One of the key visions of the sixth generation (6G) network is to provide global coverage and ubiquitous connectivity. To support this vision, the design and optimization of integrated Terrestrial and Non-Terrestrial Networks (TN-NTN) is of great importance. The heterogeneous entities in integrated TN-NTN network pose great challenges in the design and optimization. This paper investigates the joint optimization of Unmanned Aerial Vehicle (UAV) base station (BS) placement, user association, and Resource Block (RB) allocation for integrated TN-NTN networks, with the objective to support more users while satisfying Quality of Service (QoS) requirements, and achieving fairness among users. More specifically, we proposed a Heterogeneous Multi-Agent Deep Reinforcement Learning (HeMADRL) framework with various types of agents to solve the problem. Simulation results show that the proposed algorithm outperforms benchmark algorithms.

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost.

In this paper, we present5Ghoul, a framework to systematically discover and replicate security vulnerabilities on arbitrary 5 G edge devices (UE). At the core of5Ghoulis a stateful fuzzing strategy that provides full control to arbitrarily manipulate any packet down to the data link layer. Moreover,5Ghoulautomatically constructs the protocol state machines to guide the fuzzing process and employs novel strategies to reliably exploit vulnerabilities on commercial-off-the-shelf (COTS) UEs over-the-air. The design choices in5Ghoulwere carefully taken to allow packet manipulation in real-time, which, in turn allowed us to fuzz down to data link layer. As of today, we have evaluated5Ghoulwith seven COTS 5 G UEs (smartphones and USB modems) and one open source framework (OpenAirInterface).5Ghoulhas uncovered 12 unknown security vulnerabilities (14 in total) out of which ten exist in COTS UEs (ten CVEs assigned) from major vendors (e.g., Qualcomm and MediaTek). Moreover, of these COTS UE vulnerabilities have been confirmed to have high severity. We also won a bug bounty of over 20 K USD from Qualcomm and MediaTek for discovering these vulnerabilities. We envision5Ghoulto open the door for 5G security testing at scale.

In large-scale urban environments, precise six-degree-of-freedom (6DOF) pose estimation is essential for vehicles and robots to perform autonomous driving and exploration, as well as to achieve high intelligence and full autonomy of Unmanned Aerial Vehicles (UAV). Achieving 6DOF pose estimation in Global Navigation Satellite System (GNSS)-denied environments is challenging. The performance of relative 6DOF localization systems based on Light Detection and Ranging (LiDAR), vision, and inertial data is easily affected by environmental conditions, leading to error accumulation and a significant decrease in estimation accuracy in complex environments. To address this issue, we propose a tightly coupled framework based on nonlinear optimization for vision, LiDAR, inertial, and GNSS raw data. In the experimental section, we validate the effectiveness of the proposed optimization factor model for GNSS data, LiDAR data, and visual data in improving position and orientation estimation accuracy through simulations. Additionally, we use real datasets to compare the proposed algorithm with several existing open-source programs in terms of computational efficiency, pose estimation accuracy, worst-case scenarios, and reliability. The experimental results show that, although the total processing time increases, the position estimation accuracy and orientation estimation accuracy of the proposed fusion algorithm improve by at least 58.0%. Overall, the proposed tightly-coupled algorithm outperforms the existing methods.

In this paper, we conceptualize, design and evaluate VaktBLE, a novel framework to defend BLE peripherals against low-level BLE attacks. VaktBLE presents a novel, efficient and (almost) deterministic technique to silently hijack the connection between a potentially malicious BLE central and the target peripheral to be protected. This creates a benevolent man-in-the-middle (MiTM) bridge that allows us to validate each packet sent by the BLE central. For validation, we implement a flexible and extensible framework to detect a variety of attacks due to packets that are invalid, out-of-order or flooded. An appealing capability of VaktBLE is that it can validate all packets down to the link layer, thus allowing us to defend against complex BLE attacks that bypass state-of-the art binary patching frameworks. We have implemented VaktBLE and evaluated it with 25 state-of-the-art BLE attack vectors from offensive tools such as SweynTooth, CyRC and BLEDiff. Our evaluation shows that VaktBLE effectively detects all these attacks and the VaktBLE MitM bridge incurs only 10ms overhead. Moreover, we have evaluated the capability and robustness of VaktBLE against several adaptive attacks including fuzzing-based attacks. We also show the extensibility of VaktBLE to counteract protocol-level attacks and rogue peripherals. Our evaluation reveals that VaktBLE not only stops fuzzing-based attacks with high effectiveness (97.5%), but VaktBLE also does not incur false positives when attacks are randomly mixed with benign connection attempts.

