Battery health prognostics are critical for ensuring safety, efficiency and sustainability in modern energy systems. However, it has been challenging to achieve accurate and robust prognostics due to complex battery degradation behaviors with nonlinearity, noises, capacity regeneration, etc. Existing data-driven models capture temporal degradation features but often lack knowledge guidance, which leads to unreliable long-term health prognostics. To overcome these limitations, we propose KARMA, a knowledge-aware model with frequency-adaptive learning for battery capacity estimation and remaining useful life prediction. The model first performs signal decomposition to derive battery signals in different frequency bands. A dual-stream deep learning architecture is developed, where one stream captures long-term low-frequency degradation trends and the other models high-frequency short-term dynamics. KARMA regulates the prognostics with knowledge, where battery degradation is modeled as a double exponential function based on empirical studies. Our dual-stream model is used to optimize the parameters of the knowledge with particle filters to ensure physically consistent and reliable prognostics and uncertainty quantification. Experimental study demonstrates KARMA’s superior performance, achieving average error reductions of 50.6% and 33.3% over state-of-the-art algorithms for battery health prediction on two mainstream datasets, respectively. These results highlight KARMA’s robustness, generalizability and potential for safer and reliable battery management across diverse applications.

This paper investigates covert prompt transmission for secure and efficient large language model (LLM) services over wireless networks. We formulate a latency minimization problem under fidelity and detectability constraints to ensure confidential and covert communication by jointly optimizing the transmit power and prompt compression ratio. To solve this problem, we first propose a prompt compression and encryption (PCAE) framework, performing surprisal-guided compression followed by lightweight permutation-based encryption. Specifically, PCAE employs a locally deployed small language model (SLM) to estimate token-level surprisal scores, selectively retaining semantically critical tokens while discarding redundant ones. This significantly reduces computational overhead and transmission duration. To further enhance covert wireless transmission, we then develop a group-based proximal policy optimization (GPPO) method that samples multiple candidate actions for each state, selecting the optimal one within each group and incorporating a Kullback-Leibler (KL) divergence penalty to improve policy stability and exploration. Simulation results show that PCAE achieves comparable LLM response fidelity to baseline methods while reducing preprocessing latency by over five orders of magnitude, enabling real-time edge deployment. We further validate PCAE effectiveness across diverse LLM backbones, including DeepSeek-32B, Qwen-32B, and their smaller variants. Moreover, GPPO reduces covert transmission latency by up to 38.6% compared to existing reinforcement learning strategies, with further analysis showing that increased transmit power provides additional latency benefits.

Ambient backscatter communications (AmBC), where a backscatter transmitter (BT) modulates and reflects ambient signals to a backscatter receiver (BR), have been deemed a low-power communication technology for the Internet of Things. Previous work on symbol detection in AmBC assumed perfect time synchronization (TS), which is unrealistic in practice. The residual TS errors (RTSE) causepartial sample mismatch, degrading symbol detection performance. To address this, we propose a new AmBC symbol detection framework that incorporates the BT’s current and adjacent symbols, as well as channel coefficients. Using energy detector (ED) as a case study, we derive both exact and approximate bit error rate (BER) expressions. Our results show that the ED’s BER performance degrades significantly under RTSE, with the symbol detection threshold optimized under the assumption of perfect TS. We then derive a closed-form expression for a near-optimal symbol detection threshold that minimizes BER under RTSE. To estimate the required parameters for the detection threshold, we propose a novel method exploiting the attributes of the BR’s received signal samples. The analytical results are verified by simulation results.

Symbiotic Backscatter Communication (SBC) has emerged as a spectrum-efficient and low-power communication technology, where backscatter devices (BDs) modulate and reflect incident radio frequency (RF) signals from primary transmitters (PTs). While previous studies have assumed a circularly symmetric complex Gaussian (CSCG) distribution for the BD’s signal, this assumption may not be practical because the high complexity of generating CSCG signals is not supported by the low-cost BD. In this paper, we address this gap by investigating SBC for two low-complexity modulation schemes, i.e.,M-ary amplitude-shift keying (MASK) andM-ary phase-shift keying (MPSK), where BD’s signals inherently deviate from CSCG distribution. Our goal is to derive the achievable rate of the PT and BD under the MASK/MPSK and to design MASK/MPSK modulation scheme for maximizing the PT’s rate. Towards this end, we first derive the expressions of both the PT’s rate and BD’s rate. Theoretical results reveal that whether or not the BD improves the PT’s rate depends on the phase of MASK/MPSK modulation, while the BD’s rate is independent of this phase. We then formulate two optimization problems to maximize the PT’s rate by adjusting the phase under the MASK and MPSK modulation schemes, respectively, and derive the optimal phases for each modulation scheme in closed forms. Given that the optimal phase is continuous and thus impractical for real-world BDs, we also propose a practical circuit design that enables BDs to select a discrete phase close to the theoretical optimum. Simulation results demonstrate that the optimal phase of MASK/MPSK can ensure an improvement in the PT’s rate and the performance gain between the ideal continuous phase and the practical implementation with few discrete phases is negligible, and reveal that a low-order ASK modulation is better than a low-order PSK for the BD in terms of improving PT’s rate, especially when the direct link is not significantly weaker than the backscatter link in SBC.

Data freshness, measured by Age of Information (AoI), is highly relevant in networked applications such as Vehicle to Everything (V2X), smart health systems, and Industrial Internet of Things (IIoT). However, freshness alone does not always equate to utility in decision-making. In decision-critical settings, somestaledata may be more valuable thanfreshupdates. Motivated by this, we move beyond AoI-centric policies and investigate how datastalenessaffects remote decision-making effectiveness under random delay and limited communication resources. To this end, we propose AR-MDP, an Age-aware Remote Markov Decision Process framework, which co-designs optimal sampling and remote decision-making under a sampling frequency constraint and random delay. To efficiently solve this problem, we design a newtwo-stagehierarchical algorithm, namely Quick Bellman-Linear-Program (QUICKBLP), where the first stage involves solving the Dinkelbach root of a Bellman variant and the second stage involves solving a streamlined linear program (LP). For the tricky first stage, we propose a new One-layer Primal-Dinkelbach Synchronous Iteration (ONEPDSI) method, which overcomes there-convergenceandnon-expansive divergencepresent in existingper-samplemulti-layer algorithms. Through rigorous convergence analysis of our proposed algorithms, we establish that the worst-case optimality gap in ONEPDSI exhibits exponential decay with respect to iterationKat a rate ofO( 1/RK). Throughsensitivity analysis, we derive a threshold for the sampling frequency, beyond which additional sampling does not yield further gains in decision-making. Simulation results validate our analyses.

With the emerging privacy sensitive applications, anonymity is recognized as an important attribute in privacy-preserving communications. Existing anonymous precoding approaches at the physical (PHY) layer aim to mask sender’s channel characteristic while overlooking the fact that other PHY characteristics contain information that can be traced back to the sender. In this paper, we first reveal that the sender’s in-phase and quadrature-phase imbalance (IQI) characteristic of its radio frequency (RF) front end can also be exploited as unique signature of the sender. Since the signal is transmitted through the RF front end and then is propagated through the channel, the received signal carries the composite characteristics of the IQI and channel which can be exploited to identify the sender. To prevent the sender detection enhanced by IQI, an IQI aware anonymous alias sender construction scheme is proposed with the concept of Kullback-Leibler divergence (KLD). It manipulates the signaling of transmitted signal, so that from the perspective of detector, the detection statistic of the real sender is close to that of the alias. To mitigate the communication performance loss caused by the IQI, we exploit the IQI interference as a constructive element. While the constructive IQI design increases the degree of freedom of precoding design, it does not violate the sender anonymity requirement. Finally, an IQI aware anonymous precoder (IAA) is proposed. Simulation demonstrates that the anonymity and communication performance of the proposed IAA precoder is maintained at a high level and is robust to the IQI parameters, where existing approaches fail.

In opportunistic cognitive radio networks, when the primary signal is very weak compared to the background noise, the secondary user requires a long sensing time to achieve reliable spectrum sensing, leaving little time for secondary transmission. To tackle this issue, we propose an active reconfigurable intelligent surface (RIS)-assisted spectrum sensing system, where the received signal strength from the target primary user can be enhanced and underlying interference within the background noise can be mitigated. In comparison with the passive RIS, the active RIS not only adjusts the phase shifts of the reflecting elements but also amplifies the incident signals. Notably, we study the optimization of the reflecting coefficient matrix (RCM) to improve the detection probability given a maximum tolerable false alarm probability and limited sensing time. Then, we show that the formulated problem can be equivalently transformed into a weighted mean square error minimization problem using the principle of the weighted minimum mean square error (WMMSE) algorithm, and an iterative optimization approach is proposed. In addition, to fairly compare passive RIS and active RIS, we study the required power budget of the RIS to achieve a target detection probability under a special case where the direct links are negligible and the RIS-related channels are line-of-sight. The conclusions drawn from this special case are further validated through simulations under more general channel conditions. Furthermore, the effectiveness of the WMMSE-based RCM optimization approach is demonstrated via extensive simulations. The results also reveal that the active RIS can outperform the passive RIS when the interference is relatively weak, whereas the passive RIS performs better in strong interference scenarios due to its ability to support a large number of reflecting elements under the same power budget.

Performance evaluation of particular channel coding has been a significant topic in coding theory, often involving the use of bounding techniques. This paper focuses on the new family ofcapacity-achievingcodes, Spinal codes, to provide a comprehensive analysis framework to tightly upper bound the block error rate (BLER) of Spinal codes in the finite block length (FBL) regime. First, we resort to a variant of theGallager random coding boundto upper bound the BLER of Spinal codes over the fading channel. Then, this paper derives a new bound without resorting to the use ofGallager random coding bound, achieving provable tightness over the wide range of signal-to-noise ratios (SNR). The derived BLER upper bounds in this paper are generalized, facilitating the performance evaluations of Spinal codes over different types of fast fading channels. Over the Rayleigh, Nakagami-m, and Rician fading channels, this paper explicitly derived the BLER upper bounds on Spinal codes as case studies. Based on the bounds, we theoretically reveal that thetail transmission pattern(TTP) for ML-decoded Spinal codes keeps optimal in terms of reliability performance. Simulations verify the tightness of the bounds and the insights obtained.

Mutualistic symbiotic radio (MSR) that allows a primary link and a backscatter link to share resources and benefit each other has been investigated mainly for long-packet communications. In the Internet of Things (IoT), where short packets are often transmitted, the design of MSR needs to consider the distinct features of short packet transmissions. In this work, we study a multi-IoT-node MSR network supporting both long and short packet transmissions, where a primary transmitter sends long packets to a cooperative receiver (CR), while multiple IoT nodes sequentially backscatter their short packets to the CR. We first derive the lower bound expression for the primary transmission rate by considering the error probabilities (EPs) of the IoT nodes’ short-packet communications. On this basis, we propose a resource allocation scheme to maximize the weighted sum throughput of all IoT nodes, subject to the constraints on the quality-of-service requirement and energy causality for each IoT node, as well as the throughput gain for the primary transmission. Specifically, the weighted sum throughput maximization problem is formulated by jointly optimizing the short-packet blocklength, power reflection coefficient and EP of each IoT node, and is solved by proposing an iterative algorithm based on the block coordinate decent method. Simulation results confirm the quick convergence of the proposed iterative algorithm and show that our proposed scheme outperforms the existing scheme in terms of the weighted sum throughput of all IoT nodes.

Pilot plays a vital role in acquiring channels, but leading to high pilot overhead in a short packet that includes a pilot part and an effective blocklength part. The removal of pilots for channel estimation is an effective way to reduce age of information (AoI) in short-packet transmissions, by increasing transmission power in effective blocklength and reducing the optimum blocklength, given the total fixed transmission power. We investigate spatial correlation to minimize AoI for unmanned aerial vehicle (UAV)-enabled wireless data collection and wireless power transfer (WPT) in short-packet transmissions, by reducing the number of pilots as much as possible, while guaranteeing block error rate (BLER) performance. A number of sensors in proximity are organized into a cluster. Only the reference sensor needs pilots to estimate channel, while the other sensors utilize spatial correlation to estimate their channels in a cluster requiring no pilots. Channel estimation error is introduced, and considered in BLER and in cluster region determination via elevation angle of UAV. The proposed approach tolerates the variance of channel estimation error up to 0.9, and in such case still provides AoI performance better than the existing work where each sensor requires pilots to estimate channels.

Caching is crucial in hybrid satellite-terrestrial networks to reduce latency, optimize throughput, and improve data availability by storing frequently accessed content closer to users, especially in bandwidth-limited satellite systems, requiring strategic Medium Access Control (MAC) layer. This paper addresses throughput optimization in satellite-terrestrial integrated networks through opportunistic cooperative caching. We propose a joint probing and scheduling strategy to enhance content retrieval efficiency. The strategy leverages the LEO satellite to probe satellite-to-ground links and cache states of multiple cooperative terrestrial stations, enabling dynamic user scheduling for content delivery. Using an optimal stopping theoretic approach with two levels of incomplete information, we make real-time decisions on satellite-terrestrial hybrid links and caching probing. Our threshold-based strategy optimizes probing and scheduling, significantly improving average system throughput by exploiting cooperative caching, satellite-terrestrial link transmission, and time diversity from dynamic user requests. Simulation results validate the effectiveness and practicality of the proposed strategies.

This paper studies the joint beamforming and power allocation optimization in Low Earth Orbit (LEO) satellite networks with Rate-Splitting Multiple Access (RSMA), where dynamic channels and limited channel state information significantly degrade the performance of conventional optimization methods. Specifically, we formulate a sum-rate maximization problem under RSMA constraints. The decision variables include the transmit power allocated to the common and private streams, which are subject to total power and minimum user rate constraints. To solve this challenging problem, we propose STELLAR, a novel framework that employs a Large Language Model (LLM) as an intelligent decision-maker to directly generate feasible transmission strategies without requiring repeated model training. Specifically, STELLAR combines model-driven beamforming initialization with prompt-based evolutionary refinement and population updates, enabling rapid adaptation to varying channel conditions. Simulation results show that STELLAR outperforms baseline approaches, achieving superior spectral efficiency and converging within 30 iterations in a system with a 16-antenna LEO satellite and four ground stations.

The rapid advancement of AI-generated content (AIGC) is poised to reshape content delivery, by enabling AIGC capabilities at the network edge or directly on end-user devices. This will allow content requests to be satisfied with AIGC based on prompts, rather than transmitting the original content. Each option presents unique trade-offs. On-device AIGC minimizes network traffic by transmitting only prompts, but it produces lower content quality than on-edge AIGC, which supports larger AI models. AIGC on the edge, in turn, results in lower quality than the original content. Delivering the original content requires more resource on radio access (and backhaul if not cached), while on-edge AIGC consumes computing power. To optimally exploit these trade-offs, we formulate a utility maximization problem where for each content the system can opt for the original content, on-edge AIGC, or on-device AIGC, accounting for backhaul capacity, computational resources, and radio access constraints. For this discrete optimization problem, we prove that, by applying Lagrangian multipliers to the three resource constraints, the problem relaxation can be efficiently solved to optimality. We then propose a solution approach that combines the problem relaxation with an algorithm for reaching feasible solutions of the overall problem. Simulation results demonstrate that our approach outperforms the baseline strategies and the solutions are close to the global optimum.

In this paper, we investigate a novel cooperative nonorthogonal multiple access (C-NOMA) strategy for unmanned aerial vehicle (UAV)-enabled wireless communications, where UAV simultaneously works as a user and a time-division-duplex (TDD) relay in two hops. In the first hop, the cell-edge terrestrial users (TUs) transmit signals to UAV. In the second hop, UAV re-transmit TU's signals to base station (BS) together with its own signals based on C-NOMA. We propose a closed-form nearoptimal elevation angle (NOEA) based UAV's height optimization approach, where the elevation angle is utilized to derive a closedform solution to the UAV height optimization, based on the proof that the channel gain in the second hop is concave with respect to the elevation angle between UAV and BS. The proposed NOEA approach achieves near-optimal performance, while requiring no exhaustive search and no iteration. We propose a joint optimization in the TDD relay mode, referred to as JOT, to maximize TU's data rate, while guaranteeing UAV's target data rate. The proposed JOT algorithm allows a low-complexity closed-form optimal power allocation between TU and UAV in two hops, and provide TU's achievable data rate and spectral efficiency higher than the state-of-the-art methods.

Cooperative spectrum sensing (CSS) is essential for improving the spectrum efficiency and reliability of cognitive radio applications. Next-generation wireless communication networks increasingly employ uniform planar arrays (UPA) due to their ability to steer beamformers towards desired directions, mitigating interference and eavesdropping. However, the application of UPA-based CSS in cognitive radio remains largely unexplored. This paper proposes a multi-beam UPA-based weighted CSS (WCSS) framework to enhance detection reliability, applicable to various cognitive radio networks, including cellular, vehicular, and satellite communications. We first propose a weighting factor for commonly used energy detection (ED) and eigenvalue detection (EVD) techniques, based on the spatial variation of signal strengths resulting from UPA antenna beamforming. We then analytically characterize the performance of both weighted ED and weighted EVD by deriving closed-form expressions for false alarm and detection probabilities. Our numerical results, considering both static and dynamic user behaviors, demonstrate the superiority of WCSS in enhancing sensing performance compared to uniformly weighted detectors.

Accurate and real-time tracking of multiple moving targets remains a fundamental challenge in radar-based indoor sensing, particularly when the number of targets is unknown or varies over time. This paper presents a real-time motion tracking system RTCtrack that operates directly on signal variations induced by human movement. At each time step, the method extracts significant variation points relative to a static baseline and incrementally groups them into coherent trajectories without requiring prior knowledge of the number of targets. A tail-based spatial association strategy enables robust path formation, while weak or inconsistent clusters are automatically removed based on activity level. To maintain tracking stability in evolving environments, an adaptive baseline update mechanism replaces the reference signal when persistent global deviations are detected. RTCtrack is evaluated using a public indoor radar dataset involving multiple individuals moving independently. Simulation results show that the system reliably identifies and tracks multiple motion paths in real time without knowing the exact number of targets. Comparisons with video-based ground truth confirm strong spatial and temporal alignment, demonstrating its effectiveness for radar sensing and passive human motion tracking.

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines.

This paper studies time difference of arrival (TDoA)based algorithms for drone controller localization and analyzes TDoA estimation in multipath channels. Building on TDoA estimation, we propose two algorithms to enhance localization accuracy in multipath environments: the Maximum Likelihood (ML) algorithm, and the Least Squares Bancroft with GaussNewton (LS-BF-GN) algorithm. We evaluate these proposed algorithms in two typical outdoor channels: Wireless Local Area Network (WLAN) Channel F and the two-ray ground reflection (TRGR) channel. Our simulation results demonstrate that the ML and LS-BF-GN algorithms significantly outperform the LSBF algorithm in multipath channels. To further enhance localization accuracy, we propose averaging multiple tentative location estimations. Additionally, we evaluate the impact of time synchronization errors among sensors on localization performance through simulation.

In this article, we focus on the resource allocation and autonomous aerial vehicle (AAV) 3-D trajectory design for the AAV-ground base station (GBS) coordinated multipoint nonorthogonal multiple access (CoMP-NOMA) system to maximize the sum-rate of CoMP users while maintaining users’ high Quality of Service requirements. The main contributions of this article are summarized as follows: 1) with the assistance of closed-form power allocation result, a generalized joint user scheduling and power allocation (G-USPA) algorithm is proposed to derive the optimal user scheduling solution; 2) by revealing the monotone increasing relationship between the sum transmit power and the transmit rates of non-CoMP users, the optimal rate of each non-CoMP users turns out to be its inherent minimum required rate, consequently, the optimal transmit rates and power allocation of all users can also be derived; and 3) moreover, considering the Line of Sight (LoS) and non-LoS factors in the air-ground channel, the 3-D trajectory of AAV is designed based on successive convex approximation to provide a flexible user-centric service. The proposed G-USPA algorithm is compatible with the AAV trajectory design, which is optimized alternatively and can lead to fast convergence. Numerical results verify that the 3-D AAV-GBS CoMP-NOMA model and the G-USPA scheme have a superior performance in terms of total system sum rate and the sum rate of CoMP users over the non-CoMP AAV assisted nonorthogonal multiple access (NOMA) systems.

With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this article, we specifically consider Internet of Electric Vehicles (IoEV) and we categorize GenAI for IoEV into four different layers, namely, EV’s battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.

Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything (V2X) cooperative perception (CP), which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. V2X CP plays a crucial role in extending the perception range, increasing detection accuracy, and supporting more robust decision-making and control in complex environments. This article provides a comprehensive survey of recent developments in V2X CP, introducing mathematical models that characterize the perception process under different collaboration strategies. Key techniques for enabling reliable perception sharing, such as agent selection, data alignment, and feature fusion, are examined in detail. In addition, major challenges are discussed, including differences in agents and models, uncertainty in perception outputs, and the impact of communication constraints such as transmission delay and data loss. This article concludes by outlining promising research directions, including privacy-preserving artificial intelligence methods, collaborative intelligence, and integrated sensing frameworks to support future advancements in V2X CP.

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.

This paper establishes an upper bound on the block error rate (BLER) of Spinal codes, the first rateless codes proven to achieve Shannon capacity in additive white Gaussian noise (AWGN) and binary symmetric channels (BSC). Unlike the conventional reliance on the 1965 Gallager random coding bound for deriving upper bounds, as illustrated in 2016 by Yu et al., this study deviates by noting that Gallager’s bound may not adequately represent the distinct properties of specific random codes like Spinal codes and may result in loose bounding performance. We thus introduce novel techniques to refine existing results and enhance the bounding tightness. Our main results are two explicit upper bounds on the BLER of Spinal codes over the AWGN channel, accompanied by theoretical proofs that validate their tightness. Potential applications of the bounds and insights for the coding design are explored in this work.

Carrier frequency offset (CFO) and channel equalization are two critical problems for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems in ultra-reliable and low latency communication (URLLC). In this paper, we propose a semi-blind precoding aided structure that includes two CFO estimation approaches and an independent component analysis (ICA) based equalization scheme for MIMO OFDM systems in URLLC, requiring no pilots. We design a non-redundant balanced precoding strategy, killing two birds with one stone, where reference signals are superimposed into source signals to simultaneously allow CFO estimation and ambiguity elimination in the ICA-equalized signals. The proposed precoding-aided CFO estimation approach performs by maximizing a cost function formulated via the cross-correlations between the reference signal and the received signal. We further propose a low-complexity closed-form CFO estimation approach, by transforming the formulated cost function into a new expression. To maximize bit error rate (BER) performance, particle swarm optimization (PSO) is employed to perform the joint optimization of precoding constant and the number of OFDM blocks for CFO estimation, while avoiding exhaustive search. The proposed semi-blind precoding-aided structure provides a trade-off between performance, complexity and spectral efficiency for MIMO OFDM systems in URLLC.

Existing works on mutualistic backscatter communications (MBC) rely on the existence of direct links connecting both the primary user (PU) and the backscatter device (BD) to the destination node (D), rendering them ineffective when these links are blocked. While relay technology offers a solution, the performance of relay-assisted MBC and whether the backscatter link still benefits the primary link or not remain unclear. Additionally, the energy-harvesting capability of BD has been largely ignored in the study of MBC. This paper investigates a relay-assisted MBC network, where a decode-and-forward relay (R) forwards the messages of a PU and an energy constrained BD to D. We propose three forwarding schemes for R, i.e., orthogonal time-division multiple access (OMA), non-orthogonal multiple access (NOMA) with static power allocation (PA), and NOMA with dynamic PA. Considering the energy-causality constraint of BD, we derive the outage probabilities and diversity gains of the primary and backscatter links for each forwarding scheme. Computer simulation verifies the correctness of our analytical results and reveals that different from conventional MBC, in the relay-assisted MBC, whether or not the backscatter link improves the primary link’s outage performance depends on the specific forwarding scheme employed, as well as the location of R and the power reflection coefficient of BD.

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.

Ultra-Reliable Low-Latency Communication (URLLC) and Time-Sensitive Networking (TSN) are essential for enhancing 5G and Wi-Fi 6/7 to support real-time industrial automation. However, our survey shows that existing Medium Access Control (MAC) schemes still face unresolved latency issues. This paper introduces the Transmission Gating Time Hyperchannel (TGT-HC), a novel contention-free Carrier-Sense Multiple Access (CSMA) scheme driven by a per-flow Time-Aware Shaper (TAS) scheduler. Our analytical results, simulations, and prototyping with the Universal Software Radio Peripheral (USRP) demonstrate that TGT-HC achieves latency performance comparable to a First-Come-First-Served (FCFS) single server for real-time cyclic traffic, even under high frame error rates (FERs). Given its promising performance, we advocate for reconsidering contention-free CSMA as a viable MAC scheme in next-generation URLLC/TSN.

Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communication (URLLC) are two important wireless communication traffics to build a digital twin of physical reality. Therein, eMBB and URLLC traffics are to transmit high-quality sensed data and critical commands, respectively. To support these two important traffics, we develop an intelligent resource allocation mechanism. First, we model the time-frequency resource allocation as an optimization problem aiming to maximize the throughput for the eMBB traffics according to their urgency subject to the constraint on the successful transmission for the URLLC traffics. In this way, the amount of resources allocated to each traffic can be appropriately determined without causing waste in resource usage. Secondly, we propose a feasible low-complexity solution for the optimization problem by relaxing it and then applying linear programming. Thirdly, to address the possible failure of the algorithm due to the relaxation, we propose a post-processing by puncturing the resource initially allocated to eMBB traffics and thereafter reallocating this resource to URLLC traffics. By such, the characteristic of the eMBB traffic, i.e., high throughput, and that of the URLLC traffic, i.e., low latency and ultra reliability, can be achieved. We perform system-level simulations on Matlab 5 G simulation platform to evaluate the performance of the proposed mechanism under different scenarios. Simulations show that the proposed mechanism achieves better performance compared to existing schemes regarding the total eMBB throughput and URLLC failure probability on all the scenarios.

This paper investigates an unmanned aerial vehicle (UAV) assisted semantic network where the ground users (GUs) periodically capture and upload the sensing information to a base station (BS) via UAVs’ relaying. Both the GUs and the UAVs can extract semantic information from large-size raw data and transmit it to the BS for recovery. Smaller-size semantic information reduces latency and improves information freshness, while larger-size semantic information enables more accurate data reconstruction at the BS, preserving the value of original information. We introduce a novel semantic-aware age-of-information (SAoI) metric to capture both information freshness and semantic importance, and then formulate a time-averaged SAoI minimization problem by jointly optimizing the UAV-GU association, the semantic extraction, and the UAVs’ trajectories. We decouple the original problem into a series of subproblems via the Lyapunov framework and then use hierarchical deep reinforcement learning (DRL) to solve each subproblem. Specifically, the UAV-GU association is determined by DRL, followed by the optimization module updating the semantic extraction strategy and UAVs’ deployment. Simulation results show that the hierarchical structure improves learning efficiency. Moreover, it achieves low AoI through semantic extraction while ensuring minimal loss of original information, outperforming the existing baselines.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, where an RIS assists a primary transmission by passive beamforming and simultaneously acts as an information transmitter by periodically adjusting its reflection coefficients. Such RIS functions innately enable a new type of communication channel, called multiplicative multiple access channel (M-MAC), where the primary and secondary signals are superposed in a multiplicative manner. To pursue the fundamental performance limits, in this paper, we focus on characterizing the capacity region for the RIS-assisted SR system. Due to the reflection nature of RISs, the signal transmitted from the RIS elements should satisfy a passive reflection constraint. In particular, we consider two types of passive reflection constraints, one for the case that the amplitudes of the reflection coefficients are fixed but the phases are adjustable, while the other for the case that both the amplitudes and the phases can be adjusted. Under the passive reflection constraints at the RIS as well as the average power constraint at the primary transmitter (PTx), we characterize the capacity region of RIS-assisted SR when the direct link from the PTx to the receiver is blocked. It is observed that: 1) the number of sum-rate-optimal points on the boundary of the capacity region is infinite; 2) for the rate pairs with the maximum sum rate, the optimal amplitude distribution of the primary signal is a continuous Rayleigh distribution, while for the remaining rate pairs on the capacity region boundary, the optimal amplitude distribution of the primary signal is discrete; 3) when both the amplitudes and the phases of the reflection coefficients are adjusted for the RIS, the capacity region is enlarged as compared to the phase-adjusted-only case.

