple co-located transmit antennas to introduce temporal and spatial diversity, the error performance and robustness against channel fading of a multiple-hop cooperative network could be significantly improved by creating a virtual antenna array (VAA) with various distributed MIMO techniques. In this thesis, we concentrate on the low-complexity distributed MIMO designed for both coherent and non-coherent diversity signal reception at the destination node. Further improvement on the network throughput as well as spectral efficiency could be achieved by extending the concept of unidirectional relaying to bidirectional cooperative communication. Physical-layer network coding (PLNC) assisted distributed space-time block coding (STBC) scheme as well as non-coherent PLNC aided distributed differential STBC system are proposed. It is confirmed by the theoretical analysis that both approaches have the potential for offering full spatial diversity gain. 　　 Furthermore, differential encoding and non-coherent detection techniques are generally associated with performance degradation due to the doubled noise variance. More importantly, conventional differential schemes suffer from the incapability of recovering the source information in time-varying channels owing to the assumption of static channel model used in the derivation of non-coherent detection algorithm. Several low-complexity solutions are proposed and studied in this thesis, which are able to compensate the performance loss caused by non-coherent detection and guarantee the reliable recovery of information in applications with high mobility. A substantial amount of iteration gain is achieved by combining the differential encoding with error-correction code and sufficient interleaving, which allows iterative possessing at the receiver