In vehicular-to-infrastructure (V2I) applications, as we adopt higher frequencies and antenna array processing for faster data rates, beamforming is a critical enabling technology to preserve high-quality communication links between mobile users and the base station. Traditional beamforming requires overhead in establishing the directional link, compounded by short coherence time of mobile users, which necessitates frequent re-calibration. Recent works consider more efficient strategies by leveraging context information, with position-aware approaches showing promise. Nonetheless, uncertainties in even the most advanced localization and odometry methods can lead to com-pounded errors in position estimation for downstream tasks such as beam alignment and tracking. Our work addresses these challenges by factoring in the uncertainties inherent in practical odometry systems. We propose a beamforming optimization that accounts for this uncertainty, complemented by overparameter- ization strategies novel to this non-linear problem of interest. Our simulation results demonstrate the feasibility of maintaining robust communication links in representative scenarios by lever-aging localization and odometry information, despite challenges posed by a mobile platform's imprecise position estimates.

The fusion framework based on Light Detection and Ranging (LiDAR) and vision is susceptible to environmental constraints. Therefore, they are unable to adapt to complex and challenging environments. In response to this, this paper proposes a tightly coupled framework based on Global Navigation Satellite System (GNSS) data, which tightly couples GNSS, inertial, LiDAR and visual data. The experimental results demonstrate that, using publicly available challenging environment datasets, the proposed fusion system can achieve a 52.68% improvement in positioning accuracy compared to some existing fusion algorithms.

Age of Information (AoI) has been proposed to quantify the freshness of information for emerging real-time applications such as remote monitoring and control in wireless networked control systems (WNCSs). Minimization of the average AoI and its outage probability can ensure timely and stable transmission. Energy efficiency (EE) also plays an important role in WNCSs, as many devices are featured by low cost and limited battery. Multi-connectivity over multiple links enables a decrease in AoI, at the cost of energy. We tackle the unresolved problem of selecting the optimal number of connections that is both AoI-optimal and energy-efficient, while avoiding risky states. To address this issue, the average AoI and peak AoI (PAoI), as well as PAoI violation probability are formulated as functions of the number of connections. Then the EE-PAoI ratio is introduced to allow a tradeoff between AoI and energy, which is maximized by the proposed risk-aware, AoI-optimal and energy-efficient connectivity scheme. To obtain this, we analyze the property of the formulated EE-PAoI ratio and prove the monotonicity of PAoI violation probability. Interestingly, we reveal that the multi-connectivity scheme is not always preferable, and the signal-to-noise ratio (SNR) threshold that determines the selection of the multiconnectivity scheme is derived as a function of the coding rate. Also, the optimal number of connections is obtained and shown to be a decreasing function of the transmit power. Simulation results demonstrate that the proposed scheme enables more than 15 folds of EE-PAoI gain at the low SNR than the single-connectivity scheme.

We consider a wireless networked control system (WNCS) for real-time applications. Different physical processes (e.g., dynamic plant state and Markov Process-based context information) are monitored by multiple sensors via imperfect wireless links and then received/estimated by the controller for remote control. In this paper, we use violation probability as a key performance metric, which captures extreme events that violate the preset threshold of the plant state, to ensure the stability of WNCS. Scheduling multiple sensors with limited resources is challenging, due to the delays and loss in transmission, and their heterogeneous impacts on the WNCS goal. To address this problem, we first show the relationship between the considered goal and the actions of the scheduler and controller. We then present a Markov Decision Problem (MDP) to minimize violation probability with cost constraints. By analyzing the significance of information that incorporates goal-related usefulness and contextual importance, the structural results of the formulated MDP is presented. Then a goal-oriented scheduling and control co-design policy is proposed to improve the violation probability-cost tradeoff. Simulation results show that the proposed policy results in a lower violation probability and a lower cost.

Effective communication service for a Virtual Power Plant (VPP) is a major challenge as a VPP has very stringent latency and reliability requirements for communication with the Distributed Energy Resources (DERs). Network slicing is a promising solution to this challenge as network slices can be customized and provisioned to satisfy the requirements of the VPP's communication traffic. While determining the resources required for the network slice, it is important to consider the location of the VPP server and the paths along which the traffic is routed as significant latency can be experienced if the DERs are spread across a large area. The server's placement and traffic paths chosen can also impact the cost incurred as it would impact the required bandwidth on the links. We formulate a VPP Server Placement and Provisioning Problem that determines i) placement of VPP server, ii) paths for traffic distribution and iii) required bandwidth on each link in a wide area network connecting the DERs and the server while satisfying latency constraints and minimizing cost of reserving bandwidth on the links. We develop a heuristic algorithm for the formulation to obtain an effective solution in reasonable time. We also present a modified, convex version of the formulation under certain constraints through which an optimal solution can be obtained. We demonstrate the performance of our heuristic algorithm by comparing it with the optimal solution for the convex problem and another heuristic approach.