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.

The integration of Internet-of-Things (IoT) and the fifth-generation (5G) networks presents challenges due to low access efficiency caused by massive random access (RA) requests. To this end, both preamble parallelization (PP) and colliding preambles reuse (CPR) modes are proposed as critical RA control methods to enhance access performance. In this paper, we aim to maximize the random access efficiency (RAE) in a smart city scenario to determine the optimal control mode selection between the PP and CPR over the device heterogeneity with limited RA resources. We establish an access order-backoff window (AOBW) mapping model, where RA requirements are mapped onto the backoff time. It offers greater flexibility of backoff window size than previous work to guarantee diverse application and service requirements. Thanks to the derived closed-form expressions of the actual RAE, an RAE maximization algorithm is developed, which optimizes performance across both PP and CPR modes, achieving optimal performance in access delay and access throughput.

This paper presents an optimization approach for cooperative Medium Access Control (MAC) techniques in Vehic-ular Ad Hoc Networks (VANETs) equipped with Roadside Unit (RSU) to enhance network throughput. Our method employs a distributed cooperative MAC scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, featuring selective RSU probing and adaptive transmission. It utilizes a dual timescale channel access framework, with a “large-scale” phase accounting for gradual changes in vehicle locations and a “small-scale” phase adapting to rapid channel fluctuations. We propose the RSU Probing and Cooperative Access (RPCA) strategy, a two-stage approach based on dynamic inter-vehicle distances from the RSU. Using optimal sequential planned decision theory, we rigorously prove its optimality in maximizing average system throughput per large-scale phase. For practical implementation in VANETs, we develop a distributed MAC algorithm with periodic location updates. It adjusts thresholds based on inter-vehicle and vehicle-RSU distances during the large-scale phase and accesses channels following the RPCA strategy with updated thresholds during the small-scale phase. Simulation results confirm the effectiveness and efficiency of our algorithm.

In this paper, we consider a UAV-assisted wireless network, where a multi-antenna ground base station (GBS) provides communication services to a group of users through a set of UAVs in a disaster area using in-band wireless backhaul. We propose to optimize the multi- UAV assisted wireless network specifically focusing on the joint optimization of UAV deployments and wireless resources. This problem is complicated because the access link capacities of the network and its backhaul capacities are coupled, and the possible locations of UAVs in the airspace are infinite. We take into account the deployment cost of UAVs in the system. Our objective is to minimize the total system cost subject to the QoS requirements of users and the backhaul constraints. A block coordinate descent algorithm combined with successive convex approximation is introduced to solve the complicated problem by jointly optimizing the number of required UAVs, their locations, the UAV-user association, and the allocation of wireless resources. Simulation results show that the proposed method is adaptable to wireless backhaul constraints and achieves better performance compared to other benchmark methods.

In this paper, we propose a closed form (CF) based channel estimation approach for direct current biased optical-orthogonal frequency division multiplexing (DCO-OFDM) visible light communication (VLC) systems. This is the first work to utilize light emitting diode (LED)'s limited bandwidth to reduce the number of pilots while maintaining good performance. A comb-type pilot pattern is employed, requiring a small number of pilots on few DCO-OFDM subcarriers and blocks. It is high spectral efficiency, as a large number of pilots on all subcarriers and some blocks are not required as in a block-type pilot pattern. The proposed CF approach performs the line-of-sight (LoS) channel estimation and LED's limited bandwidth estimation in a CF via two formulated functions on two subcarriers. Simulation results show that the proposed CF approach provides bit error ratio (BER) and mean square error (MSE) performances better than the block-type pilot based methods in the literature, requiring less pilots.

Wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications without requiring any extra devices to be carried out by human users. However, previous studies have shown that wireless sensing performance can be significantly degraded if the relative locations of the transmitters, human users, and receivers are non-ideal and/or the distances between the user and receivers are large. These constraints hinder the wide applications of wireless sensing in many practical scenarios. A promising approach for wireless sensing is through the use of reconfigurable intelligent surfaces (RISs) that can control the propagation environment to create a customizable wireless environment for wireless sensing. To this end, in this paper, the use of RIS-assisted wireless sensing for enhanced human gesture recognition is investigated. A novel RIS-assisted distributed wireless sensing framework that utilizes federated learning (FL) is proposed to enable collaborative model training among decentralized receivers. Then, a novel metric, called human-influencing signal-to-interference ratio (HSIR), is introduced to characterize the quality of locally recorded data as well as its impact on the performance of wireless sensing. To alleviate the model draft problem of FL-assisted wireless sensing, caused by spatial heterogeneity of the quality of wireless sensing data at different receivers, the optimal amplitudes and phases of the RIS are derived so as to improve the HSIR of a set of low-performance receivers located at non-ideal locations. Simulation results show that the proposed RIS-assisted system can significantly improve wireless sensing accuracy by up to 20.1% compared to traditional distributed system.

Age of Information (AoI) has been recognized as an important metric to measure the freshness of information. Central to this consensus is that minimizing AoI can enhance the freshness of information, thereby facilitating the accuracy of subsequent decision-making processes. However, to date the direct causal relationship that links AoI to the utility of the decision-making process is unexplored. To fill this gap, this paper proposes a sampling-control co-design problem, referred to as an age-aware remote Markov Decision Process (MDP) problem, to explore this unexplored relationship. Our framework revisits the sampling problem in [1] with a refined focus: moving from AoI penalty minimization to directly optimizing goal-oriented remote decision-making process under random delay. We derive that the age-aware remote MDP problem can be reduced to a standard MDP problem without delays, and reveal that treating AoI solely as a metric for optimization is not optimal in achieving remote decision making. Instead, AoI can serve as important side information to facilitate remote decision making.

Developing sustainable and environmentally friendly network AI solutions has attracted significant interest recently. Unfortunately, analyzing the overall environmental impact, the greenhouse emissions in particular, of a network AI implementation is known to be a notoriously challenging task. As a popular distributed AI framework, federated edge intelligence (FEI) has been promoted as a candidate technology for implementing network AI in 6G. Unfortunately, recent studies suggest that the FEI network may generate more carbon emissions than the traditional centralized AI solutions. In this paper, we propose a novel analytical framework to quantify and optimize the carbon emissions of FEI networks. We develop an analytical model for quantifying the overall carbon emissions required for constructing a shared model in FEI with the guaranteed accuracy level. We propose a server-dropping-based algorithm that removes the highest-emitting and low-contributing edge servers from participating in the model training to minimize the overall carbon emissions. We conduct extensive experiments and the experimental results show that our proposed algorithm reduces up to 80% of carbon emissions, compared to the traditional FL-based solution.

Channel knowledge map (CKM) has been envisioned as a promising technology to achieve environment-aware communications for future sixth-generation (6G) wireless networks. The angle of departure (AoD) and the angle of arrival (AoA) are crucial parameters of CKM widely used for location-specific applications. Conventional stochastic methods of char-acterization fail to correlate the AoD and AoA with location-specific transmission environments. In this paper, we propose to utilize the CKM technology to characterize the location-specific AoD and AoA, by constructing the AoD and AoA map based on sparse measurement data. We further leverage the data-driven deep machine learning (DML) technique to obtain the AoD and AoA for locations without measurements. Simulation results show that the proposed method can construct both the AoD and the AoA maps with high fidelity to the true map. Moreover, Method II which predicts AoDs and AoAs sequentially achieves better performance than the independent prediction in Method I.

Age of information (AoI) is an effective performance metric to measure data freshness in short packet communication. In this paper, we investigate AoI for decode-and-forward (DF) relay systems in the time division duplex (TDD) mode in short packet communication with a number of resources, such as blocklength, transmission power and channels. We formulate an average AoI minimization problem for DF relay systems in the TDD mode with multiple resources. We propose a joint multi-resource optimization (JMO) algorithm to minimize average AoI by simultaneously optimizing blocklength, transmission power and channels. Thus, the proposed JMO algorithm can significantly reduce average AoI, as compared to the previous work just considering blocklength optimization (BO). We prove that BO is independent of power allocation (PA) and channel allocation (CA), and can be decoupled to minimize AoI independently. Thus, the complex problem can be decomposed into a BO subproblem and a PA-and-CA subproblem. We propose a BO algorithm to successively optimize blocklengths in two hops using golden section method. We propose a joint power and channel allocation (JPCA) algorithm to further reduce AoI in an iterative manner. We propose a maximum multiplication (MM) based CA criterion, where CA is performed by maximizing the multiplication between two signal-to-noise ratios (SNRs) in two hops. Thus, the proposed MM-based CA criterion provides AoI performance better than the max-min based CA criterion only maximizing the smaller SNR in two hops.

We propose an adaptive low-complexity orthogonal matching pursuit (ALOMP) channel estimation approach for direct current biased optical-orthogonal frequency division multi-plexing (DCO-OFDM) systems in optical wireless communication (OWC), requiring a single DCO-OFDM block. This is the first investigation to utilize light-emitting diode (LED)'s limited bandwidth to reduce the high complexity of the traditional OMP method in OWC, while maintaining good channel estimation performance. A number of termination factors are designed to reduce the number of iterations, by exploring correlations between channel impulse responses (CIRs) in time domain caused by LED's limited bandwidth. The proposed ALOMP approach provides complexity close to the minimum mean square error (MMSE) method, and requires just two iterations, significantly less than the traditional OMP method with a large number of iterations. We employ OMP to estimate two CIRs in time domain, and formulate two equations. The line-of-sight (LoS) channel and LED's limited bandwidth are estimated separately via the formulated equations rather than jointly in all CIRs in time domain or on all sub carriers in frequency domain in previous works. Also, we derive the lower bound of the proposed ALOMP approach, and the theoretical bit error rate (BER) including channel estimation errors. Simulation results verify the proposed ALOMP approach.

The intricate interplay of source dynamics, unreliable channels, and staleness of information has long been recognized as a significant impediment for the receiver to achieve accurate, timely, and goal-oriented decision making. Thus, a plethora of promising metrics, such as Age of Information and Value of Information have emerged to quantify these adverse factors. Optimizing these metrics indirectly improves the goal-oriented utility of decision making. Nevertheless, no metric has been devised to directly evaluate the utility. To this end, this paper investigates a novel tensor-based metric, named Goal-oriented Tensor (GoT), to directly quantify the impact of semantic mismatches on decision making. Leveraging the GoT, we design a sampler-decision maker pair that works collaboratively to achieve a shared goal. This sampling-decision making co-design is challenging since the sampler and the decision maker are strongly coupled. To decouple these processes, we formulate the problem as an infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal joint policy. We tested the sampler-decision maker co-design in terms of goal achievement utility and sampling rate, achieving significant performance advancements over conventional state-of-the-art sampling methodologies.

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.

There has been a growing interest in using unmanned-aerial-vehicles (UAVs) for high-rate wireless communications due to their flexibility in deployment and relatively low costs. In this article, we consider an UAV-assisted wireless network, where a multiantenna ground base station provides communication services to a group of users through a set of UAVs using the in-band wireless backhaul. We propose a novel framework for optimizing the multi-UAV-assisted wireless network and consider the following two problems: one minimizes the total system cost and the other maximizes the system utility. In contrast to the previous studies, we consider the deployment cost of UAVs and the impact of the UAV locations on the backhaul capacity for both the problems. The two problems are complicated mixed-integer nonlinear programming (MINLP) problems, which involve the joint optimization of the UAV selection and their locations, the UAV-user association, and the wireless resource allocations subject to the Quality of Service requirements of users and the wireless backhaul capacity constraints. Therefore, we propose the block coordinate descent-based algorithms combined with successive convex approximation to solve the two problems. Simulation results show that the proposed methods are adaptable to the wireless backhaul constraints and outperform the other benchmark methods.

The success of the Internet of Things has recently spread to the industrial sector, commonly referred to as Industrial IoT (IIoT). IIoT, which has a far-reaching impact on the operation of industries around the world, is recognized as a key enabler for the fourth industrial revolution. It has the potential to prompt economic growth and global competitiveness, in terms of improving productivity, efficiency, and so on.

In this paper, we address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data to help creating and rendering the digital copy of the physical world in the Metaverse. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. Nevertheless, mechanisms to hire sensing IoT devices to share their data with the VSP and then deliver the constructed digital twin to the Metaverse users are vulnerable to adverse selection problem. The adverse selection problem, which is caused by information asymmetry between the system entities, becomes harder to solve when the private information of the different entities are multi-dimensional. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem. To demonstrate the effectiveness of our algorithm, we conduct extensive simulations and measure several key performance metrics of the contract for the Metaverse. Our results show that our designed iterative contract is able to incentivize the participants to interact truthfully, which maximizes the profit of the VSP with minimal individual rationality (IR) and incentive compatibility (IC) violation rates. Furthermore, the proposed learning-based iterative contract framework has limited access to the private information of the participants, which is to the best of our knowledge, the first of its kind in addressing the problem of adverse selection in incentive mechanisms.

We propose spatial superimposition (SS) structures to reduce peak-to-average power ratio (PAPR) for unipolar asymmetrically clipped optical-orthogonal frequency division multiplexing (UACO-OFDM) light fidelity (LiFi) systems with multiple light emitting diodes (LEDs) at the transmitter. The traditional μ-law companding method only increases the small amplitudes of signals, while maintaining the maximum value of the signal, which provides the limited PAPR reduction. Hence, we propose an improved nonlinear μ-law companding approach for PAPR reduction by enhancing small-amplitude signals and compressing large-amplitude signals. Linear compression is further used in the transmitted signals to reduce the impact of LED nonlinearity. Multiple LEDs are utilized to compensate for signal distortion caused by joint linear and nonlinear compressions, requiring no decompanding as in the traditional method. However, there are a few negative compensation signals that can be made to be positive by adding a small value of direct current (DC) bias theoretically derived in a closed form. Also, we propose an enhanced SS (eSS) structure, where the turn-on and maximum voltages of LED are jointly considered in the PAPR reduction, requiring no additional DC bias. This is the first work to investigate channel diversity in the proposed structures, while the multiple channels are assumed to be highly correlated with each other due to small LED separation in the previous works. We propose a frequency-domain channel filling (FCF) approach and a time-domain CF (TCF) approach, to mitigate the channel differences, which enables effective equalization of received signals. This is also the first work to investigate the analytical bit error rate (BER) of UACO-OFDM systems with clipping noise. We derive the analytical BERs of the proposed SS and eSS structures, respectively. Simulation results verify the proposed approaches.

Optimizations premised on open-loop metrics such as Age of Information (AoI) indirectly enhance the system’s decision-makingutility. We therefore propose a novel closed-loop metric named Goal-oriented Tensor (GoT) to directly quantify the impact of semantic mismatches on goal-oriented decision-makingutility. Leveraging the GoT, we consider asampler & decision-makerpair that works collaboratively and distributively to achieve a shared goal of communications. We formulate a two-agent infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal deterministic sampling policy and decision-making policy. To circumvent thecurse of dimensionalityin obtaining an optimal deterministic joint policy through Brute-Force-Search, a sub-optimal yet computationally efficient algorithm is developed. This algorithm is predicated on the search for a Nash Equilibrium between the sampler and the decision-maker. Simulation results reveal that the proposedsampler & decision-makerco-design surpasses the current literature on AoI and its variants in terms of both goal achievementutilityand sparse sampling rate, signifying progress in the semantics-conscious, goal-driven sparse sampling design.

Short frame structure and its optimization plays an important role in ultra reliable and low latency communication (URLLC). We investigate and compare the latency and throughput performances of the independent encoding (IE) and joint encoding (JE) frame structures for heterogeneous multi-device URLLC in the finite block length regime. There is a counter-intuitive finding that, despite a longer frame, IE enables a much lower average latency and higher reliability than JE, thanks to lower queuing latency, while JE achieves higher throughput with lower traffic heterogeneity, thanks to less channel dispersion. It is also shown that traffic heterogeneity has less adverse effects on the performance of the IE frame structure, and can even help reduce its average latency with the shortest block length first (SBF) scheduling rule proposed. We also provide an intensive analysis of the trade-off between pilot power and pilot overhead, with near-optimal pilot power and block length derived in closed form. Low-complexity joint pilot power, pilot length and block length optimization algorithms are proposed for IE and JE frame structures. Numerical results verify the effectiveness of the proposed algorithms, and also show that pilot power optimization plays a significant role in enhancing throughput at low to medium SNR.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) enables full-space manipulation of signal propagation. In this work, we consider a multi-STAR-RIS-assisted multiple-input multiple-output (MIMO) system for multi-users and investigate the impact of different STAR-RIS schemes on the system performance. We jointly optimize the beamforming matrix of the base station and the transmitting and reflecting coefficient (TRC) matrix of each STAR-RIS to maximize the sum rate of users. We propose a block coordinate descent (BCD) algorithm to optimize the beamforming matrix and the TRC matrices. We reformulate the optimization problem and optimize the beamforming matrix using the Lagrange duality method to reduce the computational complexity and then employ the constrained concave-convex procedure to tackle the optimization problem for the TRC matrices. When the energy splitting (ES) scheme is applied, our proposed method achieves a remarkable improvement of up to 17.97% in the user sum rate compared to the mode switching and the equal ES schemes in the multi-STAR-RIS-assisted system. Furthermore, when compared with systems assisted by multiple conventional RISs, our system can achieve a substantial gain of 38.99%. The improvement is even more pronounced compared to systems with a single STAR-RIS or a single conventional RIS.

We leverage covert communication to enhance the security of a large-scale multi-tier Low Earth Orbit (LEO) satellite network against vigilant adversarial terrestrial Base Stations (BSs) aiming at detecting satellite transmissions. This approach involves deploying massive LEO satellites at different altitudes around Earth to form a multi-tier network serving as a backhaul for near-ground Unmanned Aerial Vehicles (UAVs) that provide network services to terrestrial mobile users. Meanwhile, terrestrial BSs attempt to detect satellite transmissions based on their own received signal powers. To evade detection, the LEO satellite network performs power control to obscure the satellite transmission within the co-channel interference among the LEO satellites. We formulate a two-stage Stackelberg game to model the conflict dynamics between the terrestrial BSs and the LEO satellite network. In this game, the terrestrial BSs act as non-cooperative followers at the lower stage aiming to minimize their detection errors. On the other hand, the LEO satellite network acts as the leader at the upper stage aiming to maximize its utility while ensuring communication covertness. In contrast to existing works that focus on a small set of network nodes, our study considers a large-scale multi-tier LEO satellite network and employs stochastic geometry to model the spatial distribution of network nodes. To achieve the Stackelberg equilibrium, we develop a bi-level algorithm based on Successive Convex Approximation (SCA) and golden-section search. Our numerical results provide practical insights, revealing a trade-off in leveraging co-channel interference (i.e., while it improves the communication covertness of satellite transmission, it simultaneously degrades the link reliability).

Joint radar and communication (JRC) is a promising technique for spectrum re-utilization, which enables radar sensing and data transmission to operate on the same frequencies and the same devices. However, due to the multi-objective property of JRC systems, channel allocation to JRC nodes should be carefully designed to maximize system performance. Additionally, because of the broadcast nature of wireless signals, a watchful adversary, i.e., a warden, can detect ongoing transmissions and attack the system. Thus, we develop a covert JRC system that minimizes the detection probability by wardens, in which friendly jammers are deployed to improve the covertness of the JRC nodes during radar sensing and data transmission operations. Furthermore, we propose a robust multi-item auction design for channel allocation for such a JRC system that considers the uncertainty in bids. The proposed auction mechanism achieves the properties of truthfulness, individual rationality, budget feasibility, and computational efficiency. The simulations clearly show the benefits of our design to support covert JRC systems and to provide incentive to the JRC nodes in obtaining spectrum, in which the auction-based channel allocation mechanism is robust against perturbations in the bids, which is highly effective for JRC nodes working in uncertain environments.

WiFi-based smart human sensing technology enabled by Channel State Information (CSI) has received great attention in recent years. However, CSI-based sensing systems suffer from performance degradation when deployed in different environments. Existing works solve this problem by domain adaptation using massive unlabeled high-quality data from the new environment, which is usually unavailable in practice. In this paper, we propose a novel augmented environment-invariant robust WiFi gesture recognition system named AirFi that deals with the issue of environment dependency from a new perspective. The AirFi is a novel domain generalization framework that learns the critical part of CSI regardless of different environments and generalizes the model to unseen scenarios, which does not require collecting any data for adaptation to the new environment. AirFi extracts the common features from several training environment settings and minimizes the distribution differences among them. The feature is further augmented to be more robust to environments. Moreover, the system can be further improved by few-shot learning techniques. Compared to state-of-the-art methods, AirFi is able to work in different environment settings without acquiring any CSI data from the new environment. The experimental results demonstrate that our system remains robust in the new environment and outperforms the compared systems.

We aim to develop a system-level security solution for a large-scale device-to-device (D2D) network against adversaries based on covert communication. The D2D network underlays a downlink cellular network to reuse the cellular spectrum and is enabled for simultaneous wireless information and power transfer (SWIPT). In the D2D network, the D2D transmitters communicate with the D2D receivers, and the D2D receivers extract information and energy from their received radio-frequency (RF) signals. In the meantime, the adversaries aim to detect the D2D transmission. The D2D network applies power control and leverages the cellular signal to achieve covert communication (i.e., hide the presence of transmissions) so as to defend against the adversaries. We model the interaction between the D2D network and adversaries by using a two-stage Stackelberg game. Therein, the adversaries are the followers minimizing their detection errors at the lower stage and the D2D network is the leader maximizing its network utility constrained by the communication covertness and power outage at the upper stage. Both power splitting (PS)-based and time switch (TS)-based SWIPT schemes are explored. We characterize the spatial configuration of the large-scale D2D network, adversaries, and cellular network by stochastic geometry. We analyze the adversary’s detection error minimization problem and adopt the Rosenbrock method to solve it, where the obtained solution is the best response from the lower stage. Taking into account the best response from the lower stage, we develop a bi-level algorithm to solve the D2D network’s constrained network utility maximization problem and obtain the Stackelberg equilibrium. We present numerical results to reveal interesting insights. For example, the PS-based SWIPT scheme outperforms the TS-based SWIPT scheme in terms of both network performance (e.g., link reliability and power outage probability) and resistance to the adversary, i.e., steady network utility against increasing aggressiveness of the adversary.

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. We develop a bi-level optimization algorithm to search for the equilibrium of the proposed Stackelberg game based on the successive convex approximation (SCA) method and Rosenbrock method. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility.

Massive multiple-input multiple-output (MIMO) is a key enabling technology for next-generation communication systems. In massive MIMO cellular networks, coordinated beamforming (CBF), which jointly designs the beamformers of multiple base stations (BSs), is an efficient method to enhance the network performance. In this paper, we investigate the sum rate maximization problem in a massive MIMO mobile cellular network, where in each cell a multi-antenna BS serves multiple mobile users simultaneously via downlink beamforming. Although existing optimization-based CBF algorithms can provide near-optimal solutions, they require real-time and global channel state information (CSI), in addition to their high computation complexity. Due to the non-negligible delay of practical backhaul networks and the high-complexity optimization process, it is almost impossible to apply them in mobile cellular networks. Noting that the considered problem under the practical constraints can be modeled as a networked distributed partially observable Markov decision process, we propose a deep reinforcement learning-based distributed dynamic coordinated beamforming (DDCBF) scheme, which enables each BS to determine the beamformers with only local CSI and some historical information from other BSs. Besides, the beamformers can be calculated with a considerably lower computational complexity by exploiting neural networks and expert knowledge, i.e., a solution structure observed from the iterative procedure of the centralized optimization algorithms. Moreover, we provide extensive numerical simulations to validate the effectiveness of the proposed DRL-based approach. With lower computational complexity and less required information, the results show that the proposed approach can achieve comparable performance to the centralized iterative optimization algorithms.

In orthogonal time sequency multiplexing (OTSM) modulation, the information symbols are conveyed in the delay-sequency domain upon exploiting the inverse Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of attaining a bit error ratio (BER) similar to that of orthogonal time-frequency space (OTFS) modulation at a lower complexity, since the saving of multiplication operations in the IWHT. Hence we provide its BER performance analysis and characterize its detection complexity. We commence by deriving its generalized input-output relationship and its unconditional pairwise error probability (UPEP). Then, its BER upper bound is derived in closed form under both ideal and imperfect channel estimation conditions, which is shown to be tight at moderate to high signal-to-noise ratios (SNRs). Moreover, a novel approximate message passing (AMP) aided OTSM detection framework is proposed. Specifically, to circumvent the high residual BER of the conventional AMP detector, we proposed a vector AMP-based expectation-maximization (VAMP-EM) detector for performing joint data detection and noise variance estimation. The variance auto-tuning algorithm based on the EM algorithm is designed for the VAMP-EM detector to further improve the convergence performance. The simulation results illustrate that the VAMP-EM detector is capable of striking an attractive BER vs. complexity trade-off than the state-of-the-art schemes as well as providing a better convergence. Finally, we propose AMP and VAMP-EM turbo receivers for low-density parity-check (LDPC)-coded OTSM systems. It is demonstrated that our proposed VAMP-EM turbo receiver is capable of providing both BER and convergence performance improvements over the conventional AMP solution.

Symbiotic radio (SR) is a promising technique to support cellular Internet-of-Things (IoT) by forming a mutualistic relationship between IoT and cellular transmissions. In this paper, we propose a novel multi-user multi-IoT-device SR system to enable massive access in cellular IoT. In the considered system, the base station (BS) transmits information to multiple cellular users, and a number of IoT devices simultaneously backscatter their information to these users via the cellular signal. The cellular users jointly decode the information from the BS and IoT devices. Noting that the reflective links from the IoT devices can be regarded as the channel uncertainty of the direct links, we apply the robust design method to design the beamforming vectors at the BS. Specifically, the transmit power is minimized under the cellular transmission outage probability constraints and IoT transmission sum rate constraints. The algorithm based on semi-definite programming and difference-of-convex programming is proposed to solve the power minimization problem. Moreover, we consider a special case where each cellular user is associated with several adjacent IoT devices and propose a direction of arrival (DoA)-based beamforming design approach. The DoA-based approach requires only the DoA and angular spread (AS) of the direct links instead of the instantaneous channel state information (CSI) of the reflective link channels, leading to a significant reduction in the channel feedback overhead. Simulation results have substantiated the multi-user multi-IoT-device SR system and the effectiveness of the proposed beamforming design approaches. It is shown that the DoA-based beamforming approach achieves comparable performance as the CSI-based approach in the special case when the ASs are small.

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.

Millimeter-wave (mmWave) signals provide attractive opportunities for sensing due to their inherent geometrical connections to physical propagation channels. Two common modalities used in mmWave sensing are monostatic and bistatic sensing, which are usually considered separately. By integrating these two modalities, information can be shared between them, leading to improved sensing performance. In this paper, we investigate the integration of monostatic and bistatic sensing in a 5G mmWave scenario, implement the extended Kalman-Poisson multi-Bernoulli sequential filters to solve the sensing problems, and propose a method to periodically fuse user states and maps from two sensing modalities.

In opportunistic cognitive radio networks, the secondary user (SU) requires long sensing time to achieve a reliable spectrum sensing performance when the primary signal is very weak, leading to little remaining time for the secondary transmission. To tackle this issue, we propose an active reconfigurable intelligent surface (RIS) assisted spectrum sensing system to enhance the received primary signal at the SU, therefore the required sensing time can be reduced. In contrast to the passive RIS, the active RIS can amplify the incident signal and hence is more efficient in terms of the required reflecting elements as well as power consumption. Particularly, we study the reflecting coefficient matrix (RCM) optimization problem to improve the performance of the active RIS assisted spectrum sensing system. With the knowledge of the spiked model from random matrix theory, the RCM optimization problem can be transformed to an equivalent problem maximizing the largest eigenvalue of the population covariance matrix of the sensing signal samples. Then, we adopt the weighted minimum mean square error (WMMSE) algorithm to obtain the optimal RCM. Besides, we also investigate the minimum power budget for the active RIS to realize a near-1 detection probability under a simplified case, where the direct link does not exist and line-of-sight RIS-related channels are considered. Simulation results show that the active RIS can outperform the passive RIS for the same power budget in the RIS-assisted spectrum sensing system.

Radio environment map (REM) is a database repository widely adopted for applications such as spectrum sensing, interference management and network planning for wireless networks. However, conventional REM construction requires measurement-capable devices (MCDs) to upload location-based measurements, which exposes the privacy of users. In this paper, we propose a new method to construct the REM utilizing federated learning (FL) to preserve user privacy. In this method, multiple mobile MCDs collect the signal strength measurements in parallel and store the measurements locally. FL is then used to train a shared deep machine learning (DML) model for multiple MCDs collectively. In addition, we pre-process the location information and apply power adjustment to the measurements, either to increase the dynamic range of the input data to the FL or decrease the dynamic range of the measurements. The performance of the proposed FL-based method is evaluated in terms of the colormaps, heatmaps and cumulative distribution function (CDF) of estimation errors. The colormaps show that the power adjustment greatly improves the performance of the FL-based REM. The heatmaps show that smaller estimation errors can be achieved with more participating MCDs. The simulation results also show that the proposed FL-based method achieves better performance than the distance-based method and the nearest-neighbor-based (NN-based) method, in terms of estimation errors.

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.

We propose a scheme which generates shared secret keys from the amplitude of frequency domain channel state information (CSI) of individual orthogonal frequency-division multiplexing (OFDM) subcarrier. A unique quantization scheme is proposed, with the use of two parameters,$a$and β, to set the quantization threshold. Based on multivariate Rayleigh distribution, we derive the analytical expressions for key bit disagreement rate (BDR) and key length. The theoretical analysis of BDR and key length aligns with the simulation results. We also present a mathematical model to optimize the parameters of our proposed quantization scheme and a guideline to set$a$and β. Our proposed method for iterative tuning of β can balance the number of zeros and ones in the generated key sequences so that the randomness requirement is met.

In this paper, we investigate multi-criteria client selection and energy consumption minimization for hierarchical federated learning (HFL) to deal with clients' heterogeneity and limited energy. To the best of our knowledge, this is the first work to investigate multi-criteria client selection for HFL. A fuzzy logic assisted client selection (FLACS) scheme is proposed, where multiple criteria are taken into account, including the distance, clients' battery capacity and computational resource. The FLACS scheme enables a significant performance gain in terms of the clients' average normalized suitability over the previous schemes. A joint communication and learning factors optimization (JCLFO) algorithm is proposed to minimize the system energy consumption. Thanks to the derived closed-form expressions for the optimal aggregation intervals, computation frequency and transmission power, the JCLFO algorithm can achieve the optimal performance in terms of the system energy consumption and converge within only 3 iterations, with a significant complexity reduction over exhaustive search.

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility.

Supporting wireless transmission of large-scale control systems is a challenging task due to the scarcity of wireless resources in the industrial internet of things (IIOT). To reduce wireless resource consumption while maintaining control stability, this paper investigates the wireless networked predictive control system, where only part of the control devices is permitted to transmit their state information to the centralized controller in each control cycle. For the rest unscheduled control devices, the centralized controller predicts their state information via the Gaussian process regression method. To evaluate the control performance and the wireless resources consumption, we formulate a joint optimization problem of control device scheduling, power allocation, and bandwidth allocation. The joint predictive control and communication optimization (JPCCO) scheduling algorithm is proposed to minimize both the control cost and communication cost. As for control device scheduling, we proposed a fuzzy logic based scheduling ranking (FL-SR) method, where control devices are ranked in descending order according to the fuzzy output. Numerical results show that the proposed JPCCO scheduling method with FL-SR outperforms the previous scheduling methods without predictive control, enabling a more stable wireless networked control system with less wireless resources.

In this paper, we investigate a dynamic coordinated beamforming (CBF) problem to enhance the sum rate of a massive multiple-input multiple-output (MIMO) cellular network. Although existing optimization-based algorithms can provide near-optimal solutions, they require real-time global channel state information (CSI) and have high computational complexity, making them not viable in practical mobile networks. To tackle this issue, we propose a deep reinforcement learning based distributed dynamic CBF framework, which allows each base station (BS) to determine the optimal beamformers with only local CSI and some historical information transferred from other BSs. Besides, the computational complexity is substantially reduced thanks to the exploitation of neural networks and expert knowledge, i.e., a known solution structure that can be observed from a closed-form optimization algorithm. Simulation results demonstrate that the proposed approach can outperform the closed-form optimization methods and achieve comparable performance to the state-of-the-art optimization algorithm.

Digital beamforming is a crucial technology that enables fifth-generation (5G) devices to operate on millimeter wave (mmW) radio frequencies. However, linearizing massive arrays at the transmitter (TX) side remains a significant challenge as the complexity scales with the number of antennas. This work focuses on the orthogonal frequency division multiplexing (OFDM) modulation in 5G and beyond networks and presents low-complexity linearization approaches that can be used before the precoder. These approaches do not scale with the number of antennas and provide flexible control of the linearization performance across different parts of the spectrum. Simulation results for a 64-element uniform linear array (ULA) demonstrate that the proposed techniques can achieve performance comparable to conventional multi-digital pre-distortion (DPD) with only 8.91% and 3.52% of the complexity.

Pilot contamination incurred by strong inter-cell interference seriously degrades the performance of channel estimation in massive multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We propose an independent component analysis (ICA) and sparse recovery algorithm based semi-blind channel estimation scheme, referred to as sparse ICA (SICA), for multi-cell massive MIMO-OFDM systems, which does not require any prior information of intercell interference and therefore is more practical. The proposed SICA scheme enables accurate channel estimation by exploiting both the high-order statistics of the received signal and channel sparsity in angle domain. The SICA scheme performs in a semi-blind manner as it is much more robust against pilot overhead than the previous approaches, and requires only one OFDM symbol as pilot to achieve a superior normalized mean square error of channel estimation. Furthermore, the complexity required by SICA is much lower than that required by the previous work, thanks to the negligible complexity of interference sources number estimation based on sparse recovery algorithm.

We consider a three-dimensional (3D) unmanned aerial vehicle (UAV)-terrestrial base station (TBS) coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) scheme where UAV coordinates with TBS to allow joint transmission for the terrestrial users. With the assistance of closed-form power allocation derivations, a joint user scheduling and power allocation (J-USPA) algorithm is proposed to obtain the optimal user scheduling solution, with the consideration of imperfect channel estimation. Moreover, considering the line of sight (LoS) and non-LoS factors in the air-ground channel, the 3D trajectory of UAV is designed to provide a flexible user-centric service. Numerical results verify that the 3D UAV-TBS CoMP-NOMA model and the J-USPA scheme have a superior performance in terms of sum rate of users over the TBS CoMP-NOMA and the UAV assisted NOMA systems without CoMP transmission.

In this paper, we consider a unmanned aerial vehicle (UAV)-enabled uplink (UL) heavy non-orthogonal multiple access (NOMA) system where the UL communication becomes increasingly important in some hot spot regions such as live concerts or football stadiums, and study the maximization of the minimum average rate among all users by jointly optimizing bandwidth assignment (BA) and power allocation (PA) alongside UAV trajectory design, while meeting their specified heterogeneous rate requirements. Specifically, by revealing that the inter-user interference can be naturally eliminated while deriving the sum rate of users in each NOMA group, an one-step BA and PA algorithm is proposed, with the assistance of closed-form results. Afterwards, the elevation angle is introduced as an auxiliary variable to help solve the UAV trajectory design problem. The joint BA and PA algorithm and the UAV trajectory design can be optimized alternatively, which leads to fast convergence and demonstrates a superior minimum average rate among users and user fairness performance over the previous methods.

We propose a bistatic joint radar and communication (JRC) based on standardized 5G new radio (NR) waveform, where the receiver can decode the transmitted signal, and estimate the range, speed, and angle of the radar targets at the same time. The proposed solution consists of several key components, including a) a decision feedback architecture and two JRC algorithms for bistatic radar sensing; b) a multi-user bistatic JRC solution; and c) a direction of arrival (DOA) estimation algorithm based on the range-Doppler map (RDM). We present simulation results to demonstrate the effectiveness of our proposed solution, and its ability to work well in the multi-user scenarios where multiple users share the same spectrum, making it a promising solution for practical applications that require radar sensing in crowded environments.

Extensive pathloss measurements are carried out at 3.45GHz in a campus block environment with mixed indoor and outdoor areas. Measurements are carried out under two scenarios of transmitter placement namely, a) indoor and b) outdoor locations. Receiver locations are categorized into indoor, outdoor, and outdoor-to-indoor (O2I) locations, and investigations led to evident scenario-specific effects on the pathloss modeling. The standard log distance pathloss model is fitted for the different categories, and a good agreement of the shadow fading standard deviation to 3GPP recommendations is observed. Furthermore, a systematic approach to obtain the O2I penetration loss, without any bias in the sampling, for the scenarios with a concrete wall and a wall with a glass door is carried out and compared against the recommendations from 3GPP pathloss modeling.

Multiplexing or transmission diversity via dual links enables the reduction in age of information (AoI). We address the open issue of selection between the two transmission modes for AoI-oriented short-packet system over fading channels, with a comprehensive analysis. Closed-form expressions for the average AoI and peak AoI (PAoI) are derived based on the discrete-time Markov-chain process. Then, to obtain the explicit region of preference (RoP) and quantitative PAoI gains of multiplexing/diversity over the single-queue case, we derive the signal-to-noise ratio (SNR) threshold for transmission mode selection, which is shown to be a decreasing function of the arrival rate and saturates at high arrival rate. Also, the monotonicity of the PAoI gains by multiplexing and diversity, and their achievable gains, are analyzed in a comprehensive manner. It is shown that diversity is able to achieve a PAoI gain of more than 3 dB over the single-queue case at low SNR, while multiplexing has a larger RoP than diversity and is selected at high SNR and high arrival rate. Furthermore, both throughput and PAoI violation probability are considered alongside the average PAoI for a wide range of tradeoff in system design.

We consider a multiple-input single-output (MISO) optical wireless communications (OWC) system with highly correlated channels, causing bit error rate (BER) performance degradation. Because of intensity modulation and direct detection (IM/DD), the transmitted signals are real-valued and non-negative, which limits the utilization of precoding in phase domain. We employ direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) modulation to design a group of phase rotation (PR) factors in frequency domain for OWC systems, robust against high channel correlations. The proposed PR-based precoding has a number of advantages over the power factor-based design in the literature: i) no change of transmission power on each LED; ii) no signal-to-noise ratio (SNR) and no BER degradation on transmitted signals of all LEDs. We formulate an optimization problem to obtain the optimal PR factors by maximizing the minimum pairwise Euclidean distances between all candidate signals. The optimization problem is non-convex, requiring multi-dimensional exhaustive searches. In order to reduce the complexity, we propose three low-complexity PR-based precoding approaches which provide BER performances better than the power factor-based designs in the literature, and are close to their own analytical results derived, respectively. The proposed approaches are validated via a built testbed, producing comparable performance between measured and simulated BERs.

Space-time shift keying-aided orthogonal time frequency space modulation-based multiple access (STSK-OTFS-MA) is proposed for reliable uplink transmission in high-Doppler scenarios. As a beneficial feature of our STSK-OTFS-MA system, extra information bits are mapped onto the indices of the active dispersion matrices, which allows the system to enjoy the joint benefits of both STSK and OTFS signalling. Due to the fact that both the time-, space- and DD-domain degrees of freedom are jointly exploited, our STSK-OTFS-MA achieves increased diversity and coding gains. To mitigate the potentially excessive detection complexity, the sparse structure of the equivalent transmitted symbol vector is exploited, resulting in a pair of low-complexity near-maximum likelihood (ML) multiuser detection algorithms. Explicitly, we conceive a progressive residual check-based greedy detector (PRCGD) and an iterative reduced-space check-based detector (IRCD). Then, we derive both the unconditional single-user pairwise error probability (SU-UPEP) and a tight bit error ratio (BER) union-bound for our single-user STSK-OTFS-MA system employing the ML detector. Furthermore, the discrete-input continuous-output memoryless channel (DCMC) capacity of the proposed system is derived. The optimal dispersion matrices (DMs) are designed based on the maximum attainable diversity and coding gain metrics. Finally, it is demonstrated that our STSK-OTFS-MA system achieves both a lower BER and a higher DCMC capacity than its conventional spatial modulation (SM) and its orthogonal frequency-division multiplexing (OFDM) counterparts. As a benefit, the proposed system strikes a compelling BER vs. system complexity as well as BER vs. detection complexity trade-offs.

Age of information (AoI) is an effective measure of the information freshness for wireless networked control systems (WNCSs). However, the AoI performance for a closed loop of WNCS with two-way delays has remained unexplored, especially in the finite blocklength (FBL) regime. In this paper, we investigate the peak age of loop (PAoL) performances, including the average, variance and outage probability of PAoL, for WNCSs with FBL over fading channels. Their closed-form expressions are respectively derived regarding the blocklength and the maximum number of allowable transmissions. We prove that the average PAoL is less than the sum of the average peak AoI in uplink (UL) and downlink (DL) due to the coupling between UL and DL. We also show that there is a tradeoff between the average PAoL and the variance/outage probability of PAoL. Based on the comprehensive performance analysis, we study a PAoL-oriented communication and control co-design with an adaptation scheme for transmission power, blocklength and the maximum number of allowable transmissions. Simulation results verify the correctness of the analytical results and show that the proposed PAoL-oriented scheme significantly outperforms the UL only and DL only optimization schemes, with an up to 8-fold reduction in the average control cost.

In this paper, we investigate a novel uplink reconfigurable intelligent surface (RIS) based multi-user multi-input multi-output symbiotic radio system. It indicates that each RIS, as an Internet-of-Things (IoT) device, enhances the primary transmission from a nearby user to the base station (BS), and simultaneously transmits its own information to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary communications from the users to the BS. We consider both the case of perfect and imperfect channel state information (CSI), and design the active beamforming at the BS and the passive beamforming at the RISs jointly to maximize the weighted sum-rate of both the primary and IoT transmissions. For the perfect CSI case, we propose an algorithm based on the block coordinate descent (BCD) method to solve the problem. We also propose another algorithm with a similar framework to reduce the computational complexity. For the imperfect CSI case, an algorithm based on BCD and the online successive convex approximation technique is proposed. Simulation results show that the proposed system achieves significant performance gain over a number of baseline schemes for both the perfect and imperfect CSI cases. Furthermore, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.

Timely identification of outliers occurring in key performance indicators (KPIs) of mobile cellular networks is crucial for prompt action to unexpected events. The KPIs of cellular networks typically exhibit seasonality and trend effects and these make the detection of outliers challenging. In this paper, an online unsupervised deep machine learning (DML) algorithm for outlier detection for time series with seasonality and trend is proposed. The proposed algorithm utilizes a neural network based on a temporal convolution network (TCN) and an autoencoder (AE) to reconstruct a time series that captures the normal behavior of the input data and then the reconstruction errors between the input and reconstructed time series at the output of the TCN-AE network are used for outlier detection. To train the TCN-AE network to learn the normal behavior of the input time series, we propose a two-step training process to overcome the presence of outliers in the training data. In addition, a novel loss function specially designed to address the seasonality and trend effects of the time series is proposed. Furthermore, a pre-processing technique is used to combat the adverse trend effect which might cause false alarms in outlier detection. The performance of the proposed TCN-AE-based outlier detection algorithm is evaluated using synthetic time series, Yahoo Webscope dataset and real time series of KPIs of mobile cellular networks. The results show that the proposed TCN-AE-based outlier detection algorithm achieves better detection accuracy in terms of F-score than other DML-based algorithms such as long short-term memory (LSTM)-AE-based algorithm and the convolutional neural network (CNN) based algorithm.

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.

The semantic communication system enables wireless devices to communicate effectively with the semantic meaning of the data. Wireless powered Internet of Things (IoT) that adopts the semantic communication system relies on harvested energy to transmit semantic information. However, the issue of energy constraint in the semantic communication system is not well studied. In this paper, we propose a semantic-based energy valuation and take an economic approach to solve the energy allocation problem as an incentive mechanism design. In our model, IoT devices (bidders) place their bids for the energy and power transmitter (auctioneer) decides the winner and payment by using deep learning based optimal auction. Results show that the revenue of wireless power transmitter is maximized while satisfying Individual Rationality (IR) and Incentive Compatibility (IC).

Different from traditional security-oriented designs, the aim of anonymizing techniques is to mask users' identities during communication, thereby providing users with unidentifiability and unlinkability. The existing anonymizing techniques are only designated at upper layers of networks, ignoring the risk of anonymity leakage at physical layer (PHY). In this paper, we address the PHY anonymity design with focus on a typical uplink scenario where the receiver is equipped with more antennas than the sender. With the increased degrees-of-freedom at the receiver side, we first propose a maximum likelihood estimation (MLE) signal trace-back detector, which only analyzes the signaling pattern of the received signal to disclose the sender's identity. Accordingly, an anonymity entropy anonymous (AEA) precoder is proposed, which manipulates the transmitted signalling pattern to counteract the receiver's trace-back detector and meanwhile to guarantee high receive signal-to-interference-plus-noise ratio for communication. More importantly, more data streams can be multiplexed than the number of transmit antennas, which is particularly suitable for the strong receiver configuration. Simulation demonstrates that the proposed AEA precoder can simultaneously provide high anonymity and communication performance.

Multiplexing or transmission diversity via dual queues enables the reduction in age of information (AoI) in industrial Internet-of-Things (IIoT). We address the open issue of how to select between the two transmission modes for AoI-oriented short-packet systems over fading channels. Closed-form expressions for the average peak AoI (PAoI) in the finite block length regime over fading channels are derived based on the average block error probability. The analytical results match the simulation results well. To assist with obtaining the explicit region of preference (RoP) between the multiplexing and diversity modes, the signal-to-noise ratio (SNR) threshold for transmission mode selection is derived, which is shown to be a mono-decreasing function of the arrival rate and to saturate at high arrival rate. The multiplexing mode demonstrates a larger RoP than diversity and is shown to be preferable at high SNR and high arrival rate. While the diversity mode is preferable in the case of low SNR and low arrival rate by presenting a more significant PAoI reduction over the single-queue case.

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.

In mutualistic cooperative ambient backscatter communications (AmBC), Internet-of-Things (IoT) device sends its information to a desired receiver by modulating and backscattering the primary signal, while providing beneficial multipath diversity to the primary receiver in return, thus forming a mutualism relationship between the AmBC and primary links. We note that the hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we consider a mutualistic cooperative AmBC network under HIs, and study the impacts of HIs on the achievable rates of the primary link and the AmBC link. In particular, we theoretically prove that although HIs degrades the rate of each link, the mutualism relationship between the AmBC and primary links is maintained, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. The closed-form rate expressions of both the AmBC and primary links are derived. Computer simulations are provided to validate our theoretical analysis.

For massive machine type communications (mMTC), it is critical to squeeze the transmission overhead as packet length is usually short and power is limited. Reducing preamble/pilot length for channel estimation is thus very important. In this paper, we propose to use short preamble for channel estimation in generalized frequency division multiplexing (GFDM) communication. Based on the short preamble, we can estimate a short channel first. We then show that the conventional zero-padding method for extending short channel to long channel in GFDM is not accurate. A new efficient method to construct the long effective channel from the obtained short channel is proposed. The proposed new method can construct a long channel for GFDM equalization without knowing the time sync error. It is proved theoretically that the constructed channel is correct given that the length of cyclic prefix (CP) and cyclic suffix (CS) are longer than the original channel length plus the time sync error. Simulations at various situations are shown to verify the results.

Existing channel-state information (CSI)-based human authentication systems in the literature require a large amount of CSI data to train deep neural network (DNN) models and are ineffective for unknown intruder detection. To address this issue, we propose a CSI-based human authentication system (CAUTION) which is able to learn distinctive gait features of different users through CSI data to perform human authentication in this article. By taking advantage of few-shot learning, CAUTION is able to construct an accurate user identification model with a very limited number of CSI training data. By converting the CSI samples into low-dimensional representations on the feature plane, it computes central points for different users as their CSI profiles and introduces an intruder threshold to measure whether the CSI data matches one of the user classes by a margin. The intruder threshold is able to be optimized without any intruders’ data. CAUTION does not require a large number of training data and provides an effective way to train the system for unknown intruder detection. We have tested CAUTION at different places and compared it with state-of-the-art CSI-based authentication systems. The experimental results demonstrate that CAUTION is able to perform accurate human authentication with a limited amount of CSI training data (one-fifth of data needed by compared systems) and outperforms the compared human authentication systems.

This article investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network, in which we propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV and also leverage the MUAV as a recharging power source. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network. Depending on whether there is charging between the two UAVs, two optimization problems are formulated. To solve them, two multi-agent deep reinforcement learning (DRL) approaches are proposed, which are centralized training multi-agent deep deterministic policy gradient (CT-MADDPG) and multi-agent deep deterministic policy option critic (MADDPOC). It is shown that the CT-MADDPG can greatly reduce the complexity of optimization, and the proposed MADDPOC is able to support low-level multi-agent cooperative learning in the continuous action domains, which has great advantages over the existing option-based hierarchical DRL that only supports single-agent learning and discrete actions.

Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.

Mutualistic cooperative ambient backscatter communications (AmBC) have been proposed to improve the spectrum and energy efficiencies of Internet-of-Things (IoT) systems, where a primary link (from a primary transmitter to a primary receiver) and an AmBC link (from an IoT device to the same primary receiver) form a mutualism relationship. We note that hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we study a mutualistic cooperative AmBC network with HIs at all the active transceivers and a non-linear energy harvesting circuit at each IoT device. We derive closed-form rate expressions for both the AmBC and primary links and theoretically prove that the mutualism relationship between the AmBC and primary links is maintained under HIs, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. To maximize the weighted sum rate of all links in a cooperative AmBC network under HIs, we propose two resource allocation schemes for two scenarios with a single link and multiple AmBC links, respectively. For the single AmBC link case, we derive the optimal transmit power of the primary transmitter and the optimal power reflection coefficient of the IoT device in closed forms. For the scenario with multiple AmBC links, the weighted-sum-rate maximization problem is transformed into a convex one and solved with convex optimization tools. Computer simulations validate our theoretical results and that our proposed schemes outperform the benchmark schemes in terms of the weighted sum rate.

In this paper, we investigate unmanned aerial vehicle (UAV) assisted communication systems that require quasi-balanced data rates in uplink (UL) and downlink (DL), as well as users’ heterogeneous traffic. To the best of our knowledge, this is the first work to explicitly investigate joint UL-DL optimization for UAV assisted systems under heterogeneous requirements. A hybrid-mode multiple access (HMMA) scheme is proposed toward heterogeneous traffic, where non-orthogonal multiple access (NOMA) targets high average data rate, while orthogonal multiple access (OMA) aims to meet users’ instantaneous rate demands by compensating for their rates. HMMA enables a higher degree of freedom in multiple access and achieves a superior minimum average rate among users than the UAV assisted NOMA or OMA schemes. Under HMMA, a joint UL-DL resource allocation algorithm is proposed with a closed-form optimal solution for UL/DL power allocation to achieve quasi-balanced average rates for UL and DL. Furthermore, considering the error propagation in successive interference cancellation (SIC) of NOMA, an enhanced-HMMA scheme is proposed, which demonstrates high robustness against SIC error and a higher minimum average rate than the HMMA scheme.

The notion of age of information (AoI) has become an important performance metric in network and control systems. Information freshness, represented by AoI, naturally arises in the context of caching. We address optimal scheduling of cache updates for a time-slotted system where the contents vary in size. There is limited capacity for the cache for making updates. Each content is associated with a utility function that depends on the AoI and the time duration of absence from the cache. For this combinatorial optimization problem, we present the following contributions. First, we provide theoretical results of problem tractability. Whereas the problem is NP-hard, we prove solution tractability in polynomial time for a special case where all contents have the same size, by a reformulation using network flows. Second, we derive an integer linear formulation for the problem, of which the optimal solution can be obtained for small-scale scenarios. Next, via a mathematical reformulation, we derive a scalable optimization algorithm using repeated column generation. In addition, the algorithm computes a bound of global optimum, that can be used to assess the performance of any scheduling solution. Performance evaluation of large-scale scenarios demonstrates the strengths of the algorithm in comparison to a greedy schedule. Finally, we extend the applicability of our work to cyclic scheduling.

We investigate a multi-device ultra-reliable low-latency communication system with heterogeneous traffic and finite block length over temporally-correlated fading channels. In light of the challenging demand for accurate channel estimation with limited pilot in a short frame, two frame structures, which respectively adopt independent pilots and shared pilot, are investigated. Block lengths and pilot lengths are jointly optimized for the two frame structures, through instantaneous channel state information (CSI) based dynamic optimization and statistical CSI based static optimization, to strike the tradeoffs among performance, complexity and signaling overhead. The proposed joint optimization algorithms significantly outperform the existing approaches that solely optimize block lengths or pilot lengths. The dynamic optimization algorithms achieve near-optimal performance at dramatic complexity reduction over exhaustive search, and maintain robustness against traffic heterogeneity. Also, the static optimization algorithms are conducted offline, while still outperforming the previous instantaneous CSI based dynamic optimization approaches. It is demonstrated that the independent-pilot frame structure with dynamic optimization is preferable in the scenario with high traffic heterogeneity or high mobility, and that the shared-pilot frame structure with static optimization presents a comparable performance to the former in the case of low mobility, incurring negligible complexity and signaling overhead.

In this paper, we study a covert Internet of Things (IoT) system. Compared with conventional IoT systems that apply cryptography and information-theoretic secrecy approaches to secure the transmission, our considered IoT system adopts the covertness technique and intends to hide the legitimate transmission from the observant adversaries. In the IoT system, the IoT devices randomly transmit the collected data to their associated IoT gateways (GWs). In the meantime, the adversaries attempt to detect the existence of legitimate transmission based on their received signal power and launch hostile attacks accordingly. To avoid being detected by the adversaries, the IoT system applies uplink power control to achieve covert legitimate transmission. Moreover, to distort the observation of the adversaries so as to mislead their decisions, we propose an artificial noise (AN)-assisted covert communication design, where the AN is transmitted by in-band full-duplex (IBFD) IoT GWs as a jamming operation. We formulate a Stackelberg game to study the interaction between the adversaries and the legitimate entities including the IoT GWs and IoT devices, where the legitimate entities, as the leaders, decide on the powers of legitimate and AN transmissions at the upper level and the adversaries, as the followers, aim to minimize their detection errors at the lower level. Thereafter, considering the large scale of IoT system, we further cast the Stackelberg game into a mean-field Stackelberg game and incorporate the stochastic geometry and statistical channel model to capture the location heterogeneity and channel dynamics among and of the system entities, respectively. In the performance evaluation, we verify the practicability of the mean-field Stackelberg game. Moreover, we demonstrate the effectiveness of AN in improving the transmission covertness.