Recent rise in the mobile network communication vulnerabilities highlights the need for systematic security testing frameworks for communication protocols. In this paper, we propose a real-time framework to fully manipulate the 4G and 5G data-link and network communication to the base station (eNB/gNB). This is for experimenting and testing the security of data-link protocols such as Media Access Control (MAC), Radio Link Control (RLC), Packet Data Convergence Protocol (PDCP) and network protocols such as Radio Resource Control (RRC) and Non-access stratum (NAS). Although we focus on the base station, our framework is equally applicable for manipulating the communication to the user equipment (UE). An appealing feature of our framework is that it automatically constructs the protocol state machine during normal communication. This allows us to validate the response from the base station when it is subjected to unexpected packet sequences. Our framework also exposes an application programming interfaces (APIs) for designers to install custom attack scenarios. We have implemented our framework and used it to generate several (adversarial) scenarios that include injection of malformed and out-of-order packets as well as flooding certain packets. Our evaluation revealed crashes in OpenAirInterface (OAI) UE and gNB, as well as in Open5GS core network. Additionally, we guide our validation via the automatically constructed state machine and have caught most adversarial scenarios during our evaluation. We envision our proposed framework to provide the foundation for automated security testing of 4G/5G data-link protocol implementation.

Detecting anomalies in key performance indicator (KPI) is crucial for cellular network management. With the 5G deployment, the network is becoming much more complicated, making manual anomaly detection tedious or even impossible. In this paper, we propose LSTM-GAN-G for anomaly detection in cellular KPI time series. We build a deep generative adversarial network (GAN) with long short term memory (LSTM) cells to better capture temporal characteristics of the input data. The LSTM-GAN is trained with purely normal data so that the trained generator learns the distribution of the normal cellular KPI time series. During anomaly detection, the trained generator (G) is exploited to detect anomalies in testing samples by computing the difference between the testing sample and the generator reconstructed samples. Additionally, we propose to decompose the input time series and feed the de-seasoned data to the LSTM-GAN to further improve the anomaly detection performance. The proposed algorithm is evaluated on a real-world cellular KPI dataset. Our results show that the proposed method is able to detect both point anomaly and segment anomaly accurately, and significantly outperforms benchmark algorithms.

In this work we investigate the design of energy-optimal policies for ultra-reliable low-latency communications (URLLC) over a non-stationary wireless channel, using a contextual reinforcement learning (RL) framework. We consider a point-to-point communication system over a piece-wise stationary wireless channel where the Doppler frequency of the channel switches between two distinct values, depending on the underlying state of the channel. To benchmark the performance, first we consider an oracle agent which has a perfect but causal information about the switching instants, and consists of two deep RL (DRL) agents each of which is tasked with optimal decision making in a unique partially stationary environment. Comparing the performance of the oracle agent with the conventional DRL reveals that the performance gain obtained using oracle agent depends on the dynamics of the non-stationary channel. In particular, for a non-stationary channel with faster switching rate the oracle agent results in approximately 15 − 20% less energy consumption. In contrast, for a channel with slower switching rate the performance of the oracle agent is similar to the conventional DRL agent. Next, for a more realistic scenario when the information about the switching instants for the Doppler frequency of the underlying channel is not available, we model the non-stationary channel as a regime switching process modulated by a Markov process, and adapt the oracle agent by aiding a state tracking algorithm proposed for the regime switching process. Our simulation results show that the proposed algorithm yields a better performance compared to the conventional DRL agent.

We evaluate the performance of machine learning method in identifying the channel type in 802.11ac systems. It is shown that the reference symbols contained in the packet preamble can be used as a good feature for classification. In addition, the time-domain received preamble also serves as a good classification feature with comparable performance, allowing early identification of channel type since the information can be tapped closer to the receiving antenna. We validate our approach in both software and hardware simulation, and classification accuracy of more than 94% is shown to be attainable at moderate Signal to Noise Ratio (SNR). Finally, we also evaluate the application of our proposed algorithm into link adaptation in 802.11ac systems, and show that up to 1.6dB gain can be achieved compared to the case without channel type identification.