In limited space scenarios, the antennas in a multiantenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrarily correlated antennas over the Nakagami-$m$fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with arbitrarily correlated triple diversity branches is derived as a special case. Moreover, to characterize the performance of an energy detector under mobility, the area under the curve of a receiver operating characteristic is analysed. Furthermore, as the sensing decisions can be utilized to improve the sensing performance, a comprehensive analysis of sensing performance of the mobile secondary user conditioned on the primary user (PU) activity statistics is carried out. We assume the PU traffic to be following the Discrete-Time Markov Chain (DTMC) model, and its$idle$and$busy$periods to be following generalised Pareto distribution. The analytical framework is substantiated by Monte Carlo simulations. Results indicate that antenna correlation deteriorates detection performance. Moreover, the detection performance deteriorates further due to the mobility of CR users, especially in the deep fading channel scenarios. Furthermore, findings also suggest that under mobility, the sensing performance improves if the duty cycle of the PU channel is low. This work provides a realistic sensing framework for CR enabled vehicles.

Wireless powered communication via backscatter and/or harvest-then-transmit (HTT) has been considered a promising solution to connecting nodes in the Internet-of-things (IoT) networks. However, the harvested energy at an IoT node is heavily limited by the distance between the node and the power beacon (PB) due to the high propagation loss. In this paper, we propose to employ an unmanned aerial vehicle (UAV) as a mobile PB to provide energy signals on demand to the IoT nodes, which convey their information to a reader via backscattering or active transmission using the harvested energy. We maximize the total energy efficiency (EE) of all the IoT nodes powered by the UAV by jointly optimizing the UAV’s transmit power and trajectory, the IoT nodes’ backscatter reflection coefficients and their transmit power for active transmission, and the time allocation between backscattering and active transmission. To solve the formulated non-linear fractional programming problem, we use the generalized fractional programming theory and a block coordinated decent method to decompose it into two sub-problems: one optimizes the communication resource allocation under a fixed UAV trajectory, and the other optimizes the UAV trajectory for given communication resource allocation. We then devise a Dinkelbach-based iterative algorithm to solve the two sub-problems by employing a Lagrangian dual method and a successive convex programming technique, respectively and iteratively. Simulation results show that our proposed iterative algorithm converges very fast, and the optimized UAV-enabled hybrid backscatter-HTT communication achieves a much higher EE of all the IoT nodes than the benchmark schemes including the UAV-enabled backscatter, UAV-enabled HTT, and hybrid BackCom-HTT with a fixed PB.

Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.

There has been a strong interest in using deep neural networks (DNNs) for modeling the power amplifier (PA) non-linearity and designing the digital pre-distortion (DPD) circuit. Most DNNs only accept real-valued inputs since the baseband signal has in-phase and quadrature (I/Q) components. As a result, their entire structures can be highly complex. In this paper, we are interested in reducing the complexity of such structures by exploiting both the envelope-dependent terms and residual learning. To acquire such an efficient structure, we propose a novel methodology executed over two consecutive steps; at first, we estimate the best input combinations to a shallow NN that allows it to achieve a threshold value of NMSE. Then, we exploit these combinations as inputs to our proposed structure and increase the network depth until we obtain our system's actual requirements. Finally, our optimized structure (ODNN) performance has been evaluated using MATLAB simulation and real measurements. For a 15 MHz test signal, ODNN achieves lower NMSE than conventional DNN by 2.13 dB and 3.08 dB for Doherty PA behavioral modeling and its DPD design, respectively. For a broader 40 MHz test signal, ODNN achieves lower NMSE by 0.94 dB and 1.94 dB. Moreover, in all previous scenarios, ODNN reduces the complexity of DNN by 26.40%.

In this paper, we investigate a reconfigurable in-telligent surface (RIS)-based uplink massive multi-input multi-output symbiotic radio system, where each RIS, as an IoT device, enhances the primary transmission from a nearby user to the base station (BS) and simultaneously transmits its own infor-mation to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary transmission. Assuming imperfect channel state information (CSI), we jointly design the active beamforming at the BS and the passive beamforming at the RISs to maximize the weighted sum-rate of both the primary and IoT transmissions. An algorithm based on the block coordinate descent method is proposed to solve it. Simulation results show that the proposed system achieves significant performance gain compared with different baseline schemes. Besides, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.

Joint radar-communications (JRC) has been proposed recently for autonomous vehicles (AVs) to simultaneously perform radar sensing, e.g., detecting distant vehicles and pedestrian, and data transmission, e.g., to edge computing services, all on the same waves. However, due to the high AV density in urban area, the spectrum service provider (SSP) needs to allocate the spectrum resources optimally. In this paper, we consider the social welfare of the network which is defined as the total revenue of the SSP and the utilities of the AV users, and propose an auction-based algorithm to model the competition among the AV users to obtain the spectrum resources and maximize the social welfare. Since some AV users can have critical and useful information about each other (e.g., AV neighbours sharing traffic data), we consider the network effect in our proposed auction mechanism to incentivize more AV users to join the auction. The numerical results demonstrate the effectiveness of our proposed design compared to traditional schemes.

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.

Radio interference or jamming can cause isolated area in advanced metering infrastructure (AMI) based on Wire-less smart utility network (Wi-SUN), in which conventional recovery techniques cannot cope with. In this paper, we deploy narrowband internet-of-things (NB-IoT) interface to some smart meters to act as emergency gateway, called ResiLite. An optimal ResiLite placement algorithm for enhancing network resilience is proposed. We define an implicit resilience metric based on path diversity and cluster closeness. The defined metric is used to form an integer linear programming (ILP) problem, then we solve the ILP to obtain optimal ResiLite placement. Simulation results show that the optimal ResiLite placement improves Wi-SUN AMI network resilience (defined as the number of surviving nodes with packet delivery ratio (PDR) above 99% under disturbance) by up to 167% compared to an AMI without ResiLite, and up to 29% compared to uniformly random ResiLite placement, respectively.

We introduce a new framework for fairness study of Power-Domain Non-Orthogonal Multiple Access (PD-NOMA) with fairness factor definitions to analyse pair and system behaviour. Aiming at both pair-wise and system-level fairness as key performance goals, first, we present a power allocation scheme to maximize sum bit rate within each pair. We demonstrate analytically that it is possible to enhance both users' bit rates in the pair over their Orthogonal Multiple Access (OMA) equivalent rates without impacting fairness. Additionally, at system level, we complement our work proposing two prioritization algorithms to enhance fairness through the NOMA paring process. Simulations show that the schemes are effective to maximize the system fairness when it is measured with, either the users' or the pairs' bit rates.

In this paper, we present the design, analytical and simulation results for a wireless Medium Access Control (MAC) protocol, referred to as the “Transmission Gating Time Hyperchannel” (TGT-HC) to support bounded low latency delivery of cyclic control traffic such as generated by close-loop distributed control systems. TGT-HC is based on the contention-free Carrier-Sense Multiple Access (CSMA) Hyperchannel protocol. To enhance its performance, we use a combination of per-flow Time-Aware Shaper (TAS) and First-In-First-Out (FIFO) scheduling to determine its sensing slot assignments. Based on the results, we find that this enhanced protocol comes close to the ideal delay performance of a First-Come- First-Serve (FCFS) single server in handling cyclic traffic.

In this paper, we propose a novel scheme called subband random sensing (SRS) grant free uplink for ultra-reliable low-latency communication (URLLC) in unlicensed spectrum. The SRS grant free uplink creatively combines the subband sensing, user grouping and random access, which allows a user sensing the unlicensed spectrum to use available sub-resources in a wideband and reduce collisions with other users. The new scheme overcomes the severe spectrum wastage problem of the semi-persistent scheduling (SPS) uplink adopted by new radio (NR) release 16 in applications with sporadic traffic. Compared with the contention based grant free uplink, the new scheme achieves much lower collision probability, which directly leads to higher reliability. Analysis and simulations are provided to prove the exceptional performances.

Internet of Vehicles (IoV) is an emerging concept that is believed to help realize the vision of intelligent transportation systems (ITSs). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, the Internet of Things, and cyber-physical systems. IoV enables the integration of smart vehicles with the Internet and system components attributing to their environments, such as public infrastructures, sensors, computing nodes, pedestrians, and other vehicles. By allowing the development of a common information exchange platform between vehicles and heterogeneous vehicular networks, this integration aims to create a better environment and public space for the people as well as to enhance safety for all road users. Being a participatory data exchange and storage, the underlying information exchange platform of IoV needs to be secure, transparent, and immutable in order to achieve the intended objectives of ITS. In this connection, the adoption of blockchain as a system platform for supporting the information exchange needs of IoV has been explored. Due to their decentralized and immutable nature, IoV applications enabled by blockchain are believed to have a number of desirable properties, such as decentralization, security, transparency, immutability, and automation. In this article, we present a contemporary survey on the latest advancement in blockchain for IoV. Particularly, we highlight the different application scenarios of IoV after carefully reviewing the recent literature. We also investigate several key challenges where blockchain is applied in IoV. Furthermore, we present the future opportunities and explore further research directions of IoV as a key enabler of ITS.

Jamming or illegitimate wireless network access interferes with legitimate communication sessions by mimicking the legitimate transmissions and degrades the network performance. In this article, we propose a methodology to detect such attacks by implementing a multiple hypotheses sequential testing-based detection framework with variance and channel state information (CSI)-based algorithms. The detection framework focuses on distinguishing between legitimate and illegitimate transmissions and the nature of illegitimate transmissions with a quaternary hypotheses test. The quaternary hypotheses include no transmission, legitimate node transmission, illegitimate node transmission, and collision-based attack. We first devise a sequential testing problem on a ternary hypothesis problem and then tackle the remaining hypothesis with both variance-based approach and CSI-based approach. We devise algorithms based on the same and compare their performance. We also compare our approach with the generalized Neyman-Pearson approach based on detection speed. In addition, we present a multiple sensor-based approach to further improve the detection performance through soft- and hard-decision combining. We conduct extensive performance evaluations based on both simulated and measurement data. The numerical results show fewer sample size requirements for the proposed algorithms, leading to faster detection.

Channel state information (CSI)-based human activity recognition (HAR) has received great attention in recent years due to its advantages in privacy protection, insensitivity to illumination, and no requirement for wearable devices. In this article, we propose a multimodal channel state information-based activity recognition (MCBAR) system that leverages existing WiFi infrastructures and monitors human activities from CSI measurements. MCBAR aims to address the performances degradation of WiFi-based human recognition systems due to environmental dynamics. Specifically, we address the issue of nonuniformly distributed unlabeled data with rarely performed activities by taking advantages of the generative adversarial network (GAN) and semisupervised learning. We apply a multimodal generator to approximate the CSI data distribution in different environment settings with limited measured CSI data. The generated CSI data using the multimodal generator can provide better diversity for knowledge transfer. This multimodal generator improves the ability of MCBAR to recognize specific activities with various CSI patterns caused by environmental dynamics. Compared to state-of-the-art CSI-based recognition systems, MCBAR is more robust as it is able to handle the nonuniformly distributed CSI data collected from a new environment setting. In addition, diverse generated data from the multimodal generator improves the stability of the system. We have tested MCBAR under multiple experimental settings at different places. The experimental results demonstrate that our algorithm overcomes environmental dynamics and outperforms existing HAR systems.

Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, machine learning (ML) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without transmitting raw sensitive data to UAV servers. The device selection strategy is also introduced into the AFL framework to keep the low-quality devices from affecting the learning efficiency and accuracy. Moreover, we propose an asynchronous advantage actor-critic (A3C) based joint device selection, UAVs placement, and resource management algorithm to enhance the federated convergence speed and accuracy. Simulation results demonstrate that our proposed framework and algorithm achieve higher learning accuracy and faster federated execution time compared to other existing solutions.

Both information age and delay are critical performance metrics of emerging time-sensitive applications. However, their inherent correlation in the finite block length (FBL) regime has remained uninvestigated as it is affected by block length and update rate in a complex manner. In this paper, closed-form expressions for average age of information (AoI), peak AoI (PAoI) and delay are derived for an FBL Last-Come First-Served system with retransmission and non-preemption policies, based on which a comprehensive analysis of the relationship among the three metrics in the FBL regime is presented. It is proved that there exists a strong tradeoff between delay and AoI/PAoI given a block length, and that AoI, PAoI and delay have positive correlation given an update rate, regardless of the weight. With the goal of minimizing delay and AoI simultaneously, the weighted sum of delay and PAoI (upper bound on AoI) is formulated and proved to be convex with respect to block length and update rate. A low-complexity optimization algorithm is developed with a closed-form expression of the optimal update rate, whose performance approaches the Pareto boundaries of the PAoI-delay and the AoI-delay regions, at much lower complexity than exhaustive search.

In contrast with the traditional cryptography, physical layer security has attracted the attention of many researchers having aim at reinforcing the security of communication systems. As far as vehicular communication is concerned, it is challenging to maintain secure and reliable communication between the connected vehicles due to the density, mobility, and dynamic network topology. This paper considers a vehicle to infrastructure communication in which a legitimately fixed transmitter equipped with a single antenna transmits a confidential message to a legitimate mobile receiver equipped with multiple antennas in the presence of a passive mobile eavesdropper. In such a single input multiple output wireless system, the receiver performs the maximal ratio combining technique assuming constant vehicle speed. We assume that the antennas are closely spaced and depending upon the imperfect channel state information (CSI), we derive the closed-form expressions for the average outage probability, secrecy outage probability, and average secrecy outage rate over the uniform, exponential, and arbitrary correlated Nakagami- m channels for dual antenna branches. In order to gain insight we also perform the high SNR asymptotic analysis of the outage probability and secrecy outage probability. Simulations are conducted to validate the accuracy of our derived analytic expressions. The computation error analysis is carried out to provide the suitability of the correlation type at the legitimate receiver side. Our findings suggest that the performance of the case with exponential channel correlation is better than those for the uniform and arbitrary. Numerical results show the joint effect of vehicle mobility and the antenna correlation on secrecy performance. Moreover, we also observed that the imperfect knowledge of the CSI degrades the security of the confidential messages severely under the effect of mobility.

This paper investigates the performance of non-orthogonal multiple access (NOMA) in vehicular networks where a base station (BS) communicates with the vehicles moving away from the BS with single-input multiple-output. To combine the signals received at the antennas, diversity combining techniques such as maximal ratio combining (MRC) and selection combining (SC) are performed at the receiver of each vehicle. However, in practice, the expected performance from the diversity techniques may not be achieved due to the fact that all the diversity branches are not independent and identically distributed (i.i.d) all the time. In this context, analytical expressions of the outage probability and ergodic sum rate are derived for the considered vehicular networks with the assumption of independent but not necessarily identically distributed (i.n.i.d) Nakagami-${m}$fading channels. The performance analysis of NOMA vehicular networks is also extended for multiple-input multiple-output antenna configurations and evaluated in the presence of successive interference cancellation (SIC) error propagation. The obtained analytical results are validated by Monte Carlo simulations. Furthermore, the performance of NOMA is verified with conventional orthogonal multiple access (OMA) for fading parameter$m=1$and$m=2$with perfect channel knowledge and channel estimation. Numerical results show that NOMA outperforms the conventional OMA by approximately 20% and has high sum rate with i.n.i.d as well as i.i.d channel consideration. However, i.n.i.d consideration degrades the performance of NOMA and OMA as the diversity gain achieved with i.n.i.d consideration is less as compared to i.i.d consideration. The performance is further deteriorated with SIC error and channel estimation.

A practical power amplifier (PA) has nonlinear characteristics that distort the output signal and hence increase the transmission error. Digital pre-distortion (DPD) has been widely accepted to compensate for the PA nonlinearity. However, in direct-conversion transmitters (DCTs), DPD performance is affected by in-phase and quadrature (IQ) imbalance. In this paper, we utilize the Iterative Learning Control (ILC) algorithm to design a DPD scheme to compensate for PA nonlinearity under IQ imbalance. We first prove that ILC is applicable in such a scenario. This proof is validated using simulations which show that ILC is able to estimate the PA ideal input. The estimated ideal input is then exploited in training a neural network (NN)-based DPD model. We provide the complexity estimation of our proposed scheme using the number of real multiplications. Finally, we demonstrate the performance advantage of our proposed scheme in comparison with other existing polynomial based approaches through simulations and measurements.

In limited space scenarios, the antennas in the multi-antenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrary correlated antennas over Nakagami-m fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with triple arbitrary correlated diversity branches is derived as a special case. Furthermore, to characterize the performance of energy detector under mobility, the area under the curve of receiver operating characteristic is analysed. The derived expressions converge quickly due to the monotonically decreasing hypergeometric function of two variables. The Monte Carlo simulations substantiate the analytical expressions. Results indicate that antenna correlation deteriorates detection performance. Moreover, the high speed of CR users further decreases the detection performance, especially in the deep fading channel scenarios. This work provides a realistic sensing framework for the CR enabled vehicles.

In this paper, we investigate a large intelligent surface (LIS) assisted symbiotic radio (SR) system, in which a LIS device, operating as an Internet-of-Things (IoT) device, exploits the signal from a primary transmitter (PT) as its communication carrier to achieve its own information transmission, and concurrently serves as a desirable additional link to aid the primary transmission from the PT to a primary receiver (PR). A cooperative beamforming scheme (i.e., active transmit beamforming at the PT and passive reflecting beamforming at the LIS device) is proposed to minimize PT's transmit power under quality-of-service (QoS) constraints of both the primary and LIS device transmissions. Both continuous and discrete phase shift setups of the LIS device are considered. For the continuous phase shift setup, a closed-form solution is derived, analytically showing that by smartly configuring the phase shifts, the signals from primary link and backscatter link can add coherently at the PR; while for the discrete phase shift setup, a near-optimal solution for the 1-bit phase shifter is obtained via the semi-definite relaxation (SDR) technique, and a general successive refinement algorithm (SRA) is developed for any-bit phase shifter. Simulation results demonstrate that cooperative beamforming design can adaptively adjust beamformers to strike a balance between the primary and LIS device transmissions.

Channel State Information (CSI) based human activity recognition has received great attention in recent years due to its advantages in privacy protection, insensitive to illumination and no requirement for wearable devices. However, for practical deployment, it needs to greatly enhance the performance robustness against dynamic changes of the surrounding environment. To address this problem, we propose a novel CSI based activity recognition using Roaming Generator (CSIRoG) system for human activity detection. CSIRoG leverages existing WiFi infrastructures and monitors human behaviours from CSI measurements. It utilizes the generative adversarial network (GAN) to transfer the CSI information from one environment to another with dynamic changes such as people passing by, furniture layout changes, etc. The proposed method aims to approximate the CSI distribution in the new environment setting which has very limited CSI data. Therefore, the system can learn to handle multiple environment dynamics. Compared to the existing works, CSIRoG leverages a multimodal system model for better diversity of the generated CSI data for knowledge transfer. This improves the ability of CSIRoG to recognize various kinds of CSI information for one specific user activity caused by various dynamic conditions, thus enhancing system robustness. We have tested CSIRoG under multiple environment settings at different places. The experimental results demonstrate that our algorithm overcomes environmental dynamics and outperforms existing human activity recognition systems.

Due to the intermittency of renewable energy generation, base stations (BSs) powered by renewable energy harvesters will experience power surpluses and power deficits over time. To use the renewable power more efficiently, energy harvesters with anti-correlated energy generation profiles should be deployed at BSs that are connected by transmission lines and power should be transmitted from surplus BSs to deficit BSs via transmission lines. In this paper, we develop an optimization algorithm to determine how energy harvesters with anti-correlated energy generation profiles should be deployed to every BS by taking into account the topology of the cellular network, i.e., whether or not a transmission line exists between a pair of BSs. Therefore, a BS is deployed more likely with an energy harvester type that is anti-correlated to those deployed at its connected neighboring BSs with our proposed algorithm. In addition, our proposed algorithm takes into account the distance-dependent power loss in the transmission lines. As a result, the shorter the transmission line between a pair of BSs, the more likely that these two BSs are deployed with anti-correlated energy harvesters. The renewable power that can be transmitted from the surplus BSs to the deficit BSs in the cellular network is on average around 40% higher with our proposed optimization algorithm in comparison with randomly deploying anti-correlated energy harvesters to the BSs.

Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator, which can be deployed between base stations and fog nodes to coordi- nate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that DistADMM-PVS minimizes the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that DistADMM-PVS converges much faster than some other existing algorithms. In addition, the joint network slicing with both bandwidth and computational resources offers around 15% overall latency reduction compared to network slicing with only a single resource.

Advanced metering infrastructure (AMI) based on Wireless Smart Utility Network (Wi-SUN) employs carrier sense multiple access (CSMA), hence suffers from poor network performance when the number of nodes increases but without proper design of data collection scheduling. In this paper, we introduce an interference-aware TDMA-like optimal data collection scheduling, in which a link-timeslot assignment problem is formed by considering interference constraint to achieve better spatial timeslot reuse. A constraint for achieving consecutive flow for multi-hop transmission is also introduced to avoid modifications on the original CSMA protocol, and hence the proposed scheduling is standard compliant. Our results show that the proposed scheduling reduces the total data collection time of Wi-SUN AMI network with 100(200) smart meters by 59%(48%), 33%(32%), and 19%(13%), respectively, when compared with conventional Wi-SUN AMI, conventional TDMA scheduling and heuristic scheduling based on 2-rank distance interference model.

Accurate wireless interference identification (WII) is vital for wireless industrial internet of things (IIoT) network to coexist with other technologies in the crowded 2.4 GHz unlicensed band. Deep learning (DL) based methods have emerged as a promising candidate for such type of task. However, to achieve good accuracy, DL methods require large amount of labeled training data, which comes from tedious annotation work by domain expert. In contrast, unlabeled data is easier to obtain. In this paper we present a semi-supervised DL based WII algorithm which combines temporal ensembling technique with CNN network to exploit unlabeled data to improve the performance. The proposed algorithm is able to differentiate interference from multiple wireless standards accurately with reduced number of labels, such as IEEE 802.11, IEEE 802.15.4 and IEEE 802.15.1. Specifically, the proposed algorithm achieves 90% accuracy with less than 2% of labeled data with medium to high signal SNR. Extensive simulation results show that the proposed algorithm achieves a better classification accuracy than benchmark algorithms under various SNR conditions and with different number of labeled data.

In this work, energy detection‐based spectrum sensing for multiple antenna receiver under the effect of mobility is investigated by considering L number of correlated antenna branches. The authors consider the uniform, exponential and arbitrarily correlation among the antenna branches based on the spacing between them. The moment generating function (MGF) approach is applied to obtain the statistical knowledge of the received signal to noise ratio because the Laplace domain behaviour will help to derive the closed‐form expressions using simple algebraic operations. They derived the closed‐form expressions for the detection probability over Nakagami‐ m fading, in terms of Lauricella and Confluent Hypergeometric function for maximal ratio combining (MRC) and equal gain combining (EGC) diversity techniques under the effect of vehicle mobility. Monte‐Carlo simulation is carried out to validate the derived analytical expressions. The results show that the degradation in detection performance due to fading correlation can be reduced by choosing the appropriate diversity scheme and by increasing the number of antennas. Furthermore, they also found that at high fading parameter ( ) value, the low value of the probability of false alarm and highly correlated fading, MRC works better than EGC for high relative velocity.

In this article, we propose a semi-blind full-duplex (FD) amplify-and-forward (AF) relay system with adaptive self-interference (SI) processing assisted by independent component analysis (ICA) for low-latency and high-reliability (LLHR) Internet of Things (IoT). The SI at FD relay is not necessarily canceled as much as possible like the conventional approaches, but is canceled or utilized based on a signal-to-residual-SI ratio (SRSIR) threshold at relay. According to the selected SI processing mode at relay, an ICA-based adaptive semi-blind scheme is proposed for signal separation and detection at destination. The proposed FD relay system not only features reduced signal processing cost of SI cancelation but also achieves a much higher degree of freedom in signal detection. The resulting bit error rate (BER) performance is robust against a wide range of SRSIR, much better than that of conventional FD systems, and close to the ideal case with perfect channel state information (CSI) and perfect SI cancelation. The proposed system also requires negligible spectral overhead as only a nonredundant precoding is needed for ambiguity elimination in ICA. In addition, the proposed system enables full resource utilization with consecutive data transmission at all time and same frequency, leading to much higher throughput and energy efficiency than the time-splitting and power-splitting-based self-energy recycling approaches that utilize only partial resources. Furthermore, an intensive analysis is provided, where the SRSIR thresholds for the adaptive SI processing mode selection and the BER expressions with ICA incurred ambiguities are derived.

We consider a homogeneous cellular network where a multi-antenna base station (BS) in each cell transmits messages to its intended user over a common frequency band. To improve the system capacity of this multi-cell multi-input single-output (MISO) interference channel, one of the state-of-the-art algorithms, namely, downlink-beamforming coordination, allows all BSs to cooperate with one another to mitigate the effect of inter-cell interference. However, most existing algorithms are suboptimal and impractical in a dynamic wireless environment, due to the high computational complexity and the overhead involved in collecting global channel state information (CSI). In this study, we exploit deep reinforcement learning (DRL) and propose a distributed dynamic downlink-beamforming coordination (DDBC) method with partial observability of the CSI. Each BS is able to train its own deep Q-network and employs appropriate beamformer depending on its environment, which is observed through a designed limited-information exchange protocol. The simulation results show that the proposed DRL-based DDBC method, with a considerably lower system overhead, achieves a system capacity that is very close to that of the fractional programming algorithm with global and instantaneous CSI measurements. In addition, this work demonstrates the potential of utilizing DRL to solve DDBC problems in a more practical manner.

In this paper, we propose efficient wireless power transfer (WPT) policies for various practical scenarios in wirelessly powered communication networks (WPCNs). First, we consider WPT from an energy access point (E-AP) to multiple energy receivers (E-Rs). We formulate the problem of maximizing the total average received power of the E-Rs subject to power constraints of the E-AP, which is a non-convex stochastic optimization problem. Using eigenvalue decomposition techniques, we derive a closed-form expression for the optimal policy, which requires the distribution of the channel state information (CSI) in the network. We then propose a near-optimal policy that does not require this knowledge and prove that its optimality gap can be decreased at the cost of increment in its convergence time. Next, we consider fairness among the E-Rs and propose a quality of service (QoS) aware fair policy that provides fairness and guarantees the required QoS of each E-R. Finally, we study a WPCN where the E-Rs utilize their received energy to transmit information to the E-AP. We maximize a generic fair network utility under the E-Rs' QoS constraints and the E-AP's power constraints. Numerical results show a significant improvement of O(log N) in the total throughput compared to the state-of-theart baselines.

This paper proposes a peer-assisted power supply approach for a wireless powered Internet of Things (IoT) non-orthogonal multiple access (NOMA) network in which passive user equipments (UEs) without battery harvest energy from active UEs equipped with power supply. Specifically, passive UEs harvest energy from active UEs during their uplink transmission using NOMA. They then upload information along with the active UEs. Particularly, considering the combination of time division multiple access (TDMA) and NOMA, under the assumption that the power of active UEs is fixed, we study different transmission modes (non-stand-alone/stand-alone) and different operations (NOMA/NOMA-plus-TDMA). Taking into account the practical applications, we re-investigate the above schemes in the scenario where active UEs' energy is limited, i.e., the power of active UEs is not fixed and is affected by time allocation. We maximize the sum-throughput of each proposed model. We prove that the optimization problems for all cases are convex, and we obtain closed-form solutions for most cases. Finally, we show by simulations that, in all cases, the transmit power of active UEs and the number of UEs have a positive effect on the sum-throughput. Besides, in terms of maximizing the sum-throughput, the NOMA-plus-TDMA operation outperforms the NOMA operation. If active UEs' power is fixed, the non-stand-alone transmission outperforms the stand-alone transmission, and vice versa.