In IIoT application, various IoT devices with different protocols are connected. One of the major challenges in IIoT application is to detect interference and achieve coexistence across multiple technologies. In this paper, we propose a novel and efficient method to detect the presence of Microwave Oven (MWO) interference. In order to achieve coexistence between IEEE 802.11 WIFI and IEEE 802.15.4 based sensor network, we propose an enhanced spectral matching method to detect 802.15.4 packet and WIFI packet. Our proposed method can also detect the hopping frequency of 802.15.4 packet. Simulation results show that our proposed method can achieve high detection probability in various propagation channels.

Target coverage and connectivity are two main challenging and important issues in wireless sensor networks. The former is for providing sufficient monitoring quality where all points of interest in the network are covered by sensor nodes and the latter is for guaranteeing satisfactory communicating capability where all sensors can connect to at least one sink via relay nodes. In this paper, we focus on minimizing the number of nodes (i.e., sensor nodes and relay nodes) to provide target coverage and connectivity in wireless sensor networks with multiple sinks. We formulate the problem as two sub-problems. The first one (named as TC) is for placing sensor nodes to cover all targets and the second one (named as NC) is for placing relay nodes to connect sensor nodes to the sinks. We then propose a heuristic algorithm for the TC problem that exploits clustering technique. We also propose two heuristic algorithms for the NC problem that base on greedy approach and spanning tree. The experiment results show that our protocols can significantly reduce the number of required nodes in comparison with existing protocols.

We study the application of machine learning to channel classification for identifying whether a channel belongs to the Line of Sight (LOS) or Non-Line of Sight (NLOS) classes. The machine learning approach is able to work on multiple features, resulting in a much more accurate pattern identification and classification performance. We show that even in the absence of channel estimation, it is possible to classify the channel using the received preamble sequence with machine learning. This allows quicker classification and it is robust to channel estimation error, which is favorable in the low Signal to Noise Ratio (SNR) regime. The scheme is evaluated for IEEE 802.11ad systems, but the concept is also applicable to other wireless systems in general.

In this paper we propose a Hybrid Group Paging scheme to overcome radio access networks (RANs) overload problem due to simultaneous access from massive number of machine-type communications (MTC) devices. Compared to the existing methods, the proposed scheme supports dynamic priority assignment among different sub-groups by combining features from both pull-based and push-based approaches. The performance of the proposed scheme is analyzed using recursive contending user estimation (RCE) method, and shown to agree well with the simulation results.

Massive multiple input multiple output is a promising technology to keep up with the explosive demand of wireless data traffic. The benefits of having a large number of antennas, however, depend on the availability of channel state information (CSI), especially at the transmitter. In frequency division duplex systems, this CSI has to be sent back via the uplink channel, hence incurring a large overhead and degrading the spectral efficiency. Mobility of the users and the large number of antennas exacerbate the problem with frequent tracking of the many timevarying CSI coefficients. This paper presents one approach to address this issue. By tracking only the principal components of the channel gain, and exploiting the wide-sense stationarity of the channel, the amount of required feedback can be reduced significantly. Simulation study shows that the proposed technique is able to achieve high sum-rate with good tracking capability using only limited feedback.

Tracking of items on shelves is an important but time-consuming task in inventory control. In particular, books in public libraries are frequently borrowed and returned, even misplaced, and proves a challenge to be tracked on a daily basis. To track the books efficiently, we propose the Autonomous Robotic Shelf Scanning (AuRoSS) system. This paper elaborates on the key enabling robotic technology for the fully autonomous system, namely, a navigation system with surface tracking capability. A Radio Frequency Identification (RFID) reader is carried by the navigation system to identify the RFID tags embedded in each book. Based on the tag information, a tracking report that highlights missing and misplaced books is generated for the end users. To ensure successful identification, the surface tracking requires high accuracy, for which we propose a filtered Hough transform and a macro-mini manipulator structure. Tests of the AuRoSS system in a library show high accuracy in the scanning performance.

This paper studies improper Gaussian signaling in the Z-Interference-Channel (ZIC) for the case when the interference is treated as noise. The extra degree of freedom from improper signaling provides better interference management, which balances the trade-off between maximizing the direct link throughput and controlling the effect of interference, hence giving better achievable rate than the proper signaling counterpart. In this work, a closed form solution to the sum-rate maximizing real-composite transmit covariance matrix is derived for the ZIC. The structure of the real-composite covariance matrix reveals some interesting insights, such as the optimality of binary power control, the channel condition when improper signaling is strictly better than the proper signaling strategy, as well as the relationship between the choice of the real-composite transmit covariance matrix and the system parameters. The connection between the results obtained in this work and those available in the literature are also pointed out.

We analyze the level crossing rate and the average fade duration of amplify and forward multihop relay networks. We first calculate exact closed-form expressions for these statistics when the individual links are affected by log-normal fading with arbitrary autocorrelation. Based on these expressions, we formulate a log-normal approximation to the product of N Nakagami-m independent random processes. In particular, through a proper matching of the mean, variance and the autocorrelation function, we show that the product of N Nakagami-m stochastic processes can be closely approximated by a single log-normal process. This allows us to accurately approximate the crossing statistics for the cascaded Nakagami-m fading channel in closed-form. Using this analytical framework we characterize the dynamics of the equivalent multihop channel gain for different correlation models, and study the influence of the number of hops and the relay mobility in these second order statistics.

We address the problem of energy efficiency maximization for multi-antenna downlink broadcast systems. The proposed transmission algorithm calculates the optimal pre-coding matrices, power allocation, and solves the user subset selection problem that is most energy efficient while satisfying the Quality of Service constraint of the network. Both zero-forcing beamforming scheme and dirty paper coding scheme for multi-antenna broadcast transmission are considered, and it is demonstrated that significant energy efficiency improvement can be achieved when the transmit power is appropriately adjusted. In most scenarios, it is shown that the optimum energy efficiency is achieved when the base station is transmitting below its maximum throughput capability, hence reducing its transmit power.

The impact of cognitive radio techniques on the energy efficiency of a downlink cellular system in which multiple relays assist the transmission of the base station toward multiple receivers is studied. In particular, the fundamental tradeoff between the power consumption at the base station and the level of cooperation at the relay nodes is investigated. By increasing its transmit power, the base station can distribute the same message to multiple relays. In turn, the common knowledge at the relays enables cooperation, which results in a reduction in the power consumption due to interference management and coherent combining gains. This implies that the overall power efficiency can potentially be improved by an increase in the power consumption at the base station. We employ an information-theoretical analysis of the attainable power efficiency based on the chain graph representation of achievable schemes. This novel theoretical tool uses a graphical Markov model to represent coding operations and allows for the automatic derivation of achievable rate regions for general networks. This approach provides an effective tool to analyze the relationship between the energy consumption at the base station and power savings provided by relay cooperation through the use of transmission strategies such as superposition coding, interference decoding and rate-splitting. We present numerical evaluations for the scenario in which two relay nodes aid the communication between the base station and three receivers. These evaluations show that cooperative strategies at the relays provide clear advantages as compared to the non-cooperative scenario for varying channel conditions and target rates.

We study transmit power minimization in the two-user Z Interference Channel (ZIC). When the interference link gain is strong, the capacity of the ZIC has been fully characterized. For this strong interference regime, we derive the closed-form solution of the minimum required transmit power to achieve a given rate pair, and show that the resulting power allocation between the two users is not necessarily unique. When the interference link gain is weak, the capacity of the ZIC is still an open problem to date. For this weak interference regime, we develop an inner and outer bound for the required transmit power to achieve a given rate pair, and characterize the constant power ratio relation between the two bounds. In contrast to the strong interference case, rate splitting is necessary for power minimization in this regime. The optimal power-minimizing rate splitting solution is then derived, and performance in terms of total transmit power required for a given rate pair is analyzed.

We analyze the level crossing rate and the average fade duration of multihop amplify-and-forward relay networks. We obtain exact closed-form expressions for these statistics when the individual links are affected by log-normal fading with arbitrary autocorrelation. Using this analytical framework we characterize the dynamics of the equivalent multihop channel gain for different correlation models, and study the influence of sampling time, the number of hops and the relay mobility in the investigated scenarios.

A downlink cellular system in which multiple relays assist the transmission of the base station is considered. Cooperation strategies based on superposition coding are derived for this network. Superposition coding is attained by sending each message to one or more relays while satisfying the rate constraints of the base station-to-relay links. The chain graph representation of achievable schemes is used to maximize the network throughput over the set of feasible transmission strategies based on superposition coding. Rate advantages as compared to the non-cooperative scenario are obtained under varying relay positions and available power at the base station and relay nodes.

Rateless codes have been found to be particularly attractive for decode and forward based relaying strategy in delay tolerant multihop networks. The latency performance of such networks is dependent on the worst links that results in the starvation of subsequent nodes with good channel conditions. The total delay suffered by such networks can be constrained by limiting the number of rateless coded transmissions, especially for applications with critical latency requirements. However, the performance of rateless codes deteriorates in such circumstances due to the lack of sufficient mutual information for successfully recovering the entire source packets. The fraction of source packets that can be recovered will depend on the encoded packets received across the transmission channel. This paper investigates the degradation in the performance of such rateless coded networks by deriving the average packet recovery rate. In order to improve the reliability in such delay constrained networks, a novel spectrally efficient transmission scheme for reliable multihop data transfer is proposed. The proposed scheme exploits the broadcast nature of wireless transmissions, which provides an inherent implicit feedback channel, to ensure the reliable delivery of information packets to the nodes in the network. Rather than allocating dedicated channels to feedback the packet recovery information, the implicit feedback channel determines such information which enhances the spectral efficiency. Further, the optimum number of packets recoverable within the specified delay constraint to reduce the reprocessing of lost packets across hops is analytically analysed in this paper.