In this paper, we study the secrecy throughput in a massive Multiple-Input-Multiple-Output (MIMO) full-duplex wireless powered communication network (WPCN). The network consists of a massive MIMO base station (BS) and two groups of single-antenna sensor nodes which harvest energy from the BS. The first group, referred to as information transmitters (ITs), use the harvested energy to transmit information back to the BS; the second group, referred to as energy receivers (ERs), use the harvested energy for non-transmission related operations. We consider a two-slot protocol. In the first time slot, all nodes harvest energy from the BS. In the second slot, ITs transmit information to the BS, while BS use one set of its antennas to receive the signals and the other set of antennas to transmit artificial noise (AN) to the ERs. The AN serves two purposes: wireless power transfer to the ERs, and information security for the ITs with ERs, which are considered as potential eavesdroppers. We aim to maximize the total secrecy throughput of ITs subject to the ERs' received energy constraints. The problem is shown to be non-convex. To tackle the problem, we propose a twostage suboptimal approach, referred to as Maximizing Received AN aided Secrecy Throughput Maximization (MRAN-STM). In the first stage, the transmitted AN is optimized to maximize the minimum received AN of all the ERs. Then, in the second stage, the power allocation and time slot duration are optimized to maximize the total secrecy throughput. Numerical results show the improvement of the proposed algorithm.

In this paper, we investigate the secrecy throughput for a full-duplex wireless powered communication network. A multi-antenna base station (BS) transmits energy towards nodes all the time and each node harvests energy prior to its transmission time slot. Nodes sequentially transmit their confidential information to the BS in presence of other nodes which are considered as potential eavesdroppers. We derive the secrecy rate and formulate the sum secrecy throughput optimization of all nodes. The optimization variables are the time slot duration and the BS beamforming during different transmission phases. The problem is non-convex and non-trivial. We propose a suboptimal approach in which the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during the information transmission phase, with which we then obtain the optimum beamforming in each time slot and its duration. We compare our algorithm with uniform time slot and uniform beamforming in different settings and demonstrate its superior performance.

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, noninterfering manner with the primary network, thus being a promising approach to solve the problem of spectrum scarcity. Energy Detection, a spectrum sensing technique for DSA/CR systems, is widely used for blind sensing of unused frequency bands due to its non-parametric sensing ability and computationally low complexity. However, spectrum sensing becomes more challenging in Cognitive Vehicular Networks (CVNs) due to Secondary User's (SU's) mobility and often yields a detection performance loss as compared to static scenarios. In order to mitigate the impact of reduced detection performance due to mobility, the usage of an improved version of energy detection technique is proposed in this paper. Usage of Improved Energy Detection (IED) technique in CVNs results more than 10% increment in DSA/CR system performance. In this paper, we study the joint impact of SU's sensing range, PU's protection range and SU's mobility model on the PU Activity using IED technique in CVNs, with detection probability and probability of false alarm as the performance metrics. Also, we derive a closed form expression for the probability of PU being inside SU's sensing range. Based on the proposed framework, numerical results show great agreement with analysis, yielding a superior performance.

Broadband power line communication (BPLC) and television white space (TVWS) are regarded as promising candidates for indoor broadband applications of the Internet of Things. However, they share the access to the very high frequency (VHF) band, which could cause harmful interference and performance degradation to each other. In this paper, a TVWS regulated BPLC system is proposed for point-to-multipoint downlink communication, which integrates the requirement of primary user sensing and the permissible transmission power spectral density (PSD) for TVWS users into the BPLC standard, regarding VHF band access. This integration guarantees minimum interference level between TVWS and BPLC and allows higher transmission PSD for BPLC users in VHF, and hence higher capacity and coverage for BPLC.

In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max-min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.

In this paper, we study high reliability, low latency and cost effective network planning for industrial wireless mesh networks. Based on the requirements of routing reliability, minimum end-to-end delay and reduced deployment cost in wireless mesh networks, such as WirelessHART network, we propose three network planning approaches following the principles of the shortest hops, the least routers and balance of the shortest hops and the least routers, respectively. We then implement the proposed algorithms respectively to generate the network deployment for a given factory layout. Simulation results show that there exists a performance trade-off between these three algorithms. The proposed algorithms have also been implemented and validated in an NS-2 WirelessHART network simulator.

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.

Context and location services become increasingly important in many Internet of Things (IoT) applications such as smart home, disaster assistance, security and safety in smart factory and so on. Traditionally, these services are provided by independent systems. In this paper, we propose a software defined radio (SDR) based multi-function radar for respiratory rate and moving speed detections as well as ranging, which can be used to provide human body detection and tracking in human-centric IoT applications. Since SDR concept is adopted, the same hardware platform could be shared with wireless communications periodically by re-defining. The system architecture design is introduced. The detection algorithms are presented. The ranging performance is compared with some typical ones using simulations. The detections for respiratory rate, ranging and moving speed are carried out on the SDR platform consisting of FMCOMMS5 and ZC706. The system occupies 2MHz bandwidth. It is shown that the proposed multi-function radar achieves the estimation of the human respiratory rate with <;5% errors compared to a clinic-grade device. The ranging resolution achieved is <;0.3 m. The tiny movement as slow as 0.19 m/s can be detected.

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.

In this paper, we study automated network planning for industrial wireless mesh networks. Based on the requirements of routing reliability in WirelessHART network, we propose two network planning approaches following the principles of the shortest hops and the least routers, respectively. We then implement them in Algorithms 1 and 2 respectively to automatically generate the network deployment for a given factory layout. Simulation results show that there exists a performance tradeoff between the two algorithms. The proposed algorithms have also been implemented and validated in an NS-2 WirelessHART network simulation.

With the increasing usage of software defined radio and digital signal processing, the hardware and RF frontend for radar and wireless communication tends to be similar. Thus, using the same RF and hardware platform for joint radar-communication becomes viable. Joint radar-communication would bring more efficient plan and usage for the radio spectral resource. Furthermore, it could enable new applications which require information exchange and precise localization at the same time. In this paper, efficient algorithms are proposed to use standardized waveform in wireless communication (IEEE 802.11ad, IEEE 802.11p, etc) for range and speed detection of targets. Novel approach is found to cancel the self-interference due to full duplexing operation. Detection performances are verified by simulations.

Low-latency and high-reliability communication is one of the crucial building blocks for many Internet-of-things (IoT) applications. It is observed that many new applications raise a sporadic traffic with short messages, in which case considerable amount of communication resource is dominated by control data. A joint blind deconvolution and blind demixing approach has recently been proposed to reduce the signaling overhead by allowing the recovery of multiple pairs of signal and channel based on only one received signal. However, the existing approach is not capable to verify its own recovery performance, making it vulnerable to jamming attacks. In this paper, we introduce a novel scheme for stable signal recovery under jamming attacks. Our method of adding a guarding node to the network is able to detect the jamming attack, reject the jamming signals and recover the legitimate signals. We also propose an iterative reweighted optimization algorithm that supports automatically learning of the optimal regularization parameter. Numerical experiments demonstrate that the proposed approach achieves about 4 dB reduction in average recovery error.

Energy sharing among energy harvesting base stations (BSs) has the potential to improve the utilization of the harvested renewable energy. However, not much work has been done to optimize the power sharing among BSs while considering the topology of the cellular network and the distance-dependent power loss (DDPL) in the transmission lines. In this paper, we propose two power sharing optimization algorithms: the min-cost-max-flow (MCMF) algorithm and the max-flow (MF) algorithm. The MCMF algorithm optimizes the power sharing considering the DDPL, and therefore shares the power over much shorter distances whereas the MF algorithm optimizes the power sharing without considering the DDPL. Our numerical results show that for cellular networks with moderate DDPL value, the MCMF saves up to 10%, 22%, and 30% more power than the MF algorithm for 5, 10, and 15 BSs uniformly distributed in a square of unit length where every pair of BSs can share power, respectively. In contrast, for cellular networks with very high or very low DDPL value, the performance difference between the two algorithms is negligible. In addition, the performance gain of MCMF over MF increases with the BS density.

Optimizing non-orthogonal multiple access (NOMA) in multi-cell scenarios is much more challenging than the single-cell case because inter-cell interference must be considered. Most papers addressing NOMA consider a single cell. We take a significant step in analyzing NOMA in multi-cell scenarios. We explore the potential of NOMA networks in achieving optimal resource utilization with arbitrary topologies. Towards this goal, we investigate a broad class of problems consisting of optimizing power allocation and user pairing for any cost function that is monotonically increasing in time-frequency resource consumption. We propose an algorithm that achieves global optimality for this problem class. The basic idea is to prove that solving the joint optimization problem of power allocation, user pair selection, and time-frequency resource allocation amounts to solving a so-called iterated function without a closed form. We prove that the algorithm approaches optimality with fast convergence. Numerically, we evaluate and demonstrate the performance of NOMA for multi-cell scenarios in terms of resource efficiency and load balancing.

Network anomaly detection in large scale sensor networks is a fundamental task in many Internet of Things (IoT) applications. Given an incomplete set of corrupted observations of the network matrices, the problem of network matrix recovery and anomaly detection can be formulated as a low-rank matrix completion problem with a fraction of the observed entries being corrupted by outliers. Although many centralized algorithms have been proposed to solve the low-rank matrix completion problem, they generally require the observations to be centrally available, which can incur many problems in practical applications, e.g., power budget constraints, single point failure and privacy concern. Recently, a decentralized nuclear-norm minimization-based algorithm was developed to solve the low-rank matrix completion problem in a mesh network. In this paper, we consider a two-tier network and propose a new decentralized approach based on the Riemannian optimization. Our proposed clustering and consensus sharing method achieves a balance between the performance guarantee and the computational cost: the proposed method distributes the computational burden over the agent nodes, while exhibits a recovery performance close to its centralized counterpart. In addition, our Riemannian optimization-based approach scales well with the size of the problem, hence it is more favoured for handling large data sets in IoT than the nuclear-norm minimization-based algorithm. Numerical simulations are performed to demonstrate the effectiveness of the proposed approach, which is able to solve problems of size 2000×2000 of rank 5 with 50 agent nodes in 10 seconds.

Photovoltaic (PV) cell powered base stations (BSs) have been widely considered for reducing the cellular network's environmental footprint in the future. An inherent challenge is to match the energy generation profile with the energy consumption profile. In this paper, we develop a Markov chain based algorithm to determine the optimal orientation angle of the PV cell for matching the two profiles, given the energy generation profile of the geo-location, the load profile of the BS and the battery capacity. We investigate the effects of different battery capacities on the optimized PV cell orientation angle for BSs located in a business district in London in summer. Our results show that the optimal orientation angle for a small battery capacity (10000 Joules at a small BS) is in the range from 35° to 60°, whereas a wider range from -50° to 60° could be chosen for a large battery capacity (50000 Joules at a small BS). This reveals that PV cell orientation angle optimization is more important for PV cell powered BSs with small battery capacities than for large battery capacities.

In this paper, we aim to design and optimize the performance of IEEE 802.11ad-based dense network in cabin environment. For a typical airplane model, we propose a suitable network deployment and build up the directional antenna model and path loss model. We also develop an electromagnetic algorithm to predict the in-cabin 60 GHz radio propagation characteristics and the channel model. Considering the blockage effects, we further investigate the link level and system level performance of IEEE 802.11ad networks for different modulations and different cabin regions. The simulation results validate that the IEEE 802.11ad network is applicable to the wireless in- flight entertainment and communication (WIFEC) services. Moreover, the adopted rate adaption scheme dramatically improves the robustness of IEEE 802.11ad networks, which achieves better performance than fixed modulation scheme.

Previously, a scheme in [1] was proposed for the outdated channel state information (CSI) problem, for data transmission in time division duplex (TDD) systems. In user movement environment, the actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation. In this paper, the effect of different user mobility on TDD downlink multiuser distributed antenna system is investigated. An efficient autocorrelation based feedback interval technique is proposed and updates CSI at less the cost of the downlink time slots. In the proposed technique, the frequency of CSI feedback for different users is proportional to their speed. Cooperative clusters are formed to maximize sum rate where channel gain based antenna selection and user clustering based on signal-to- interference plus noise ratio (SINR) threshold is applied to reduce computational complexity. Numerical results show that sum rate superiority of the proposed scheme over the user mobility.

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.

Although pre-shared key schemes are popularly used in securing communication systems, they are impractical in some applications such as ad-hoc communications. This paper proposes a new method for secret key generation between wireless endpoints. In contrast to previous studies, the present method utilizes a joint time-frequency multiscale features of wireless channels, thus yields higher bit-rate and/or larger secret key, as demonstrated in the numerical evaluations and experiments.

Motivated by the high data rate requirement, full- duplex (FD) multiple-input multiple-output (MIMO) has attracted much attention in both academia and industry. However, FD and MIMO techniques require high power consumption. To strike the balance between the data rate and power consumption, we propose a novel hybrid duplexing strategy, in which antennas are distributed across the service areas and are capable of working in hybrid modes of FD, half- duplex (HD) and sleeping mode, leading to higher energy efficiency (EE) than FD mode only due to enhanced degree of freedom. Based on the hybrid duplexing strategy, we maximize system EE by jointly designing transmitting/receiving chains' activation/deactivation at DAs, downlink beamformer, and uplink transmission power. Novel optimization algorithms are developed. Simulation results confirm that the hybrid duplexing distributed antenna (DA) system provides significant EE improvement over the conventional centralized FD MIMO system, showing its green evolution and applicability to future network deployment.

Non-orthogonal multiple access (NOMA) allows multiple users to simultaneously access the same time-frequency resource by using superposition coding and successive interfer- ence cancellation (SIC). Thus far, most papers on NOMA have focused on performance gain for one or sometimes two base stations. In this paper, we study multi-cell NOMA and provide a general framework for user clustering and power allocation, taking into account inter-cell interference, for optimizing resource allocation of NOMA in multi-cell networks of arbitrary topology. We provide a series of theoretical analysis, to algorithmically en- able optimization approaches. The resulting algorithmic notion is very general. Namely, we prove that for any performance metric that monotonically increases in the cells' resource consumption, we have convergence guarantee for global optimum. We apply the framework with its algorithmic concept to a multi-cell scenario to demonstrate the gain of NOMA in achieving significantly higher efficiency.

Despite the increasing interest in photovoltaic (PV) cell powered small-cell base stations (SBSs), it has not been sufficiently studied yet how different PV cell angles can be utilized to achieve a good match between the energy arrival and consumption at the SBS. This is especially important in an urban environment where cellular network operators often struggle to apply optimal angles to the PV cells due to implementation constraints or shadowing effects of surrounding buildings. We develop an energy generation, storage and consumption model of a PV cell powered SBS, which includes the effects of PV cell orientations. A linear optimization problem is derived to optimize the energy performance of the SBS throughout the day. The effects of different PV cell orientations on the green energy utilization are evaluated assuming deployment in London in summer and winter. Our results show that west orientated PV cells (45° misalignment to the southern direction) are preferred in London during summer, whereas south orientated ones are preferred during winter. This reveals that the PV cell orientation needs to be optimized for not only the location and weather of the SBS deployment site but also the network traffic distribution in time (or energy consumption profile) of the deployment location.

To address the problem of unbalanced received signal-to-interference-and-noise ratio (SINR) at relay and destination nodes in wireless power transfer (WPT)- supported relay system, we propose a novel asymmetric full-duplex (FD) decode-and-forward (DF) WPT relay strategy, where the transmission time slots are not necessarily identical. By introducing asymmetric time slots, higher degree of freedom is obtained than the conventional symmetric WPT relay system. Furthermore, based on the asymmetric strategy, we develop a spectral efficiency (SE)- oriented resource allocation algorithm by jointly designing time slots, transmission power at source and relay. Simulation results show that the proposed asymmetric system demonstrates higher SE than the symmetric WPT-powered FD and the time- switching based FD relay systems. Besides, more energy can be harvested at the relay node by the proposed system benefiting from the enhanced degree of freedom, showing its applicability in WPT-powered relay systems.

In this paper, we study the performance of partial-fading and Rayleigh fading wireless networks using stochastic geometry. The aim is to provide closed-form bounds for the signal-to-interference-plus-noise ratio (SINR) distribution, average Shannon rate, and outage rate. We first characterize the SINR distribution of partial-fading channels through the Laplace transform of the inverted SINR. Since most communication systems are interference limited, we also consider the case of negligible noise power, and derive the upper and lower bounds for the signal-to-interference ratio distribution under both partial fading and fading cases. These bounds are of closed forms and thus more convenient for theoretical analysis. Based on these derivations, we obtain closed-form bounds for the average Shannon and outage rates. These results are useful for investigating the fifth-generation communication systems, for example massive multi-antenna networks as described in our illustrative example.

In this paper, we propose a two-step algorithm that can achieve any tradeoff point between antenna selection complexity and performance for a massive multiple-input multiple-output (M-MIMO) system. The first and second steps target low complexity and high performance, respectively, in the antenna selection. For the first-step antenna selection, we propose a correlation-based best-first selection algorithm that selects the least spatially correlated antennas. For the second-step selection, we consider a performance-aware algorithm that maximizes the singular values of a selected channel matrix. By adjusting the number of selected antennas in each step, we can actively balance the complexity and performance of the M-MIMO system. The computational complexity and the bit-error-rate performance of various antenna selection algorithms have been analyzed and compared. The investigation in the paper provides a good reference for further study for an antenna selection-based M-MIMO system.

In this paper, we study the downlink performance of two important 5G network architectures, i.e. massive multiple-input multiple-output (M-MIMO) and small-cell densification. We propose a comparative modeling for the two systems, where the user and antenna/base station (BS) locations are distributed according to Poisson point processes (PPPs). We then study the SIR distribution and the outage rate of each network. By comparing these results, we observe that for user-average spectral efficiency, small-cell densification is favorable in crowded areas with moderate to high user density and M-MIMO with low user density. However, small-cell systems outperform M-MIMO in all cases when the performance metric is the energy efficiency. The results of this paper are useful for the optimal design of practical 5G networks.

In the present paper, we investigate the design and optimization for fronhaul links in cloud radio access networks (C-RAN). Existing C-RAN designs rely on the instantaneous network-wide channel state information (CSI), which might impose a significant overhead due to the potential large-scale of C-RAN. To overcome this limitation, we optimize C-RAN based on the average performance metrics which only require the second-order statistics of the fading channels. Firstly, a tight upper bound of the block error rate (BLER) over Rayleigh fading channels is derived in closed-form expression, through which some insights on C-RAN are drawn: i) full diversity order, which is equal to the number of RRHs, is achievable with respect to the signal to compression plus noise ratio; and ii) the BLER is limited below by either compression or Gaussian noises. Secondly, based on the derived bound, a compression optimization is proposed to minimize the fronthaul transmission rate while satisfying some predefined BLER constraints. The premise of the proposed optimization originates from practical scenarios where most applications tolerate a non-zero BLER. Finally, a fronthaul rate allocation scheme is proposed to minimize the system BLER. It is proved that the proposed allocation scheme, which imposes uniform compression noise across the RRHs, approaches the optimal allocation as the total fronthauls' bandwidth increases.

Energy efficiency (EE) is an important issue at 60 GHz due to high power consumption of devices working at such high frequency. In this paper, we investigate EE optimization for full-duplex (FD) decode-of-forward (DF) relay-assisted 60 GHz multiuser indoor networks. In contrast to the existing spectral efficiency (SE) optimization, our scheme maximizes system EE for FD relaying system under cross-layer constraints, addressing the typical problems at 60 GHz, such as the effect of imperfect channel estimation due to serious signal blockage. A low-complexity EE-orientated optimization algorithm is proposed, by which the transmission power, subcarriers and throughput are allocated jointly across multiple users. Simulation results verify our analytical results and confirm that the proposed algorithm achieves a higher EE than the SE-oriented approach, while offering a comparable SE. Also, FD relaying with the proposed algorithm outperforms HD relaying in terms of both EE and SE. In addition, a much lower throughput outage probability is guaranteed by the proposed algorithm, showing its robustness against channel estimation errors. A full range of power consumption sources and imperfect self-interference cancellation are considered to rationalize our analysis.

In this paper, we present a user equipment (UE) based distributed traffic steering mechanism between long-term evolution (LTE) and Wi-Fi networks. An agent residing in each UE evaluates the traffic condition of the network it is currently connecting to and makes the traffic steering decision. The evaluation is either periodic or event-driven such as access denial in the admission control due to network congestion. The learning mechanism enables each UE to use the locally available information at the UE and select the proper network under dynamic network conditions. The computer simulation results show that the proposed mechanism achieves low outage probability and small number of network switching with even less information than or almost the same as the existing method. We have also implemented the proposed traffic steering mechanism as an APP on android platform and verified that the proposed mechanism works effectively in real-time testing.

Broadband power line communication (BPLC) is a promising technology for indoor broadband communication networks. However, BPLC has restricted transmission power in the very high frequency (VHF) band in order to avoid harmful interference to existing wireless services. In this paper, a new hybrid multiple-input multiple-output (MIMO) system is proposed between BPLC and TV white space (TVWS) channels to enhance the system capacity over BPLC in VHF. In order to satisfy the interference limit at the TV primary user (PU) receiver (Rx), an iterative precoding algorithm is proposed to enhance the overall ergodic capacity. Simulation results verify the effectiveness of the proposed hybrid system over the previous cognitive BPLC system.

The provision of Quality of Experience (QoE) for wireless video streaming has become a necessity. From this perspective, we design a QoE-aware scheduling (QoEAS) scheme for video streaming over IEEE 802.11n/ac networks. We consider an application scenario where videos are streamed in an area with high density of users. To improve the streamed video quality, we enhance the conventional gradient based scheduling scheme (U'R) by incorporating the packet delay and channel transmission rate with video packet importance index in scheduling. This index is derived based on the gradient of the QoE function for video, which accurately reflects the usefulness of a packet in enhancing the video quality. To be compatible to practical networks such as IEEE 802.11n/ac, the packet importance index is embedded in the Type-of-Service (ToS) field of IP header and is employed at Media Access Control (MAC) layer to perform scheduling. The proposed QoEAS significantly enhances the streamed video quality and achieves good fairness among users. It surpasses the conventional scheme by as much as 5 dB in Peak Signal-to-Noise Ratio (PSNR) at the lower 10 percentile of the users. To demonstrate the superiority and practicability of QoEAS, we also set up a lab test bed on a real 802.11ac network. Compared to conventional schemes, the proposed QoEAS shows notable QoE enhancement in lab test.

In this paper, we propose an online energy management strategy to minimize the operational expenses incurred by cellular base stations powered by both renewable and conventional energy. Our proposed strategy uses an Auto Regressive Integrated Moving Average (ARIMA) time series model for inter-day forecasting, a Markov chain model for intra-day predictions, and linear programming techniques for optimizing the decision variables on a real-time basis. To the best of our knowledge, the potential of these techniques has not been sufficiently explored in the literature. We assume that the base station is equipped with a rechargeable battery and a solar panel. Moreover, we consider real-time electricity pricing, and the application of a net-metering policy, whereby consumers are allowed to add excess renewable energy to the grid and obtain kilowatt credits in return. To evaluate the performance of the proposed algorithm, we devise an offline strategy which assumes non-causal knowledge of renewable energy generation and hence provides an upper bound in performance. Finally, we present numerical results obtained using real meteorological data, practical solar panel specifications, and factual energy tariffs. Through simulations we benchmark the proposed algorithm against the genie-aided strategy, and show its robustness by considering random energy rates.

In this study, the authors investigate the resource allocation issue for sensing‐based orthogonal frequency‐division multiple access (OFDMA) cognitive radio networks. They consider a network consisting multiple secondary users (SUs) and a secondary base station (BS) implementing a two‐phase protocol. In the first phase, cooperative spectrum sensing is carried out to detect the vacant subchannels. In the second phase, SUs transmit data in the uplink to the BS by using OFDMA. They optimise the sensing parameters, transmit power and subchannel assignments jointly to minimise the total energy consumption with the constraints on SUs’ quality of service and detection probability of the primary user. This is a mixed binary integer programming problem which is NP (non‐deterministic polynomial‐time)‐hard and generally intractable. They represent the problem as a bilevel problem and propose two efficient algorithms to solve the slave and master subproblems. They also study the separate optimisation, in which the sensing parameters of SUs are set regardless of the allocated resources. They investigate the energy savings of joint versus separate optimisation using numerical experiments. The results show that the joint optimisation method can introduce up to 16% of energy saving in zero sensing signal‐to‐noise ratio with the same total transmission bandwidth of 2.5 MHz.

Energy-efficiency (EE)-oriented green communication design is an important issue at 60 GHz due to high power consumption of devices working at such a high frequency. In this paper, we investigate EE-oriented resource allocation for full-duplex (FD) decode-and-forward relay-assisted 60-GHz multiuser indoor systems. In contrast to the existing spectral efficiency (SE)-oriented designs, our scheme maximizes the EE for a FD relaying system under cross-layer constraints, addressing the typical problems at 60 GHz, such as the intermittent signal blockage caused by the small wavelength of millimeter-wave. A low-complexity EE-orientated resource allocation algorithm is proposed, by which the transmission power allocation, subcarrier allocation, and throughput assignment are performed jointly across multiple users. Simulation results verify our analytical results and confirm that the FD relaying with the proposed algorithm achieves a higher EE than the FD relaying with SE-oriented approaches, while offering a comparable SE. In addition, a much lower throughput outage probability is guaranteed by the proposed resource allocation algorithm, showing its robustness against channel estimation errors. A full range of power consumption sources and imperfect self-interference cancellation are considered to rationalize our analysis.

A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration for 5G systems. NOMA allows more than one user to simultaneously access the same frequency-time resource and separates multi-user signals by SIC. These render resource optimization in NOMA different from orthogonal multiple access. We provide theoretical insights and algorithmic solutions to jointly optimize power and channel allocation in NOMA. We mathematically formulate NOMA resource allocation problems, and characterize and analyze the problems' tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming to deliver near-optimal solutions. To gauge the performance of the solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource allocation scheme.

In this paper, we will first motivate the heterogeneous network as an evolutionary path to the fifth generation (5G) communications. We then present an agile software defined heterogeneous network architecture which virtualizes the various radio access networks such as cellular basestations and Wi-Fi access points and the various carrier frequencies from both licensed and unlicensed bands. The software defined heterogeneous network architecture can therefore support much more efficient resource utilization and meet the quality of service requirement. We will also share our work in context-aware Wi-Fi-cellular network traffic steering and mobility management as two use cases in the software defined heterogeneous network.

The channel state information (CSI) is used to optimise data transmission in time division duplex (TDD) systems, which is obtained at the time of channel estimation. The actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation in user movement environment. In this paper the impact of different user mobility on TDD downlink multiuser distributed antenna system is investigated. Based on mobility state information (MSI), an autocorrelation based feedback interval technique is proposed and updates CSI and mitigate the performance degradation imposed by the user speed and transmission delay. Cooperative clusters are formed to maximize sum rate and a channel gain based antenna selection and user clustering based on SINR threshold is applied to reduce computational complexity. Numerical results show that the proposed scheme can provide improved sum rate over the non cooperative system and no MSI knowledge. The proposed technique has good performance for wide range of speed and suitable for future wireless communication systems.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is considered as a candidate multi-user access scheme for 5G cellular systems. In this paper, we provide theoretical insights and solution algorithm for optimizing multi- user power and channel allocation in NOMA systems. We mathematically formulate the NOMA resource allocation problem and prove its NP-hardness. For solving the problem, we propose an algorithm combining Lagrangian duality and dynamic programming to deliver a competitive suboptimal solution. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance over orthogonal frequency division multiple access (OFDMA) as well as over other existing NOMA resource allocation scheme.