The energy consumption of different cellular network architectures are analyzed. In particular, a comparison of the transmit energy consumption between a single large cell with multiple co-located antennas, multiple micro-cells with a single antenna at each cell, and a large cell with a distributed antenna system are presented. The influence of different system parameters such as cell size, spatial distribution of the users, and the availability of channel state information (CSI) toward the total required transmit energy are analyzed. It is shown that the current macro-cellular architecture with co-located antennas has poor energy efficiency in the absence of CSI, but has better energy efficiency than small cells when perfect CSI is available. Moreover, macro-cells with distributed antennas have the best energy efficiency of all three architectures under perfect CSI. These results shed light on design guidelines to improve the energy efficiency of cellular network architectures.

The cognitive interference channel models cognitive overlay radio systems, in which cognitive radios overhear the transmission of neighboring nodes. For the Gaussian case capacity is known in three subsets of the parameter space: the “weak interference”, “very strong interference” and “primary decodes cognitive” regime. Capacity in the “very strong interference” regime is achieved by superposing the cognitive message over the primary message while in the “primary decodes cognitive” regime the cognitive message is binned against the primary message. This paper provides a new capacity result obtained by combining the capacity achieving schemes in these two regimes thus generalizing and extending these results. Interestingly, the capacity achieving strategy for a given channel also depends on the level of cooperation among the users: that is, either superposition coding or binning is employed depending on the amount of power allotted by the cognitive transmitter to aid the primary user.

We study a low-complexity practical quantize-and-forward (QF) scheme, a special case of the compress-and-forward (CF) transmission for half-duplex relay systems. By adjusting the number of quantization levels, we show that significant performance improvement can be achieved even though the relay node does not exploit any correlation information with the signal received at the destination. We demonstrate the performance of our scheme using 16QAM as the modulation and Turbo Code as the channel coding, and compare its performance against the decode-and-forward (DF) scheme. An approximately 2 dB gain is shown to be achievable at a target error rate of 10-4in the scenario where the relay is close to destination.

When a message is forwarded through a network, a node will have multiple opportunities to reliably receive the message and cooperate in transmission. However, the transmit power of each node in a network is limited and so is the transmission range. Therefore, multihop transmission is a potential technique to deliver the data over large distances. Rateless code properties ideally suit decode and forward based relaying strategies as it improves the outage and bandwidth efficiency. In decode and forward based multihop networks, the messages are decoded and relayed over sequential point-to-point communication links. In a conventional multihop network, the intermediate nodes will have to wait indefinitely to correctly decode the information. To quantify the impact of this unknown waiting time, this paper analyses the performance of a multihop network with strict delay constraints. The average packet loss for such a network is obtained as a function of the total delay. By optimizing the total delay based on the performance across each links, the average packet loss can be minimized.

This paper evaluates the performance of FASTAR code in Multimedia Broadcast Multicast Service (MBMS) System. It is demonstrated that FASTAR code, which is optimised for erasure channel through its overhead minimising degree distribution, can improve the system performance compared to the conventional systematic Raptor code. The system is simulated under the Evolved UMTS Terrestrial Radio Access (E-UTRA) network environment according to 3GPP LTE-A specifications. The extent of the improvement and its relation to the system parameters are also studied.

Cooperation among nodes in a network provides spatial diversity as well as it enhances the system's overall capacity and coverage. However the benefits provided by such systems depends on the level of cooperation (based on the relay information quality) and on the coordination among nodes. One way of achieving proper coordination is through the use of feedback channels. An implicit feedback channel available during relaying has been determined to minimize the feedback among cooperating nodes. In addition, it improves the reliability of information decoding at the destination by mitigating erroneous relay transmissions. The present paper studies the outage analysis of a cooperative transmission scheme assisted by the implicit feedback channel. The analysis is performed in a delay constrained network employing rateless codes. Further, a performance improvement of the order of 2.5 dB is observed for such networks.

This paper presents the outage probability analysis of Joint Channel and Network Coding (JCNC) scheme in Multiple Access Relay Channel (MARC). In particular, the effect of using different interleaver patterns at different users is analysed. The motivation of this work is to show that despite its capability of achieving up to 2 dB gain in Packet Error Rate (PER), the corresponding outage probability improvement is only minimal (upper bounded at approximately 0.6 dB). This signifies that the improvement achieved by using different interleaver patterns is largely attributed by the ability of iteratively decodable codes to perform closer to the capacity limit compared to the conventional convolutional code with Soft Input Soft Output (SISO) decoder.

In this paper, a novel transmission scheme which incorporates network coding into rateless transmission over relay networks is proposed. This technique allows partial information about the upcoming message block to be transmitted during the current block transmission, hence reducing the required number of transmissions and improving the spectral efficiency. Different techniques for applying network coding to several fading models are discussed, and their relationships to an erasure channel model is explained. An optimisation of the network coding weight is given, and we show that by choosing the weight that minimizes the duration of the current block transmission and sending sufficient information about subsequent block, we can improve the overall performance.

In this paper, we propose a technique to improve the performance of Joint Channel and Network Coding (JCNC) scheme in Multiple Access Relay Channel (MARC). This technique is motivated by the observation that decoding quality is improved when channel output can be processed iteratively. Using different permutation patterns for different users, we enabled more scenarios that destination can apply iterative decoding, thereby achieving an improvement in the overall performance. The proposed scheme is evaluated for Gaussian channels and Rayleigh fading channels, and it is shown to perform well in both cases. A comparative study is also given, and it is shown that up to 3dB and 6dB gain from the original JCNC scheme is achievable in AWGN and Rayleigh fading channels respectively.

In wireless networks, designing transmission schemes adapting to time varying channel conditions is key to achieve better spectral efficiency. The design of such schemes for multi-hop wireless networks requires coordination among nodes in the network, which is achieved through feedback between nodes. Spectral efficiency can be improved for such networks by designing transmission schemes with limited feedback. Incremental code relaying (ICR) with implicit feedback proposed previously is one that mitigates the use of feedback link. In this paper, location-aware ICR with power allocation is proposed that dynamically makes the routing decisions based on the link that offers the maximum capacity. Theoretical upper bounds for bit-error rate (BER) is derived and is verified by the simulation results, yielding further insight to the proposed scheme.

In this paper, an incremental redundancy based cooperative coding scheme for wireless networks is proposed. User cooperation is an effective way of mitigating fading where one user cooperates with another user in transmitting its information, thereby providing spatial diversity. Wireless transmissions are broadcast by nature and this provides a new dimension to the system in terms of an implicit feedback channel from the relay to the source. To exploit the implicit feedback channel a novel incremental redundancy based transmission scheme called incremental code relaying with implicit feedback (ICRIF) is proposed. This scheme enhances the reliability of information transmitted by the relay and thereby improving the throughput at the destination. By employing a powerful code such as low-density parity-check (LDPC) code, the benefit of the proposed scheme is clearly established. The simplified error probability for correct reception of the proposed scheme is derived with respect to direct transmissions and transmissions assisted by the relay.

In this paper, a transmission strategy of fountain codes over cooperative relay networks is proposed. When more than one relay nodes are available, we apply network coding to fountain-coded packets. By doing this, partial information is made available to the destination node about the upcoming message block. It is therefore able to reduce the required number of transmissions over erasure channels, hence increasing the effective throughput. Its application to wireless channels with Rayleigh fading and AWGN noise is also analysed, whereby the role of analogue network coding and optimal weight selection is demonstrated.

In this paper, two distributed transmission schemes, namely Distributed Transmitter Maximal Ratio Combining (D-TxMRC) and Distributed Space Time Block Coding (D-STBC) are analysed. The array and diversity gain inherent to the underlying Multiple Input Multiple Output (MIMO) technique are studied, both under independent and correlated fading scenario. It is shown that D-TxMRC is capable of achieving high array gain at the expense of complexity, while only limited array gain is attained using D-STBC scheme. As far as diversity gain is concerned, both schemes achieve full diversity in the order of total number of cooperating nodes under independent fading. The overall performance of the schemes under random network scenario is analysed using average outage probability as a performance metric. The averaging encompasses channel variation as well as network dependent parameters. Effects of different network parameters towards system performance are also analysed. Finally, the performance of cooperative schemes in a more practical network with regular node placements are also studied.

In this paper, a low complexity antenna selection scheme for multi-antenna broadcast systems is developed. Using sum-rate capacity maximisation as the selection criteria, the proposed scheme exploits the duality principle of broadcast and multiple access channel in order to reduce the computational complexity. The performance of the proposed scheme under i.i.d. Rayleigh fading channel is then evaluated via Monte Carlo simulation under different system parameters, and its performance is compared to the full complexity brute force scheme. It is demonstrated that the scheme is able to achieve most of the gain available in the system with a considerably lower complexity.