In this paper, we propose an incentive mechanism to motivate WiFi Access Points (APs) to provide data offloading service for the mobile network operator (MNO). Particularly, we propose using both salary and bonus to attract WiFi APs to participate in data offloading. Under the proposed incentive scheme, WiFi APs are rewarded not only based on the amount of offloaded data but also based on the quality of the offloading service. We investigate the interactions between theWiFi APs and the MNO using Stackelberg game. We derive the best response functions for WiFi APs (i.e. the optimal amount of data to offload), and show that pure strategy Nash Equilibrium (NE) always exists for the subgame. Then, given WiFi APs' strategies, we investigate the optimal strategy (i.e. the optimal salary and the optimal bonus) for the MNO to maximize its utility. It is shown that the proposed incentive mechanism is effective in stimulating WiFi APs to offload more data and provide higher quality of offloading service.

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.

In this paper, we propose an evolutionary algorithm to minimize the energy bill incurred by a cellular base station (CBS) that is equipped with a rechargeable battery. Assuming time-of-use electricity pricing, we formulate an optimization problem to minimize the energy expenditures incurred by the CBS. Unlike existing strategies, the proposed algorithm takes into account the non-linear properties of the rechargeable battery. More specifically, Peukert's law is used to model the energy loss incurred during the discharging operation. As a result, the energy-bill minimization problem is non-convex. We therefore use the concept of evolution strategy to devise an algorithm that minimizes the energy bill, and at the same time ensures maximum renewable energy usage. The proposed algorithm exhibits linear time complexity and is able to achieve convergence within a few hundreds of iterations. Finally, we use simulations to show that the proposed algorithm outperforms the solutions based on linearised models.

A distributed antenna system (DAS) consists of multiple baseband units (BBUs) connecting to distributed antennas (DAs) via dedicated access links. In this study, we investigate a DAS with limited channel state information (CSI) and consider an average rate of users as an objective, where the expectation is taken over the channel uncertainty. We propose two distributed precoder designs that are based on a rate lower bound and a rate upper bound, respectively. As a benchmark, coordinated precoder and cooperative dirty paper coding (DPC)-based precoder with full CSI are compared with our proposed algorithms. Numerical results verifies that the rate performance of our upper-bound based scheme with limited CSI approaches tightly the maximum rates of the full CSI schemes, while that of lower-bound based scheme is relatively worse.

Channel uncertainty degrades the performance of multiple-input multiple-output (MIMO) systems considerably. In the literature, capacity maximization for MIMO channels with imperfect channel state information (CSI) has been extensively investigated. However, the error probability minimization counterpart is less studied. In this paper, we aim to minimize the transmission error probability of MIMO systems with imperfect channel estimate and error covariance matrix available at the transmitter. We propose a precoder design which minimizes the maximum pair-wise error probability among every symbol pair. Compared with other schemes utilizing indirect alternatives, i.e., mean-square error (MSE) or approximated signal-to-noise ratio (SNR), numerical results show that the proposed design achieves a significant improvement in error performance.

We propose a opportunistic multicast scheduling scheme to exploit content reuse when there is asynchronicity in user requests. A unicast transmission setup is used for content delivery, while multicast transmission is employed opportunistically to reduce wireless resource usage. We then develop a multicast scheduling scheme for the downlink multiple-input multiple-output orthogonal-frequency division multiplexing system in IEEE 802.11 wireless local area network (WLAN). At each time slot, the scheduler serves the users by either unicast or multicast transmission. Out-sequence data received by a user is stored in user's cache for future use. Multicast precoding and user selection for multicast grouping are also considered and compliance with the IEEE 802.11 WLAN transmission protocol. The scheduling scheme is based on the Lyapunov optimization technique, which aims to maximize system rate. The resulting scheme has low complexity and requires no prior statistical information on the channels and queues. Furthermore, in the absence of channel error, the proposed scheme restricts the worst case of frame dropping deadline, which is useful for delivering real-time traffic. Simulation results show that our proposed algorithm outperforms existing techniques by 17 % to 35 % in term of user capacity.

In this paper, we propose a novel channel estimation method for massive multiple-input multiple-output (MIMO) systems operating in time-division duplexing (TDD) mode. By exploiting the fact that the degrees of freedom of the physical channel matrix are smaller than the number of free parameters, the channel estimation is formulated as an atomic norm denoising problem and solved efficiently via the alternating direction method of multipliers (ADMM). Both theoretical analysis and numerical simulations demonstrate that our proposed method outperforms existing ones in terms of the channel estimation performance.

This paper addresses the problem of timing synchronization at the receiver of an IEEE 802.11a/n/ac communications system. In such systems, typically with multiple antennas at the transmitter, cyclic delay diversity (CDD) is implemented in the transmitted signal to provide diversity and hence, performance improvement in the error rates. However, the use of CDD creates pseudo-multipaths which degrade the performance of conventional timing synchronization algorithms, due to the increased delay spread of the channel. This paper proposes a method that achieves fine timing synchronization with low latency in two concurrent steps. The proposed scheme first achieves a coarse synchronization point so that important receiver operations can first take place. A fine timing synchronization algorithm operates concurrently to achieve a more accurate synchronization point and, to avoid incurring additional latency, post-processing is performed on the frequency domain samples of the preambles to compensate for the difference in synchronization points. The proposed scheme is shown to achieve excellent packet error rate performance with low latency requirements.

In this paper, we propose an energy cost minimization strategy for cellular base stations (CBSs) that are jointly powered by renewable and conventional energy. Energy tariffs follow time-of-use and consumption-based block pricing. In addition, consumers are allowed to return energy to the utility, e.g. following a net metering policy. Assuming that the CBS is equipped with a finite-capacity battery, we formulate an optimization problem to minimize the energy expenses incurred over a finite time horizon. Through convex analysis, we show that this optimization problem is equivalent to a linear program. We then use the equivalent formulation to solve the original problem and thus to find the optimal battery management policy. We evaluate the performance of the proposed strategy in terms of pricing parameters such as the consumption limits and their associated financial penalties. Using simulations we show that the proposed strategy can effectively minimize the energy bill.

We provide a comprehensive energy efficiency (EE) analysis of the full-duplex (FD) and half-duplex (HD) amplify-and-forward (AF) relay-assisted 60-GHz dual-hop indoor wireless systems, aiming to answer the question of which relaying mode is greener (more energy efficient) and to address the issue of EE optimization. We develop an opportunistic relaying mode selection scheme, where FD relaying with one-stage self-interference cancellation (passive suppression) or two-stage self-interference cancellation (passive suppression + analog cancellation) or HD relaying is opportunistically selected, together with transmission power adaptation, to maximize the EE with given channel gains. A low-complexity joint mode selection and EE optimization algorithm are proposed. We show a counter-intuitive finding that with a relatively loose maximum transmission power constraint, FD relaying with two-stage self-interference cancellation is preferable to both FD relaying with one-stage self-interference cancellation and HD relaying, resulting in a higher optimized EE. A full range of power consumption sources is considered to rationalize our analysis. The effects of imperfect self-interference cancellation at relay, drain efficiency, and static circuit power on EE are investigated. Simulation results verify our theoretical analysis.

The rate maximization for the K-user interference channels (ICs) has been investigated extensively in the literature. However, the practical problem of minimizing the error probability with given signal modulations and/or data rates of the users is less studied. In this paper, by utilizing additional degrees of freedom from the improper signaling (versus the conventional proper signaling) , we seek to optimize the precoding matrices for the K-user single-input single-output (SISO) ICs to minimize pair-wise error probability (PEP) and symbol error rate (SER) with two proposed algorithms, respectively. Compared with conventional proper signaling and other state-of-the-art improper signaling designs, our proposed improper signaling schemes achieve notable error rate improvement in SISO-ICs under both the additive white Gaussian noise (AWGN) and cellular system setups with or without channel coding. Our study provides another viewpoint for optimizing transmissions in ICs and further justifies the practical benefit of improper signaling in interference-limited communication systems.

We consider the problem of minimization of sum transmission energy in cellular networks where coupling occurs between cells due to mutual interference. The coupling relation is characterized by the signal-to-interference-and-noise-ratio (SINR) coupling model. Both cell load and transmission power, where cell load measures the average level of resource usage in the cell, interact via the coupling model. The coupling is implicitly characterized with load and power as the variables of interest using two equivalent equations, namely, non-linear load coupling equation (NLCE) and non-linear power coupling equation (NPCE), respectively. By analyzing the NLCE and NPCE, we prove that operating at full load is optimal in minimizing sum energy, and provide an iterative power adjustment algorithm to obtain the corresponding optimal power solution with guaranteed convergence, where in each iteration a standard bisection search is employed. To obtain the algorithmic result, we use the properties of the so-called standard interference function; the proof is non-standard because the NPCE cannot even be expressed as a closed-form expression with power as the implicit variable of interest. We present numerical results illustrating the theoretical findings for a real-life and large-scale cellular network, showing the advantage of our solution compared to the conventional solution of deploying uniform power for base stations.

In this paper, we consider a wireless communication network with a full-duplex hybrid energy and information access point and a set of wireless users with energy harvesting capabilities. The hybrid access point (HAP) implements full-duplex through two antennas: one for broadcasting wireless energy to users in the downlink and the other for simultaneously receiving information from the users via time division multiple access (TDMA) in the uplink. Each user can continuously harvest wireless power from the HAP until it transmits, i.e., the energy causality constraint is modeled by assuming that energy harvested in the future cannot be used for the current transmission. This leads to the causal dependence of each user's harvesting time on the transmission time of earlier users, e.g., the second user scheduled to transmit can harvest more energy if the first user has longer transmission time. Under this setup, we investigate the sum-throughput maximization (STM) problem and the total-time minimization (TTM) problem for the proposed full-duplex wireless-powered communication network. For the STM problem, the optimal solution is obtained as a closed-form expression, which can be computed with linear complexity. For the TTM problem, by exploiting the properties of the coupled constraints, we propose a two-step algorithm to obtain an optimal solution. Then, low-complexity suboptimal solutions are proposed for each problem by exploiting the characteristics of the optimal solutions. Finally, simulation studies on the effect of user scheduling show that different scheduling strategies should be adopted for STM and TTM.

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.

Optimizing activation and deactivation of base station transmissions provides an instrument for improving energy efficiency in cellular networks. In this paper, we study the problem of performing cell clustering and setting the activation time of each cluster, with the objective of minimizing the sum energy, subject to a time constraint of serving the users' traffic demand. Our optimization framework accounts for inter-cell interference, and, thus, the users' achievable rates depend on cluster formation. We provide mathematical formulations and analysis, and prove the problem's NP hardness. For problem solution, we first apply an optimization method that successively augments the set of variables under consideration, with the capability of approaching global optimum. Then, we derive a second solution algorithm to deal with the trade-off between optimality and the combinatorial nature of cluster formation. Numerical results demonstrate that our solutions achieve more than 40% energy saving over existing schemes, and that the solutions we obtain are within a few percent of deviation from global optimum.

This paper proposes a general model for offloading to an access point (AP) to minimize energy cost in a heterogeneous network with a renewable-energy powered base station (BS) that is connected to a microgrid. We consider the setting where both the BS and the AP can buy energy from the grid to satisfy their requirements, but the BS can sell back any excess renewable energy to the grid. The downlink scenario is considered whereby the BS and the AP cooperate to serve a set of users, and the problem is to determine the user assignment to the BS or the AP. In the specific case of time division multiple access, we show that the optimal user assignment problem is NP Hard. We propose a sub-optimal, low complexity algorithm for this setting and obtain an approximation guarantee to the worst case sub-optimality of our algorithm. Simulations show that our algorithm performs well, with only a small gap to the optimal exhaustive search method over the set of users.

In this paper, the sum-rate of a cognitive multiple access channel (C-MAC) is studied, where a secondary network (SN) with multiple secondary users (SUs) transmitting to a secondary base station (SBS) shares the spectrum band with a primary user (PU). An interference power constraint (IPC) is imposed on the SN to protect the PU. Under the IPC and the individual transmit power constraint (TPC) imposed on each SU, we investigate the power allocation strategies to maximize the sum-rate of the C-MAC without successive interference cancellation (SIC). We prove that the optimal solution must be at the extreme points of the feasible region. We show that Dynamic Time Division Multiple Access (D-TDMA) is optimal with high probability when the number of SUs is large. Furthermore, we show through simulations that the optimal power allocation to maximize the sum-rate of the C-MAC with SIC is optimal or near-optimal for our setting when D-TDMA is not optimal.

In this paper, we consider a wireless communication network with a hybrid access point (HAP) and a set of wireless users with energy harvesting capabilities. The HAP is assumed to have two antennas: one for transferring wireless energy in the downlink and one for receiving wireless information in the uplink. Without fixed energy sources, users first have to harvest energy from the wireless signals broadcast by the HAP, and then using the harvested energy to transmit their individual collected data to the HAP through dynamic-time-division-multiple-access (D-TDMA). All users can harvest the wireless energy prior to its transmission, and thus latter users can harvest more energy. We investigate the optimal time allocation to minimize the completion time of charging and transmitting all data subject to the constraints that all users have to send back some minimum amount of data. A high-efficient algorithm is then proposed to obtain an optimal time allocation of the formulated problem by exploring the properties of the constraints. It is also shown by simulations that the users with higher SNR should be scheduled to transmit first. Then, a suboptimal algorithm is also proposed based on the optimal time allocation. It is then shown by simulations that the suboptimal time allocation can achieve a close-to-optimal performance.

In this paper, we consider a wireless communication network with a hybrid access point (HAP) and a set of wireless users with energy harvesting capabilities. The HAP is assumed to have two antennas: one for transferring wireless energy in the downlink and one for receiving wireless information in the uplink. Without fixed energy sources, users first have to harvest energy from the wireless signals broadcast by the HAP, and then using the harvested energy to transmit their individual information to the HAP through dynamic-time-division-multiple-access (D-TDMA). We investigate the optimal time allocation to maximize the throughput of the proposed system subject to a total time constant. The formulated throughput maximization problem is proved to be a convex optimization problem. By using convex optimization techniques, the optimal time allocation strategy is obtained in closed-form expression. We show that the optimal time allocation can be obtained with linear complexity. It is then shown by simulations that the total throughput of the network increases with the number of users. It is also shown by simulations that the users with low SNR should be scheduled to transmit first.

The rate maximization for the K-user interference channels (ICs) has been investigated extensively in the literature. However, the dual problem of minimizing the error probability with given signal constellations and/or data rates of the users is less exploited. In this paper, by utilizing the additional degrees of freedom attained from the improper signaling (versus the conventional proper signaling), we optimize the precoding matrices for the K-user single-input single-output (SISO) ICs to achieve minimal transmission symbol error rate (SER). Compared to conventional proper signaling as well as other state-of-the-art improper signaling designs, our proposed improper signaling scheme is shown to achieve notable SER improvement in SISO-ICs by simulations. Our study provides another viewpoint for optimizing transmissions in ICs and further justifies the practical benefit of improper signaling in interference-limited communication systems.

We propose a precoder design to minimize the codeword error rate of multiple-input multiple-output (MIMO) systems in the presence of channel estimation errors. Our proposed scheme only requires knowledge of the second-order statistics of the channel, noise and the estimation errors; an estimate of the instantaneous channel state information (CSI) is not required. Compared to the instantaneous CSI requirement, our assumption is more practical since obtaining an accurate CSI is challenging if the channel fluctuates rapidly, while the channel statistics are likely to remain unchanged for a much longer period. Furthermore, the feedback overhead from the receiver to the transmitter is greatly reduced. When compared to the case without transmit preceding, our proposed design achieves a significant improvement in error performance: (i) for a real 2 × 2 MIMO system with 4-PAM modulation and at codeword error rate of 104, our proposed precoder design achieves coding gains of up to 6 dB and (ii) for a real 4 × 4 MIMO system with BPSK modulation and at codeword error rate of 104, coding gains of up to 6.5 dB can be achieved.

We consider the problem of sum transmission energy minimization in a cellular network where base stations interfere with one another. Each base station has to serve a target amount of data to its set of users, by varying its power and load, where the latter refers to the average level of channel resource usage in the cell. We employ the signal-to-interference-and-noise-ratio (SINR) load-coupled model that takes into account the load of each cell. We show analytically that operating at full load is optimal to minimize sum energy. Moreover, we provide an iterative power adjustment algorithm for all base stations to achieve full load. Numerical results are obtained that corroborate the analysis and illustrate the advantage of our solution compared to the conventional solution where uniform power is used for all base stations.

In this paper, we investigate resource allocation strategies for a point-to-point wireless communications system with hybrid energy sources consisting of an energy harvester and a conventional energy source. By assuming that the non-causal information of the energy arrivals and the channel power gains is known, a mixed integer programming problem is formulated to minimize the total energy cost of such a system over N fading slots under the energy harvesting constraints and a proposed outage constraint. The outage constraint requires that a minimum fraction of slots to be reliably decoded. This constraint is useful if, for example, an outer code is used to recover that all data bits. Optimal linear time algorithms are obtained for two extreme cases: when the number of outage slots is 1 or N -1. For the general case, a lower bound based on linear programming relaxation, and two suboptimal algorithms are proposed. Numerical simulations indicate that the proposed suboptimal algorithms exhibit only a small gap from the lower bound for a wide range of given parameters.

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.

In this paper, we study the problem of minimizing the energy cost incurred by a Cellular Network Operator (CNO) in a Smart Grid (SG) environment. We consider a CNO that deploys several Cellular Base Stations (CBS) to serve a given geographical area. Each CBS is equipped with a limited-capacity battery and can be powered either by the SG or by a renewable-energy (RE) harvester. Given this topology, two-way energy flow is allowed between each CBS and the SG and between any pair of CBSs in the network through the SG. The space-time-dependent energy-buying and energy-sharing costs and the energy-selling prices are made known to the CNO in advance. Therefore, in order to minimize the total cost incurred by the CNO, we find the optimal energy-management strategy by solving a constrained optimization problem. The proposed strategy ensures that the instantaneous energy demand of each CBS and the constraints imposed by each battery are satisfied at every point in time. We evaluate the performance of the proposed solution using simulations. Our results show that a significant cost reduction can be achieved by implementing the proposed strategy.

Sampling from certain probability distribution shows better recovery performance than uniform sampling in literature. However, a comprehensive theoretical analysis concerning more realistic signal models is still lacking. In this paper, we consider the sampling of stochastic processes and random fields in the Fourier domain. We propose a new variable-density sampling and linear reconstruction technique, and prove its theoretical recovery guarantee. For high dimensional random fields, uniform sampling requires a number of samples increasing exponentially with the dimension, while the variable density sampling scheme guarantees faithful recovery performance with a polynomial size of random samples.

We consider uplink WiFi data offloading in a heterogeneous network consisting of one LTE macrocell and one overlaid 802.11n WiFi hotspot to maximize the minimum energy efficiency (EE) of the users. We propose a closed-form expression to compute Nw, i.e., the number of offloaded users, and then offload the data traffic of Nw users with the lowest received SINR at the cellular base station (BS) to the WiFi network. Our simulation results show that our proposed offloading algorithm performs nearly the same as the exhaustive search and greedy algorithms, but requires much lower computational complexity. We also compare the EE performance of our proposed algorithm with naive schemes, i.e., no offloading, all offloading, and random selection, by taking into account the practical energy cost such as the transmit-power dependent energy consumption in LTE and the contention energy consumption in WiFi. We show that our proposed algorithm always outperforms naive schemes, especially when the user density in the hotspot is high.

The authors consider centralised cooperative spectrum sensing under correlated shadowing in this study. Formulating the spectrum sensing problem as a Gauss–Gauss hypothesis test, they use a linear quadratic rule and show that it is the optimal detector under Bayesian criterion. They derive the upper and lower Bhattacharyya bounds and investigate the error performance of spectrum sensing by studying the behaviour of these upper and lower bounds. They also study the asymptotic error performance in two different scenarios of finite and infinite area networks. They show that by increasing the number of nodes the sensing error probability approaches zero in both cases but with different decay rates. The lower the correlation between nodes or the larger the network area, the faster the decay.

In wireless communication systems, the nonlinear effect and inefficiency of power amplifier (PA) have posed practical challenges for system designs to achieve high spectral efficiency (SE) and energy efficiency (EE). In this paper, we analyze the impact of PA on the SE-EE tradeoff of orthogonal frequency division multiplex (OFDM) systems. An ideal PA that is always linear and incurs no additional power consumption can be shown to yield a decreasing convex function in the SE-EE tradeoff. In contrast, we show that a practical PA has an SE-EE tradeoff that has a turning point and decreases sharply after its maximum EE point. In other words, the Pareto-optimal tradeoff boundary of the SE-EE curve is very narrow. A wide range of SE-EE tradeoff, however, is desired for future wireless communications that have dynamic demand depending on the traffic loads, channel conditions, and system applications, e.g., high-SE-with-low-EE for rate-limited systems and high-EE-with-low-SE for energy-limited systems. For the SE-EE tradeoff improvement, we propose a PA switching (PAS) technique. In a PAS transmitter, one or more PAs are switched on intermittently to maximize the EE and deliver an overall required SE. As a consequence, a high EE over a wide range SE can be achieved, which is verified by numerical evaluations: with 15% SE reduction for low SE demand, the PAS between a low power PA and a high power PA can improve EE by 323%, while a single high power PA transmitter improves EE by only 68%.

Drill string acoustic telemetry is an effective transmission method to retrieve downhole data. Finite-difference simulations produce the comb-filter-like channel response (patterns of pass bands and stop bands) due to the presence of coupling joints in the metallic drill string. Practical pipes used for drilling deep wells have slight variation in length. The selection and arrangement of downhole pipes is important for improving the transmission efficiency of extensional waves transmitted through the drill string. Downhole drill string channel is studied using the transmission coefficients calculated from the transmission matrix method, and the resultant transfer function produces identical results similar to the finite-difference simulations. Reciprocity of the drill string structure is proved by comparing the pass band responses using the ascend-only (AO) and descend-only pipe arrangements. Transferred energies calculated up to 180 pipes of random length at the end of the drill strings using transmission coefficients for the three different pipe arrangements, namely, AO, descend-then-ascend, and ascend-then-descend (ATD), are compared to find the optimal pipe arrangement for single measurement. For the situations when pipes are distributed in sets, multiple measurements are required. In this paper, two sets of AO and two sets of ATD arrangements are analyzed for multiple measurements. ATD and$n$xATD arrangements are proposed as optimal pipe arrangements to produce the best possible telemetry performance in terms of optimal acoustic energy transfer via one- and two-way acoustic communication for single and multiple measurements, respectively.

In this paper, we propose a model for energy cooperation between cellular base stations (BSs) with individual hybrid power supplies (including both the conventional grid and renewable energy sources), limited energy storages, and connected by resistive power lines for energy sharing. When the renewable energy profile and energy demand profile at all BSs are deterministic or known ahead of time, we show that the optimal energy cooperation policy for the BSs can be found by solving a linear program. We show the benefits of energy cooperation in this regime. When the renewable energy and demand profiles are stochastic and only causally known at the BSs, we propose an online energy cooperation algorithm and show the optimality properties of this algorithm under certain conditions. Furthermore, the energy-saving performances of the developed offline and online algorithms are compared by simulations, and the effect of the availability of energy state information (ESI) on the performance gains of the BSs' energy cooperation is investigated. Finally, we propose a hybrid algorithm that can incorporate offline information about the energy profiles, but operates in an online manner.

We provide a general framework for the problem of data offloading in a heterogeneous wireless network, where some demand of cellular users is served by a complementary network. The complementary network is either a small-cell network that shares the same resources as the cellular network, or a WiFi network that uses orthogonal resources. For a given demand served in a cellular network, the load, or the level of resource usage, of each cell depends in a non-linear manner on the load of other cells due to the mutual coupling of interference seen by one another. With load coupling, we optimize the demand to be served in the cellular or the complementary networks, so as to maximize a utility function. We consider three representative utility functions that balance, to varying degrees, the revenue from serving the users vs the user fairness. We establish conditions for which the optimization problem has a feasible solution and is convex, and hence tractable to numerical computations. Finally, we propose a strategy with theoretical justification to constrain the load to some maximum value, as required for practical implementation. Numerical studies are conducted for both under-loaded and over-loaded networks.

In this paper, we investigate resource allocation strategies for a point-to-point wireless communications system with hybrid energy sources consisting of an energy harvester and a conventional energy source. In particular, as an incentive to promote the use of renewable energy, we assume that the renewable energy has a lower cost than the conventional energy. Then, by assuming that the non-causal information of the energy arrivals and the channel power gains are available, we minimize the total energy cost of such a system over N fading slots under a proposed outage constraint together with the energy harvesting constraints. The outage constraint requires a minimum fixed number of slots to be reliably decoded, and thus leads to a mixed-integer programming formulation for the optimization problem. This constraint is useful, for example, if an outer code is used to recover all the data bits. Optimal linear time algorithms are obtained for two extreme cases, i.e., the number of outage slot is 1 or N - 1. For the general case, a lower bound based on the linear programming relaxation, and two suboptimal algorithms are proposed. It is shown that the proposed suboptimal algorithms exhibit only a small gap from the lower bound. We then extend the proposed algorithms to the multi-cycle scenario in which the outage constraint is imposed for each cycle separately. Finally, we investigate the resource allocation strategies when only causal information on the energy arrivals and only channel statistics is available. It is shown that the greedy energy allocation is optimal for this scenario.

WiFi offloading is regarded as one of the most promising techniques for dealing with the explosive data increase in cellular networks due to its high data transmission rate and low requirement on devices. In this paper, we investigate the mobile data offloading problem through a third-party WiFi access point (AP) for a cellular mobile system. From the cellular operator's perspective, by assuming a usage-based charging model, we formulate the problem as a utility maximization problem. In particular, we consider three scenarios: 1) successive interference cancellation (SIC) available at both the base station (BS) and the AP; 2) SIC available at neither the BS nor the AP; and 3) SIC available at only the BS. For scenario 1, we show that the utility maximization problem can be solved by considering its relaxation problem, and the proposed data offloading scheme is near-optimal when the number of users is large. For scenario 2, we prove that with high probability the optimal solution is One-One-Association, i.e., one user connects to the BS and one user connects to the AP. For scenario 3, we show that with high probability there is at most one user connecting to the AP, and all the other users connect to the BS. By comparing these three scenarios, we prove that SIC decoders help the cellular operator maximize its utility. To relieve the computational burden of the BS, we propose a threshold-based distributed data offloading scheme. We show that the proposed distributed scheme performs well if the threshold is properly chosen.

In this paper, we consider a distributed transmitter (D-TX) system, in which each TX has a dissimilar power amplifier with different maximum output power, and different number of transmit antennas. To improve energy efficiency (EE) of the D-TX system, we design a multiuser multiple-input multiple-output (MU-MIMO) precoding matrix, a transmit antenna selection (AS) matrix, and a power control (PC) matrix. A conventional zero-forcing based MU-MIMO precoding is shown to be EE optimal for given AS and PC. Optimal and heuristic PC methods are proposed for given AS and MU-MIMO precoding. For the AS, we also propose heuristic algorithms. Average transmit power, outage probability, and EE performance are evaluated to compare three AS algorithms, and to observe the performance gap between the optimal and heuristic PC methods. From the numerical results, we discuss a tradeoff between AS complexity and EE performance and provide a useful guide for energy efficient D-TX system design.