In this paper, transmission of fountain code over cooperative networks is considered. While it is known that fountain code is optimal for sending data to multiple receivers, extra care is needed when it is used in cooperative networks. On one hand, the presence of relay node can help to improve the overall performance. On the other hand, multiple relay nodes introduce the problem of mutual interference, which can potentially degrade the performance. This work proposes an amplitude modulation scheme to address the above issue. By appropriately scaling the output signal at each node according to their unique identifier, separability of data from several transmitters at the destination can be achieved. A numerical analysis highlighting the advantage of the proposed scheme is then presented.

In this paper, the application of rate-compatible punctured convolutional (RCPC) code into coded cooperation relay system is studied. As opposed to conventional coded cooperation systems, RCPC is attractive for its better spectral efficiency. In addition, the broadcast nature of relay transmission provides a new dimension to the system in the form of implicit feedback channel from relay to source. This allows the source node to justify the quality of information bits at relay node and react accordingly by taking over relay's role to transmit the additional parity. At the destination, selection code combining is then performed, which provides a diversity benefit to the underlying RCPC code. The performance of the proposed scheme in terms of the number of correctly decoded bits (goodput), bit error probability, and the required number of slots for message transmission are then analysed. Extension to the case where there are multiple relays to assist the transmission is also discussed.

In this paper, antenna selection algorithm for multiuser downlink systems where each terminal is equipped with multiple antennas is proposed. As opposed to point to point systems, the broadcast nature of the transmission complicates the selection process as the selection criteria must take into consideration performances of all users collectively. Moreover, computational complexity required needs to be kept as low as possible for practical reason. Exploiting duality between broadcast and multiple access channel, the proposed scheme provides a heuristic approach to selection process in order to maximise the sum-rate capacity of the system, and it is shown that a close to optimal performance can be attained at a considerably lower complexity.

This paper analyses the effect of relay node and the importance of power allocation scheme in cooperative relay networks. It is shown that adding relay node alone is not sufficient to improve system performance. Under a simplest scenario where relay node adopts decode and forward relaying, direct transmission may perform better under certain cases with equal total power constraints. Furthermore, in practical scenario when source node has to transmit its message to relay node before cooperatively transmitting it to destination, the existence of relay node may even degrade system performance. By adopting the right relaying technique and proper power allocation scheme, it is shown that cooperative relay transmission can be made strictly better than direct transmission.

This paper evaluates the performance of distributed space time coding in generic ad-hoc network scenario. The condition where multiple nodes (confined within a certain geographical area) wanting to exchange information to one another is considered. Effect of different system parameters such as total number of nodes, path loss exponent, node density, and error threshold, toward overall system performance are analysed.

The present paper proposes a new relaying scheme called successive dirty paper coding. Theoretical achievable rate of the scheme is derived and its performance is compared to other existing relaying schemes in terms of their outage probability. It is shown via numerical studies and supported by simulation analysis that the proposed scheme is able to achieve higher bandwidth efficiency by reusing relay slots to transmit new messages. An improvement of up to M-fold, where M is the average number of relay stages in every transmission, compared to traditional non-cooperative relaying is observed.

This paper presents sub optimal method to perform antenna selection in MIMO-OFDM systems. This method allows reduction in the complexity by exploiting inter-subcarrier correlation or through disjoint selection of transmit and receive antennas. Their performances and complexities are then analysed and contrasted with respect to conventional brute force method.

A joint transmit and receive antenna selection technique for MIMO-OFDM is presented. By appropriately ordering and tabulating the minimum channel sub-matrix's sub-stream signal to noise ratio in all subcarriers, an optimal antenna subset can be obtained. This procedure can be simplified further by analysing the inter-subcarrier correlation and by exploiting group selection methods. Complexity analyses on the proposed algorithm are presented, and performance improvements achievable are demonstrated through simulation studies.

This paper presents performance evaluation of antenna selection capability proposed in IEEE 802.16e (Amendment of IEEE 802.16-2004 standard for combined Fixed and Mobile Broadband Wireless Access operation in licensed band), under 2048 points orthogonal frequency division multiple access (OFDMA) mode of operation. It is shown that channel condition plays significant role on the amount of gain achievable by the scheme. Modification to the selection method is proposed and additional gain achieved is demonstrated. Measurements under different signal to noise ratio (SNR) values for different channel models are analysed. Results of the experimental studies are then summarised