In this paper we study the problem of energy management in cellular base stations powered by smart grids and renewable energy. The utility company, through the smart grid, offers hourly-varying electricity prices made known a day ahead to the base station. We formulate an optimization problem in which the cost function is defined as the billing cost of the energy consumed each day. We seek to minimize the cost function while meeting the energy demand of the base station. We assume that the base station is equipped with a finite-capacity battery and a renewable source of energy such as a solar panel. The battery incurs charging and discharging losses which are accounted in the problem formulation. We find the optimal energy management policy using linear programming techniques. Furthermore, we study how the optimal cost is affected by several system parameters such as: initial state of the battery, its capacity, maximum charging/discharging rates, losses, smoothness of the price profile and correlation between the price and the consumption profiles. Our results show that significant cost savings can be achieved by properly scheduling the battery.

We study the optimality of separate network-channel coding (SJNC) for three independent sources on the orthogonal access scheme. By SJNC, we mean that the network coded messages are formed from the binary XOR of the sources' messages, followed by channel encoding which is independent of the network coding. Decoding is performed jointly across the network and channel codes. Using standard random coding and joint typical set decoding, we obtain an achievable rate region for SJNC. By allowing time-sharing of codes, the rate region coincides with the capacity region of joint network-channel coding, where network and channel coding are performed jointly. This proves, surprising, that there is no loss of optimality for SJNC.

In this paper, we study the power consumption in relay networks of the 60 GHz wireless communication based on amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies. We propose a total power consumption model including drive power, decoding power, and power consumption of power amplifier (PA). This model is formulated as a function of drive power, which gives an easy access to the system level optimisation. The optimal drive power that minimises the total power consumption while satisfying the performance requirement can be found by numerical searching method. The impact of relay's locations on the total power consumption is also investigated. We show that, with the same performance requirement, in the small source-relay separation case AF consumes less power than DF, while with larger separation, AF consumes significantly more power than DF. This is different from the common intuition that DF is always more power consuming than AF due to the extra decoding power consumption at relay, which is due to the fact that the large source-relay separation limits the effective destination signal-to-noise ratio (SNR) in AF, leading to more substantial decoding power consumption in the many more decoding iterations than DF.

In this paper, we propose a model for energy cooperation between cellular base stations (BSs) with individual renewable energy sources, limited energy storages and connected by resistive power lines for energy sharing. When the renewable energy profile and energy demand profile at all BSs are deterministic or known ahead of time, we show that the optimal energy cooperation policy for the BSs can be found by solving a linear program. We show the benefits of energy cooperation in this regime. When the renewable energy and demand profiles are stochastic and only causally known at the BSs, we propose an online energy cooperation algorithm and show the optimality properties of this algorithm under certain conditions. Furthermore, the energy-saving performances of the developed offline and online algorithms are compared by simulations, and the effect of the availability of energy state information (ESI) on the performance gains of the BSs' energy cooperation is investigated.

We consider a time-division duplex (TDD) two-tier network, comprising macro- and femtocells coexisting in a co-channel operation mode, in which inter- and intra-tier interference are incurred. The proposed cell selection accounts for both uplink and downlink capacities. Comparison is made with path loss based and received signal strength based cell selections. Through simulation, we evaluate the performance of indoor and outdoor users, in terms of cdf, average capacity and 5%-outage capacity under different parameters. Our results indicate appealing performance of the proposed capacity based cell selection under TDD two-tier cellular networks.

In many communication scenarios, such as in cellular systems, the energy cost is substantial and should be conserved, yet there is a growing need to support many real-time applications that require timely data delivery. To model such a scenario, in this paper we consider the problem of minimizing the expected sum energy of delivering a message of a given size from a source to a destination subject to a deadline constraint. A relay is present and can assist after it has decoded the message. Causal channel state information (CSI), in the form of present and past SNRs of all links, is available for determining the optimal power allocation for the source and relay. We obtain the optimal power allocation policy by dynamic programming and explore its structure. We also obtain conditions for which the minimum expected sum energy is bounded given a general channel distribution. In particular, we show that for Rayleigh and Rician fading channels, relaying is necessary for the minimum expected sum energy to be bounded. This illustrates the fundamental advantage of relaying from the perspective of energy efficient communications when only causal CSI is available. Numerical results are obtained which show the reduction in the expected sum energy under different communication scenarios.

We propose a time slot based transmission strategy, referred to as adaptive coordinated napping (CoNap), for energy saving in cellular networks under time-varying traffic demand. In adaptive CoNap network, multiple neighboring base stations (BSs) form a cluster and each BS operates in either a transmit mode (TM) or a nap mode (NM) in each time slot. The dynamic assignment of TM and NM to each BS is implicitly coordinated among multiple BSs. This implicit coordination is realized by a binary general flickering pattern matrix (FPM) through adaptively selected mapping matrix (MM). To track the time-varying traffic demand, we develop an adaptive algorithm to dynamically select the appropriate MM from a predefined MM set by taking into account the network quality of service (QoS) requirement. Our numerical results based on a realistic energy consumption model in a cellular network show that as high as 40% saving can be achieved without compromising the network QoS.

We consider the problem of carrier frequency offset (CFO) estimation for a two-way relaying system based on the amplify-and-forward (AF) protocol. Our contributions are in designing an optimal preamble, and the corresponding estimator, to closely achieve the minimum Cramer-Rao bound (CRB) for the CFO. This optimality is asserted with respect to a novel class of preambles, referred to as block-rotated preambles (BRPs). This class includes the periodic preamble that is used widely in practice, yet this class provides an additional degree of design freedom via a block rotation angle. We first identify the catastrophic scenario of an arbitrarily large CRB when a conventional periodic preamble is used. We next resolve this problem by using a BRP with a non-zero block rotation angle. This angle creates, in effect, an artificial frequency offset that separates the desired relayed signal from the self-interference that is introduced in the AF protocol. With appropriate optimization, the CRB incurs only marginal loss from one-way relaying under practical channel conditions. To facilitate implementation, a specific low-complexity class of estimators is examined, and conditions for the estimators to achieve the optimized CRB are established. Numerical results corroborate with theoretical findings.

Optimal channel allocation is a key performance engineering aspect in single-carrier frequency-division multiple access (SC-FDMA). In SC-FDMA with localized channel assignment, the channels of each user must form a consecutive block. Subject to this constraint, various performance objectives, such as maximum utility, minimum power, and minimum number of channels, have been studied. We present a unified graph labeling algorithm for these problems, based on the structural insight that SC-FDMA channel allocation can be modeled as finding an optimal path in an acyclic graph. By this insight, our algorithm applies the concept of labeling and label domination that represent non-trivial extensions of finding a shortest or longest path. The key parameter in trading performance versus computation is the number of labels kept per node. Increasing the number ultimately enables global optimality. The algorithm's approach is further justified by its global optimality guarantee with strong polynomial-time complexity for two specific scenarios, where the input is user-invariant and channel-invariant, respectively. For the general case, we provide numerical results demonstrating the algorithm's ability of attaining near-optimal solutions.

Spectral efficiency (SE) and energy efficiency (EE) are key measures for wireless communication systems. In OFDM systems, the non-linear effects and inefficiencies of power amplifiers (PAs) have posed practical challenges for system design. In this paper, we analyze the impact of the PA on the SE and EE tradeoff of OFDM systems. We also propose a PA switching technique which achieves a better SE-EE tradeoff, e.g., the EE can be improved by 323% with 15% SE reduction.

In this paper, we employ a relay to assist in transmission from a source to a destination over a fixed number of time slots. We seek to minimize the expected sum transmission energy used by the source and relay, by choosing the transmission power of each node dynamically slot by slot. We assume that both receivers, namely the relay and the destination, can accumulate mutual information across previous and present slots by using earlier received packets for joint decoding. Assuming availability of causal channel state information (CSI) at all transmitters, we obtain the structure of the optimal solutions by dynamic programming. A key insight is that relaying is necessary for the expected sum energy to be bounded in Rayleigh fading channels. Numerical results show that the reduction in the expected sum energy is significant.

Medical implants are devices manufactured to replace damaged biological organs or structures. Once implanted, these devices communicate with other devices outside the body wirelessly. In this paper, we present our work on a particular type of WMI (wireless medical implants): artificial pancreas. We investigate challenges and issues in the context of artificial pancreas designs. In particular, we focus on how to achieve a low power design for WMI and propose a Virtual Patient system to evaluate control algorithms and communication protocols without an actual implantation of the device. Simulation and experiment results presented in this paper serves as design guidelines for wireless medical implant development.

QR decomposition based near maximum likelihood (ML) block detection significantly improves the transmission performance of uplink single-carrier (SC) multi-user multiple-input multiple-output (MU-MIMO). Hybrid automatic repeat request (HARQ) is an indispensable error control technique for high quality packet data transmission. The achievable diversity gain of HARQ depends on the packet combining strategy. In uplink MU-MIMO HARQ, received signal may consist of new packets and retransmitted packets as retransmission for each user acts independently. In this paper, a recursive QR packet combining scheme suitable for uplink SC MU-MIMO HARQ is proposed, which takes into account the number of retransmissions for each user in the detection order. The proposed scheme helps reduce the computational complexity and storage requirement significantly. Moreover, it improves the packet error rate (PER) performance significantly over the conventional bit-level log likelihood ratio (LLR) packet combining, as shown by our computer simulation results.

In this paper, dynamic traffic sharing and base station (BS) muting are incorporated into a clustered cellular system to reduce the total power consumption. Neighboring sectorized BSs form a cluster. Several BSs within the cluster can be muted as long as quality-of-service (QoS) requirement is met. Since the muted BS does not join the transmission, all the users within the cluster need to be served by the active BSs. A power consumption model is also established in this paper which is modeled as a function of traffic load, and incorporates the fixed, variable power consumptions in the system, as well as the power amplifier efficiency. Based on this model, the power saving gain under the effect of the additional traffic load due to BS muting will be discussed. The computer simulation results show that about 30% total power saving is achieved by dynamic traffic sharing and BS muting with clustered BS system.

The key of the multimedia compression technique is trying to remove the correlation between the neighboring frames. Due to the computational complexity limitation, considerable correlation is still observed in the multimedia-coded sequence. This residual redundancy can be modeled as a first-order Markov model with the transition probability of the multimedia-coded parameter between adjacent frames. Therefore, in the multimedia transmission over wireless network, there have been researches on the iterative joint source-channel decoding (ISCD), using the transition probability as source side information (SSI) to reduce errors due to the noisy channel. To further correct the errors after the ISCD, we propose a low-complexity yet efficient bit-flipping (BF) algorithm where restrictions are imposed to prevent incurring high undetected error frames. For the ISCD-BF, the source transition probability is crucial to its performance. However, in practice, it is unknown to the receiver. Thus, we propose an SSI estimation to update the transition probability from the output of the ISCD-BF. The performances of the proposed algorithms are examined by applying to a speech transmission over a wireless network. The simulation results show that our proposed algorithms achieve notable gain over the conventional decoder.

In this paper, we propose to minimize the downlink transmission power through inter-sector cooperative relaying. One common resource block (RB) is shared by two users near the boundary between two neighbouring sectors, and a half-duplex shared relay node (SRN) is introduced along the sector boundary to assist the transmission. The SRN and the two basestation (BS) sectors cooperate to transmit information to the two users. The time sharing factor and frequency sharing factor are jointly optimized to minimize the total network power while satisfying the transmission rate constraint. We also consider rate splitting technique to optimize the cooperation between SRN and BS in order to further reduce the power consumption. Joint optimization is then performed on the message splitting, time and frequency sharing factors. Numerical results show that our proposed scheme can save total network power as high as 10 dB over the system without SRN.

We consider a time-slotted source-relay-destination network where data is to be delivered by a deadline. Our goal is to minimize the sum transmission energy by power allocation for each node and time slot, with knowledge of full channel state information (CSI) in the form of the SNRs of all slots before the deadline. We assume that both receivers, namely the relay and the destination, can accumulate mutual information across previous and present slots by using earlier received packets for joint decoding. We obtain the structure of the optimal solution in a semi-analytical closed-form. The structural result shows that the source and relay jointly perform a generalized form of water-filling over slots. Finally, we use the structural result to develop a heuristic scheme that uses only causal CSI, in the form of the SNRs of only past and present slots.

We consider an energy efficient resource allocation method for orthogonal frequency division multiple access (OFDMA) systems consisting of M users and N subchannels. Transmit power assignment follows the optimal strategy, i.e., a water-filling strategy, for the given subchannels. For the subchannel allocation, we introduce a recently proposed algorithm called a sequential linear assignment algorithm (SLAA). The SLAA determines how many subchannels are allocated to each user and which subchannels are allocated to each user by solving the outer and inner problems, respectively, in an iterative manner; as a result, it requires O(MN2(N-M)2) complexity. To reduce the computational complexity of SLAA, we propose a simplified sequential linear assignment algorithm (SSLAA). Based on the observation that a user requiring the higher power would take the more subchannels, SSLAA uses each user's power consumption as a metric to determine who will take an additional subchannel in each iteration. Consequently, contrast to SLAA using network power consumption as a metric, the SSLAA incurs significant reduction of computational complexity, by O(N2(N-M)2). Computer simulations in compared with other existing heuristic algorithms verify that the proposed SSLAA results in high energy efficiency and low computational complexity.

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.

As Gaussian minimum shift keying (GMSK) modulation scheme is adopted for wireless body area network (WBAN) applications, simple and noise-robust synchronisation algorithms for GMSK are desired to meet the limited resources of power, storage and size in WBAN. However, the current synchronisation algorithms for GMSK are either too complicated or have unsatisfactory performance for low signal-to-noise ratio (SNR), especially for coded GMSK systems. The authors will propose the synchronisation algorithms for general GMSK systems with three unknown synchronisation parameters, namely, timing, frequency and phase offsets estimation. By incorporating noise/interference-reduction methods, the authors propose low-complexity synchronisation algorithms for GMSK that can achieve satisfactory performance in low SNR region. Furthermore, their new feedforward timing and frequency recovery algorithms can provide much better performance than those in the literature by using reliability-measuring mechanism for timing estimation and two-phase frequency estimation. The data-aided Cramer–Rao bounds (CRBs) for the joint estimation of GMSK synchronisation parameters are also derived. The numerical results show that the performance of their proposed synchronisation algorithms is close to the CRBs. The overall bit error rate performance for both uncoded and Bose-Chaudhuri-Hocquenhem BCH-coded GMSK systems are examined. The performance loss owing to synchronisation errors can be reduced to about 1 dB with their proposed algorithms.

We introduce a generalized two-way relay channel where two sources exchange information (not necessarily of the same rate) with help from a relay, and each source additionally sends private information to the relay. We consider the Gaussian setting where all point-to-point links are Gaussian channels. For this channel, we consider a two-phase protocol consisting of a multiple access channel (MAC) phase and a broadcast channel (BC) phase. We propose a general decode-and-forward (DF) scheme where the MAC phase is related to computation over MAC, while the BC phase is related to BC with receiver side information. In the MAC phase, we time share a capacity-achieving code for the MAC and a superposition code with a lattice code as its component code. We show that the proposed DF scheme is near optimal for any channel conditions, in that it achieves rates within half bit of the capacity region of the two-phase protocol.

Power minimization of a cooperative decode-and-forward (DF) half-duplex relaying, where two users communicate with one destination node (D), is studied in this paper. Recognizing the difference in link qualities, one user not only transmits its own message to D but also acts as a relay node (R) for the other user. To achieve total network power minimization, we consider two strategies: (i) a rate splitting strategy (RSS) which splits the whole message into two sub-messages with one being transmitted via R and the other directly to D, (ii) a transmission power boosting strategy (TPBS) in the broadcast phase that allocates more power than needed to ensure correct decoding of the message at R. We first consider joint optimization of rate splitting, a time sharing factor (between the broadcast phase and cooperation phase), and transmission powers. We then consider the second scenario where the time sharing factor is fixed. We prove that TPBS is not required for the first scenario, but is necessary for the second scenario to minimize the total network power. We also obtain channel conditions for which the optimal solution is to employ a multiple access channel (MAC) strategy, i.e., without relaying. Finally we show through numerical results that significant power saving is achieved by our proposed strategies.

In this paper, we consider the half-duplex cooperative multiple-access channel (CMAC) with frequency-selective block-fading. Each link employs an orthogonal frequency division multiplexing (OFDM) system, where modulated symbols are drawn from a finite constellation set. We first obtain the diversity order of the CMAC, as a function of the time sharing variables of the users and the rates of the codes. To achieve this rate-diversity tradeoff, we use the principle of network coding where messages of the two sources are jointly encoded. Both separate and joint network-channel coding approaches are considered. Specifically, we design multiple turbo codes that minimize the outage probabilities of these approaches. We also give a code structure for the multiple turbo codes to achieve full diversity of the system. The codes are optimized using the extrinsic information transfer (EXIT) chart analysis with iterative decoding tailored for OFDM modulated CMAC. Numerical examples show that with our proposed design technique, the achieved frame error rate is within 0.5dB from the information outage. Without network-channel coding, the outage probability of distributed coding cannot achieve the diversity order given in the rate-diversity tradeoff.

ABSTRACT QR decomposition‐based near maximum likelihood block detection significantly improves the transmission performance of uplink single‐carrier (SC) multi‐user multiple‐input multiple‐output (MU‐MIMO). Hybrid automatic repeat request (HARQ) is an indispensable error control technique for high‐quality packet data transmission. The achievable diversity gain of HARQ depends on the packet combining strategy. In uplink MU‐MIMO HARQ, the received signal may consist of new packets and retransmitted packets as retransmission for each user acts independently. In this paper, a recursive QR packet combining scheme suitable for uplink SC MU‐MIMO HARQ is proposed, which takes into account the number of retransmissions for each user in the detection order. The computational complexity and required storage size can be significantly reduced by the proposed scheme. Moreover, it improves the packet error rate performance and throughput performance significantly over the conventional bit‐level log likelihood ratio packet combining, as shown by our computer simulation results. Copyright © 2012 John Wiley & Sons, Ltd.

General signal model for orthogonal-frequency-division multiple access cellular networks with multiple amplify-and-forward relays has been introduced. An optimization problem minimizing network power is formulated to allocate subchannels and to design relay processing, and it is simplified to achieve a suboptimal solution. Numerical results show that the proposed resource allocation method achieves network power reduction and performance improvement.

The encoding and decoding of network coding at network layer and channel coding at the physical layer can be performed either jointly or separately. The largest achievable rate region is obtained when both are done jointly. Our interest in this paper lies in investigating the optimality of separate encoding and joint decoding of network-channel (NC) code (SJNC) assuming an orthogonal access scheme. Our key result is that SJNC is optimal as it achieves the capacity region of JJNC. However, SJNC is suboptimal if time sharing of codes is not allowed, or if separate decoding is used. We then apply these results to the cooperative multiple-access channel (CMAC) where time-sharing of codes is practically infeasible. Optimality of SJNC is compared to JJNC in terms of outage region. The conditions for full diversity order of orthogonal frequency division multiplexing (OFDM) modulated CMAC with JJNC and JSNC are then derived. Using numerical examples, multiple turbo codes are designed to show that the achieved frame error rate (FER) is within 0.6dB from the information outage.

Network power minimization problem is considered for orthogonal frequency-division multiple access (OFDMA) relay systems. To devise a simple algorithm, we introduce a new cost that is an inverse-of-required-minimum power for a target rate. Using the new cost, we propose a greedy-search-based subchannel resource allocation algorithm with a communication mode selection and relay processing adaptation. The communication mode selection allows each user to employ multiple (hybrid) modes with direct, amplify-and-forward (AF), and decode-and-forward (DF) communications, and the relay processing adaptation controls relay transmit power, so that network power consumption can be significantly reduced as verified in simulation.

This paper addresses the problem of timing synchronization in an IEEE 802.11n-based multiple-input single-output (MISO) communications system. In IEEE 802.11n systems, cyclic delay diversity (CDD) is implemented to avoid the problems associated with the unintentional beamforming effect which occurs when scalar multiples of one signal combine destructively after being transmitted through different antennas. However, the use of CDD results in the creation of pseudo-multipaths which will degrade the performance of correlation-based timing synchronization algorithms commonly used in such systems, due to the increased delay spread of the channel. Thus, a frequency domain timing synchronization algorithm, which is robust to the increase in delay spread due to the presence of pseudo-multipaths, is proposed. The proposed algorithm achieves a higher probability of timing synchronization than correlation-based timing synchronization algorithms typically implemented.

Power minimization of a cooperative decode-and-forward (DF) half-duplex relaying is considered in this paper. In the system considered, two source nodes (S) try to communicate with one destination (D). Recognizing the difference in link qualities, one of source nodes not only transmits its own information to D but also acts as a relay for the other S. An optimization problem is formulated to minimize total network power by adjusting transmission powers and a time sharing variable. Since the objective function is not convex, an optimal solution cannot be obtained with low complexity. Instead, we approximate the objective function by a convex function to obtain a suboptimum time sharing factor among transmission phases. Then, transmission power is calculated based on the obtained suboptimum time sharing factor. Power saving of the system considered is compared to multiple access channel (MAC) with optimum power allocation.

In this paper, we consider a two-phase cooperative relay transmission setup with relay's private information (CRT-RPI). A half-duplex decode-and-forward (DF) relay not only helps the source-to-destination transmission, but also transmits its own private information mRto the destination. Distributed space-time coding is employed across the source and the relay to obtain cooperative diversity gain for the source message, and superposition coding between the source and relay messages is used at the relay to transmit mR. We derive the achievable rate region of CRT-RPI and design i) the optimal time sharing factor between two phases and ii) the power allocation factors for the two messages, based on the rate region. Numerical results are also provided to verify the effectiveness of our proposed optimization schemes.

For multiple half-duplex amplify -and-forward (AF) relays in cellular communications, a statistical power allocation (control) method is designed to minimize network power under the relay-transmit-power and the signal-to-interference-plus-noise ratio constraints. Computer simulation corroborates that the designed relay processing yields significant performance enhancement and network power reduction compared to a system with plain AF relays and a direct communication system without relays.

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.

We propose a symbol-by-symbol (SBS) detector for Gaussian minimum shift keying (GMSK) signals in body area networks (BAN). Our detector exploits the orthogonality between adjacent GMSK-modulated symbols. By dividing the received signals into inphase-/quadrature-channels and odd-/even-streams, the strongest inter-symbol interference (ISI) from the adjacent symbols is removed. A whitened matched filter is designed, and a decision-feedback scheme is proposed to further reduce the residue ISI. This SBS detector has much lower complexity than the conventional maximum likelihood sequence detection schemes using the Viterbi algorithm. Its performance can approach the MSK/BPSK performance which results in high energy efficiency. Thus, our proposed energy-efficient, low-complexity, SBS GMSK demodulator is suitable for BAN which have very limited resources. Based on our demodulator's structure, several other SBS detectors can be obtained straightforwardly.

We propose an incremental relaying scheme using joint Tx/Rx frequency-domain equalization (FDE) for single-carrier (SC) transmission. If a packet sent by a source node (S) has been correctly decoded at a relay node (R), but not at the destination node (D), retransmission is cooperatively done by S and R. Assuming that the channel state information (CSI) is shared by S, R, and D, joint Tx/Rx FDE is performed. We derive a set of optimal/suboptimal Tx/Rx FDE weights among S, R, and D, based on the minimum mean square error (MMSE) criterion under total transmit power constraint of S and R. Computer simulation verifies the effectiveness of the proposed scheme.

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.

Initial ranging (IR) involves detecting the binary phase shift keying (BPSK) code transmitted by the new subscriber station (SS). In this paper, we formulate a simple detector using the Neyman-Pearson theorem (NPT) for single-code detection in IEEE 802.16-compliant time division duplex (TDD) orthogonal frequency division multiple access (OFDMA) systems. Exploiting the BPSK structure in the ranging code together with the time-invariant channel, a novel method for separating multiple ranging codes is presented. The NPT-based detector is then used to detect the separated codes. The proposed detector is computationally simple and has favorable detection performance for practical implementation.

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.

Cooperative communications have received considerable attention for wireless multimedia applications as a technique to improve reliability and service coverage. To apply such cooperation over slow fading channels, we consider exploiting the spatial diversity in multiple relay networks. In this paper, we focus on amplify-and-forward relaying schemes, namely, orthogonal amplify-and-forward, selective relaying, and distributed beamforming, for the cooperative wireless multimedia transmission. Furthermore, the successive refinement source coding is exploited such that the multimedia signal is encoded into multiple layers and the quality of its reconstruction at the receiver is improved when more layers are received correctly. We study two strategies for the layered source transmission, namely, progressive transmission and superposition coding. With our developed framework, we propose suboptimal resource allocation algorithms to efficiently assign rate, power, and channel uses to different layers so as to maximize the quality of the multimedia signal reconstructed at the receiver. The proposed optimization methodology is simple and only requires the knowledge of the channel statistics compared to the existing algorithms. As a result, the receiver only requires to feedback the determined rate, power, and channel uses to the transmitter whenever the channel statistics or the layered source transmission strategy changes.

Interference is a performance limiting factor in dense cellular networks with aggressive frequency reuse. Cooperation among base stations (BSs) is a promising approach for improving data rates by eliminating or mitigating interference. For the downlink, the highest spectral efficiency gains are achieved through precoding with full coordination, which requires complete channel state information (CSI) and data be shared among BSs at the cost of significant utilization of the backhaul. In this paper, we propose distributed precoding techniques for the multicell MIMO downlink. Unlike prior work, our proposed precoders are both decentralized with respect to the BSs as well as capable of enabling multiple users to share the same frequency carrier spatially within each cell. Specifically, each BS designs its own precoder without requiring data or downlink CSI of links from other BSs. Since CSI is unlikely to be perfect, we study the effect of imperfect CSI on our proposed precoders and propose a robust precoder in the presence of CSI uncertainty. Simulations show that our proposed methods enjoy a rate increase with SNR similar to multicell joint dirty paper coding in the high SNR regime due to effective interference mitigation. Numerical results reflect the sensitivity of each proposed precoder with respect to the imperfectness in the available CSI.

Cooperative communication using decode and forward (DF)-based distributed relays is one important solution to achieve better connectivity and higher data rates in wireless fading channels. In distributed relay networks, each relay has an independent local oscillator which when unsynchronized results in the presence of multiple carrier frequency offsets (CFOs) at the destination. The maximum likelihood estimator (MLE) for estimating the multiple CFOs requires a two dimensional grid search involving matrix inversion for each search point. Thus, the multiple CFO and channel estimation is computationally prohibitive and challenging in distributed relay networks. In this paper, we propose a simple estimator for multiple CFO and channel estimation at the destination by exploiting the fact that the relays receive information from a common source. Computer simulations show that the mean square error (MSE) performance of the proposed estimator is close to the Cramer-Rao lower bound for small carrier frequency synchronization errors at the relays.

Network MIMO, in which base stations cooperate and coordinate through a centralized design, is an attractive technology that promises higher spectral efficiency and lower inter-cell interference. However, such performance gain comes at the cost of extensive exchange of signal information (SI), channel state information (CSI), and coordinated precoding information over backhaul links. In this paper, we propose a distributed precoding strategy for network MIMO which does not require explicit exchange of SI and CSI among the cooperating base stations. Instead, each base station derives its own precoder to simultaneously minimize the out-of-cell interference as well as maximize the intra-cell sum rate. A robust design is also proposed, to address CSI imperfectness, and which recovers some of the rate reduction due to the CSI uncertainty.

In two-way relaying, two sources exchange information via a relay. In the generalized two-way relay, private information is also sent by each source to the relay, which may be used for overhead data, such as for channel state information. We consider the generalized two-way relay in a multi-carrier system over the wireless channel. Our problem is to maximize the rate that the sources exchange information, subject to some minimum rates of private information. We employ a coding scheme based on time-sharing and superposition of lattice codes and multiple access codes for each subcarrier, and show that the problem of optimizing the time-sharing variables is solved as a linear program. To simplify implementation, we propose a heuristic scheme where the time-sharing variables arequasi-extremal, i.e., all time-sharing variables, except for at most one, are assigned the largest or smallest possible values. Interestingly, the proposed scheme is provably optimal under certain channel scenarios. Numerical results show that the proposed scheme performs close to the optimal scheme in most practical channel scenarios.

In this paper, we employ a relay for scheduling with deadline, i.e., a relay assists to deliver a given number of bits from a source to a destination over a fixed number of slots. Prior to every transmission, the channel of the present slot is made known to all nodes, but not of future slots. We seek to minimize the sum energy used, by choosing the transmission power and transmission duration of each node dynamically slot by slot. Assuming a decode-and-forward scheme for every slot, we obtain the optimal policy via dynamic programming. We also propose and analyze an asymptotically optimal policy that can be solved as a series of convex optimization problems. Numerical results show that the proposed policy gives a tight upper bound on the minimum sum energy, and that a significant fraction of energy can be saved with relaying.

In this paper, we consider the half-duplex relay channel. We seek to design multiple turbo codes to minimize the information outage in the block fading channel. An analysis on the diversity order of the relay channel, which depends on the time sharing variable and the rate of the code, is given for practical modulations. We also give a code structure for the multiple turbo codes to achieve the full diversity when it is achievable. The codes are optimized using the extrinsic information transfer (EXIT) chart analysis, based on the convergence thresholds of the iterative decoding tailored for relay channel. Numerical examples shows that with our design technique, the achieved frame error rate is within 0.7dB of the information outage. To reduce complexity, a suboptimum code search approach is proposed, resulting codes which perform 1dB away from the information outage.

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.

This paper addresses the issue of encoding of LDPC codes for certain transmission standards that employ LDPC as coding scheme. It is shown that for these LDPC codes, the encoding can be done simply by reusing decoding resources with negligible overhead. Compared to conventional encoding designs, our approach requires much less resources specified for encoder.

In this paper, we propose a double space-time transmit diversity (DSTTD)-based two-path relay system with power allocation and link combining methods. The two-path relaying protocol can restore spectral efficiency decreased due to the half-duplexing at the relay nodes and the DSTTD method can appropriately achieve the diversity gain from the two paths. To circumvent severe performance degradation caused by error propagation through the relays, power allocation is considered by maximizing an achievable rate of the system. For the performance enhancement with low complexity, two estimates from the direct-and relay-links are combined at the destination node with combining weights derived from the effective receive signal-to-noise ratios (SNRs) of the links. The performance evaluation shows that the proposed system can provide significant performance enhancement compared to the conventional point-to-point and half-duplex relay communications in any SNR region.

Recently, there has been great interest in the multicast service where a common content such as video streaming and TV program is intended for multiple users. However, while receiving the common message, the users may also wish to receive their personal messages. Therefore, in this paper, we consider a multiple-antenna base station communicating with two single-antenna users and there are a common message to be received by both as well as private messages for each user. Then, we propose to transmit the superposition of the common stream and the private stream intended to only the user with the largest channel gain where each stream is beamformed. An algorithm to determine the optimal beamforming vectors such that the system sum rate is maximized subject to power constraint is presented. Furthermore, the associated achievable rate region is also employed to gain more insight into the achievable sum rate. Our simulation results show that the proposed scheme achieves higher sum rate compared to the conventional schemes especially in the unequal channel gain case.

Opportunistic scheduling for wireless network coding (OWNC) combines the strategies of opportunistic scheduling and network coding. This paper applies two variants of OWNC on a two-user OFDMA system. Symbol-based OWNC decides a strategy from sum rate of the whole OFDM symbol. Subcarrier-based OWNC decides a strategy from the capacity on a subcarrier level. Novel algorithms are derived for power allocation on an OFDM symbol. They appear in the form of waterfilling algorithms but are specifically tailored to the OWNC. Simulation results show average capacity and outage capacity obtained by different approaches of the OWNC. It is shown that, for OWNC with two-user OFDMA, the proposed waterfilling power allocations have modest gains over uniform power allocation at low SNR.

A low complexity M-algorithm based multiple-input multiple-output (MIMO) tree search algorithm with near maximum likelihood (ML) performance is proposed in this paper. Numerical examples show that our tree search algorithm is able to provide a significant performance gain over the MMSE detection. Based on this algorithm, a fully pipelined architecture is presented for the MIMO orthogonal frequency division multiplexing (OFDM) systems. The throughput for a 4x4 MIMO-OFDM IEEE 802.11n system with 64-QAM is 312 Mbps.

Coordinated multi-point transmission/reception (CoMP), in which base stations (BSs) cooperate during the downlink, has been identified as a tool for improving user rates and mitigating interference. The cost for existing coordination schemes with joint processing is the heavy exchange of channel state information and signal information over backhaul links. In this paper, we propose a decentralized BS processing method for the downlink of CoMP systems. Its key feature is that each BS performs decentralized processing without requiring any explicit information exchange between BSs. Unlike existing work, each BS maximizes the rate for multiple intra-cell users and cancels inter-cell interference in a decentralized manner.

Cooperative system has been receiving considerable attention recently as an effective approach to improve transmission reliability in slow fading wireless channels. In this paper, we exploit cooperative diversity in multiple relay networks to improve the performance of Gaussian source transmission with layered broadcast coding. Specifically, we study the distortion performance of two amplify-and-forward (AF) schemes, namely, distributed beamforming and selection AF. First, we analyze their asymptotic performance in terms of the distortion exponent and the expected distortion. We then show that, under stringent delay constraint, there exist SNR thresholds below which the AF systems achieve lower distortion than the direct transmission system.

We derive in this paper the bit error probability and cutoff rate of an Amplify-and-Forward (AF) relay system operating in Rayleigh fast fading. In our model, we consider the relay thermal noise to be non-negligible and derive a closed form expression to approximate the bit error probability. Our approximated closed form expression matches well with simulation result. Using Chernoff bound, we upper bound its pairwise error probability and we also derive the cutoff rate of the AF relay system.

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.

Abstract Networking cognitive radios and nodes from primary system (PS) results in a heterogeneous coexisting multi‐radio wireless network, so that significant network throughput gain can be achieved. However, by investigating cognitive radio network (CRN) architecture, the links in CRNs are unlikely to support complete security check due to link dynamics, opportunistic availability, and uni‐directional in available time window. We therefore introduce trusted cognitive radio networking (TCRN) concept to facilitate network functions such as association in dynamic spectrum access and routing. First of all, we explore the mathematical framework for trust in CRNs. We then show successful association of node to CRN based on the mathematical structure of trust from statistical decision theory. Furthermore, we modify the machine‐learning algorithm to update the trust measure for each node, and develop rules of thumbs to facilitate TCRN with learning capability, based on numerical simulations. Trusted CRN can greatly alleviate heterogeneous challenge for CRN operation. Copyright © 2009 John Wiley & Sons, Ltd.

Average and outage capacities for nonidentical two-user opportunistic scheduling for wireless network coding (OWNC) are derived in this paper. Nonidentical user case represents a more realistic scenario in which two users have different average signal-to-noise ratio (SNR). Our derived analytical results corroborate with the simulation results in the performance evaluation of OWNC. Insight on the performance of OWNC for the nonidentical user case is provided.

Cooperative relaying is an effective approach to combat wireless fading. However, the reliability enhancements depend strongly on the accuracy of the carrier frequency offset (CFO) compensation and channel estimation algorithms. In this paper, we show that CFO compensation at relay is necessary in non-regenerative OFDM based wireless relay networks with relays employing space-time coding. A simple training scheme available for estimating space-time channel is exploited to estimate the CFO at the relay. Maximum likelihood (ML) and least squares (LS) based joint CFO and channel estimators are constructed for estimating the CFO and the product channel (effective channel from source to destination) at destination. For non-regenerative OFDM based wireless relays, we prove that the LS based estimator is equivalent to the ML based estimator. Theoretical mean square error for the product channel estimator is also derived.

In the conventional two-way relay channel, two sources exchange information with help from a relay. We introduce a generalized two-way relay channel where each source additionally sends private information to the relay. For this channel, we consider a protocol that consists of a multiple- access (MA) phase and a broadcast (BC) phase and obtain achievable rate regions for both phases. The MA phase is related to computation over the multiple-access channel (MAC). We show that our scheme achieves rates that always lie within half bit of the capacity region for computation over MAC, and is capacity achieving in the BC phase. This near optimality in the MA phase can be achieved by time sharing a capacity-achieving code for the MAC and a superposition code that uses a lattice code as its component code.

This paper provides an analytical formulation of average capacity and outage capacity for two-user opportunistic scheduling for wireless network coding (OWNC). It is shown that our derived capacity formulas can be easily evaluated without numerical integration and match exactly with the simulation results. At high SNR, it is shown that the average capacity of OWNC is double of logarithm (base 2) of average SNR subtracted by a loss term composed of the ‘scheduling loss’ and ‘concavity loss’ factors.

The problem of multi-user cooperation to achieve cooperative diversity for single-antenna terminals is considered. A cooperative strategy is proposed which divides multiple users into groups with each group having two users as partners. The two users alternately transmit their own information first, then simultaneously amplify and forward the partner's data in the same channel. Comparing to the conventional multi-user cooperation where the two users relay the partner's data in two orthogonal channels, the spectral efficiency of the proposed scheme is increased. However, as the two users relay the partner's data in the same channel, multi-user detection problem arises. An iterative detection making use of the new scheme's property is proposed in the presence of frequency offsets. The proposed scheme only requires time and frequency synchronization but no channel state information at the user terminals. To estimate the channel at the destination, a training scheme is proposed. The simulation results show that the proposed scheme can achieve the same cooperative diversity as the conventional scheme, while the spectral efficiency is lifted by 4/3.

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 multi-cell OFDMA systems, inter-cell interference can severely degrade the system throughput, particularly for cell-edge users. To mitigate the inter-cell interference problem, interference coordination is a promising approach that effectively restricts and allocates certain resources among users in different cells to minimize the effect of inter-cell interference. However, most approaches are studied for static user distribution/traffic load and usually require a centralizer controller to solve a complicated optimization problem. In this paper, we propose an adaptive and distributed interference coordination algorithm that can achieve an efficient frequency reuse for a given user distribution and traffic load. The proposed algorithm only requires minimal coordination between base stations and does not need any a priori frequency planning. To enable reduction in system optimization, we classify the users into two groups, namely cell-interior and cell-edge user groups. With such user group differentiation, we can efficiently decompose a multi-cell optimization problem into distributed optimization problems, which is composed of solving single-cell resource allocation problem.

In a previous work by Wu et ah, it is shown that the performance of the pre-transformed space-time block coded orthogonal frequency division multiplexing (PT-STBC-OFDM) system has much superior performance compared to the normal STBC-OFDM system. In this paper, we present an analytical study on the bit error rate (BER) of the PT-STBC-OFDM system. We derive the noise distribution of PT-STBC-OFDM system and hence, obtain the BER numerically. We also derived two closed- form BER approximations for the PT-STBC-OFDM system at two different SNR regions. We show that by adding the PT operation, the system has better diversity compared to STBC-OFDM system at the medium SNR region. Using linear detection, the diversity of both systems at the high SNR regions are the same. Moreover, we demonstrate that at high SNR, a PT-STBC- OFDM system of transform size N requires 5log10(N) dB less SNR to achieve the same BER as without PT.

We study Bluetooth (BT) interference mitigation in IEEE 802.11g systems. A robust modified minimum mean squared error (MMSE) algorithm is derived for channel estimation. For interference estimation and cancelation, we consider two cases: the interference non-coincident and coincident with the 802.11g subcarriers. For the former case, non-linear least square (NLS) interference estimation and time-domain cancelation is proposed. The unique 11g long preamble structure is exploited to further improve the NLS estimation efficiency. For the latter case when the interference is coincident with 802.11g subcarriers, we propose to use NLS and polynomial smoothing for interference frequency estimation, and interpolation for channel estimation at the subjected subcarriers. The frequency domain interference signal is then obtained by subtracting the re-constructed 802.11g preamble from the received signal. Thereafter, mitigation at the data transmission stage is also performed in frequency domain. The performance of the proposed algorithms is evaluated in terms of channel estimation mean squared error (MSE) and the bit error rate (BER) by simulations for a range of interference powers.

This paper presents a novel cyclic-prefix-free preamble structure to enable efficient transmission of training sequences for channel estimation in Multiple-Input-Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems. The joint space, time and frequency domain design requirements are derived and two solutions are formulated. Simulation results confirm that the proposed preamble structure provides overhead reduction without compromising channel estimation performance.

In traditional MIMO-OFDM beamforming system with limited feedback, the channel state information (CSI) feedback from the receiver contains only the selected beamforming vectors information. In this paper, we propose to include the SNR strength knowledge of some subcarriers in the feedback and implement adaptive channel selection (ACS) at the transmitter for JPEG2000 image transmission. As a result, a significant enhancement and robustness can be achieved. We also propose nonuniform-based beamforming algorithms to further reduce the number of required feedback bits. Although the simulation results show that this leads to worse bit error rate (BER) performance, the received image quality measured in average peak signal-to-noise ratio (PSNR), however, is comparable with the traditional optimally quantized beamforming which requires much more feedback bits. Moreover, with the proposed algorithms, the possibility of successful image reconstruction at the receiver is much higher than the existing algorithms even at low SNRs.

We propose an improved beamforming matrix compression by givens rotation with the use of variable feedback rate. The variable feedback rate means that the number of bits used to represent the quantized beamforming matrix is based on the value of the matrix. Compared with the fixed feedback rate scheme, the proposed method has better performance without additional feedback bandwidth.

The problem of carrier and clock synchronization without a training sequence for a robust receiver working at low signal-to-noise (SNR) ratios is considered. Both short packet and long burst transmission scenarios are taken into account. A low-complexity iterative symbol timing and carrier synchronization algorithm making use of the extrinsic information from the soft- input soft-output (SISO) decoder is proposed. The non-data-aided (NDA) algorithms are used for initial synchronization. In order to solve the phase ambiguity problem inherent in the NDA phase synchronization algorithm, two SISO decoders denoted by I and Q decoders, respectively, along with a differential decoder are proposed. The simulation results show that the proposed iterative synchronizer and decoder can reach the ideal performance at very low SNR with an increased allowed residual frequency offset and [-pi,pi] phase uncertainty region.

Previously proposed technique called joint cooperative diversity and scheduling (JCDS) has been shown to increase aggregate throughput of multiuser OFDMA relay networks significantly while being able to maintain per-link throughput when the number of relays is high enough. This paper proposes a new technique so-called joint cooperative diversity and proportional fair scheduling (JCPFS) for the multiuser OFDMA relay networks which sacrifices a small amount of the aggregate throughput in return for significant improvement in the per-link throughput. Unlike JCDS, JCPFS provides increases in both aggregate and per-link throughputs as the number of users increases. The throughput performance of JCPFS is characterized by delay spread of the channels and its effect is shown by simulation.

We consider the problem of carrier frequency and phase synchronization at low SNR without a training sequence for short packet transmission. A low-complexity iterative joint symbol timing and carrier synchronization algorithm making use of the extrinsic information from the soft-input soft-output (SISO) decoder is proposed. The non-data-aided (NDA) algorithms are used for initial synchronization. The simulation results show that the frequency offset estimation is the most critical problem in low SNR region. The proposed low-complexity algorithm can reach the ideal performance with perfect synchronization in the absence of frequency offset at very low SNR. When the frequency offset exists, the proposed algorithm can work effectively and improve the performance significantly.

In this paper, we study the problem of link adaptation based on adaptive modulation and coding for multiple antenna OFDM system in slow fading channel. Based on the extrinsic information transfer analysis, we give an accurate packet error rate prediction with channel estimation errors. This method uses Gaussian approximation to characterize the output of the detector and decoder. We also discuss approaches for searching and selecting the best modulation and coding scheme for the link adaptation algorithm. Finally, the performance of the proposed link adaptation algorithm is studied for the IEEE 802.11n multiple-antenna OFDM system. Good system throughput are achieved using the proposed method, compared to the SNR based algorithm.

We propose an open-loop two-dimensional linear pre-transformed (2DLPT) multiple-input multiple- output (MIMO) orthogonal frequency division multiplexing (OFDM) system. ThenTNtimesnTN2DPLT, withnTthe number of transmit antennas and N the FFT size in the OFDM modulation, is applied in both spatial and frequency domains and is unitary in individual as well as both domains. While the spatial domain transform converts the transmit diversity from the other transmit antennas to frequency diversity, the frequency domain transform fully exploits the frequency domain diversity made available. More over, the unitary transform guarantees the same ergodic capacity as the non-transformed spatial division multiplexing MIMO-OFDM channel. Therefore, the proposed 2DLPT MIMO-OFDM system achieves full diversity ofnTnRnLwithnRandnLrepresenting the number of receive antennas and the number of multipaths in each transmit-receive antenna pair channel, and full capacity.

In this paper, we present a detailed study of the performance of hybrid STBC wireless LAN systems. In particular, we look at the performances of hybrid STBC systems with two different antenna configurations. We analyze the performance and advantages of using hybrid STBC over spatial multiplexing and of using "even" and "uneven" modulation and coding sets.

We compare four open-loop transmit diversity schemes in a coded Orthogonal Frequency Division Multiplexing (OFDM) system with four transmit antennas, namely cyclic delay diversity (CDD), Space-Time Block Code (STBC, Alamouti code is used) with CDD, Quasi-Orthogonal STBC (QO-STBC) and Minimum- Decoding-Complexity QOSTBC (MDC-QOSTBQ. We show that in a coded system with low code rate, a scheme with spatial transmit diversity of second order can achieve similar performance to that with spatial transmit diversity of fourth order due to the additional diversity provided by the phase shift diversity with channel coding. In addition, we also compare the decoding complexity and other features of the above four mentioned schemes, such as the requirement for the training signals, hybrid automatic retransmission request (HARQ), etc. The discussions in this paper can be readily applied to future wireless communication systems, such as mobile systems beyond 3G, IEEE 802.11 wireless LAN, or IEEE 802.16 WiMAX, that employ more than two transmit antennas and OFDM.

We study space-time precoding techniques for asymmetric multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channels with more transmit than receive antennas which is a typical downlink setup. Under the condition of perfect channel state information (CSI) at the receiver but no CSI at the transmitter, we consider linear dispersion coding (LDC), groupwise space-time block coding (GSTBC), quasi-orthogonal space-time block coding (QSTBC), and spatial spreading (SS) and study their capacity and diversity performances. Our results show that for both ergodic and outage capacities, SS is worse than LDC, GSTBC and QSTBC, while the latter three have very close/same performance. We also show that by adjusting the phase shift values in the SS matrices, its outage capacities can be improved. In terms of diversity, LDC, GSTBC and QSTBC have both transmit and receive spatial diversity, and LDC's diversity order is slightly higher than the latter two. SS, on the other hand, has not only receive spatial diversity, but also introduces additional frequency diversity. This contributes to their very close bit error rate (BER) performance when turbo processing is deployed at the receiver. Considering the implementation flexibility and complexity, we therefore recommend SS and GSTBC as the promising precoding schemes for asymmetric MIMO-OFDM channels.

This paper presents the design of a (n,k) Reed-Solomon decoder for both errors and erasures. The key-equation solver is based on Sarwate's reformulated inversionless Berlekamp-Massey algorithm. The decoder has been implemented on FPGA and the maximum clock frequency can be 150 MHz for a (255, 239) code on a Xilinx Virtex-II device.

In this paper, we study the performance of two transmit diversity schemes, the cyclic delay diversity (CDD) and the groupwise space time block code (G-STBC), for high throughput wireless local area network (LAN) system using orthogonal frequency division multiplexing (OFDM) and multiple input multiple output (MIMO) technologies. We compare the performance of CDD and the G-STBC transmission with the VBLAST transmission using both convolutional code (CC) and low density parity check (LDPC) code. We show that G-STBC provides the best performance in all the three transmission schemes. The performance of CDD depends both on the frequency diversity of the channel and the power of coding. CDD has better performance advantage for systems with smaller multi-path and with powerful coding schemes. CDD also has the advantage of being receiver transparent

The interest in near-ML detection algorithms for multiple-input/multiple-output (MIMO) systems have always been high due to their drastic performance gain over suboptimal algorithms. Algorithms such as the M algorithm and the stack algorithm yields near-ML performance while only requiring a fraction of the computational complexity of an ML receiver. While the stack algorithm is computationally less intensive than the M algorithm for uncoded systems and hard decision decoding, the reverse is true for soft decision decoding as a candidate list is required to compute the soft metric. The M algorithm can easily generate a candidate list at no extra cost, the stack algorithm, on the other hand, would require more iterations to generate the list. In this paper, we propose a modified stack algorithm which have a lower computational complexity than a conventional one without sacrificing much of the performance of the algorithm

We propose a pseudo-inverse minimum mean squared error (MMSE) based QR decomposition (QRD) M-algorithm (QRD-M) for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithm can effectively mitigate the detrimental effect of MIMO channel ill-conditions on the bit error rate (BER) performance. As a result, the number of retained branches (M value) can be reduced to half of the original QRD-M algorithm in [1] in order to achieve the same performance for both the uncoded and coded systems. The detection complexity is therefore greatly reduced.

We study space-time precoding techniques for asymmetric multiple-input multiple-output (MIMO) channels with more transmit than receive antennas which is a typical downlink setup. Under the condition that channel state information (CSI) is perfectly known at the receiver, but not known at the transmitter, we consider groupwise space-time block coding (GSTBC), quasi-orthogonal space-time block coding (QSTBC), linear dispersion coding (LDC), and spatial spreading (SS). We show that for narrow-band i.i.d. circularly symmetric complex Gaussian (CSCG) MIMO channels, SS has the lowest ergodic capacity, while GSTBC, QSTBC and LDC have almost the same ergodic capacity. We then compare their input-output mutual information when fixed-order modulation is deployed and show that the same performance order maintains. Finally we look into their bit error rate (BER) performance for uncoded systems, and show that LDC slightly outperforms GSTBC and QSTBC in high SNR regions and below BER of 10-2, and SS has the worst performance. Due to the good flexibility for various asymmetric channels and its simple implementation, we therefore recommend GSTBC as the asymmetric MIMO precoding scheme

This paper studies the problem of signal detection for generic MIMO channels with large signal dimensions. We propose a block-iterative generalized decision feedback equalization (BI-GDFE) receiver to recover the transmitted symbols in a block-iterative manner. By exploiting the input-decision correlation (IDC), a measure for the reliability of the earlier-made decisions, we design the feed-forward equalizers (FFEs) and feedback equalizers (FBEs) in such a way that maximized signal-to-interference-plus-noise ratio (SINR) is achieved for each iteration. We propose an IDC determination method to achieve fast and guaranteed convergence for the proposed receiver. Computer simulations are presented to illustrate the capability of the proposed receiver to achieve single user matched-filter bound for large MIMO channels with high SNR.

In this paper, we present a pre-transformed (PT) Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system, in short PT-MIMO-OFDM system. We propose two receiver structures that combine the iterative subcarrier reconstruction and iterative interference cancellation to fully exploit both the spatial and the frequency diversity of MIMO-OFDM systems. Signi cant performance gain can be achieved. Based on the performance and receiver complexity tradeoff, we also present a set of guidelines for choosing either one of the two receivers in practical systems.

We present a comparative study of two tree search based detection algorithms, namely, the M-algorithm combined with QR decomposition (QRD-M) and the sphere decoding (SD) algorithms, for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. First, we show that nodes ordering before and during the tree search is important for both algorithms. With appropriate ordering, QRDM can improve detection performance significantly and SD can reduce decoding complexity substantially. Then we compare the implementation complexity of the two algorithms, in terms of the number of nodes required to search or the required number of multiplications to achieve maximum likelihood detection performance. It is interesting to show that the average complexity of SD is lower than that of QRD-M, whereas the worst case complexity of SD is much higher than that of QRD-M.

A list MIMO detection scheme is proposed to achieve near optimal maximum likelihood detection performance. The scheme is especially efficient in high data rate packet transmission systems, such as wireless LAN based hotspots, fixed broadband wireless access systems, 4G cellular based hotspots etc. The complexity of the optimal performance achieving scheme is significantly lower than that of the ML detection and the state of art technologies.

An adaptive trellis coded modulation (TCM) and bit-interleaved coded modulation (BICM) scheme is proposed for ordered MIMO-OFDM channels, derived through singular value decomposition (SVD) based eigen-beamforming and sub-channel grouping techniques. By deriving bit-error-rate (BER) upper bound of TCM and BICM for the ordered channels, it is observed that TCM is better than BICM for the grouped channels with less fluctuation, while BICM is better than TCM for severely fluctuated grouped channels. Thus for a given throughput requirement, the proposed scheme adaptively selects the suitable TCM or BICM for each of the grouped channels, based on available power budget and BER constraint. We show that the adaptive TCM/BICM scheme has superior performance over all-TCM and all-BICM schemes, especially when bit loading and power allocation are jointly applied.

A multiple-input multiple-output (MIMO) system with serially concatenated bit-interleaved coded modulation (BICM) and linear dispersion code (LDC) is investigated. LDC is a member of the family of linear space-time block codes STBC. A tight upper bound for this system over a spatially correlated Rician fading channel is derived, based on the moment generation function (MGF) approach. We utilize this bound to study the impact of the propagation environment features, such as the Rician factor, geometry of the Rician component, and transmit and receive antenna correlation, on the performance of a bit-interleaved convolutional-coded LDC MIMO system.

We propose a low complexity VBLAST OFDM detection algorithm. In this method, we partition the subcarriers into a number of groups. The group size is determined by the frequency correlation between different subcarriers. Within each group, we use conventional VBLAST detection on the centre subcarrier and record the detection order. For the rest of the subcarriers in the group, we use a QR decomposition technique according to the recorded detection order. We show that this algorithm significantly reduces the complexity of VBLAST OFDM detection. The performance degradation of the proposed algorithm is small compared to conventional detection.

The upper bound on bit error rate (BER) of a multiple-input multiple-output (MIMO) system with the serial concatenated bit-interleaved coded modulation (BICM) and linear dispersion code (LDC) is derived in this paper. This bound is evaluated based on the expurgated bound of BICM and the approach of moment generation function (MGF). Compared with simulated performance of the 16QAM Alamouti code (AC) and optimal QPSK LDC, this bound is tight in the range of medium to high signal-to-noise ratio (SNR). By utilizing the bound, we obtain the optimal choice of code rate and modulation size for fixed transmission rates in BICM-LDC MIMO system.

We propose a novel iterative receiver for coded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Starting from the derivation of the optimal minimum mean squared error (MMSE) estimate of the transmitted data, we show that for BPSK and QPSK modulated signals, the soft estimate is a hyperbolic tangent function of the extrinsic information from the soft-input soft-output (SISO) decoder and the decision statistic from the detector. Compared with the conventional iterative receiver which computes the data estimate using only the decoder output, the proposed structure has significant performance gain, for example, it is 2.0 dB better for groupwise space time block coded (GSTBC) MIMO OFDM system at BER of 10/sup -6/, and 1.5 dB better for vertical Bell Lab layered space-time (VBLAST) OFDM system at BER of 10/sup -5/.

A performance comparison between convolutional coded vertical Bell-lab layered space time (V-BLAST) and groupwise space time trellis coded (GSTTC) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is presented in this paper. Both systems have the same data rate and employ receivers with comparable complexity, which are zero-forcing, minimum mean square error (MMSE) and list sphere decoder (LSD) based turbo receivers. Simulation results show that V-BLAST significantly outperform the GSTTC systems when MMSE or LSD based turbo receiver is applied, while GSTTC perform similarly to or better than the V-BLAST systems when zero-forcing detection algorithm is applied.

A semiblind method for channel estimation in multiple-input multiple-output (MlMO) orthogonal frequency-division multiplexing (OFDM) systems is proposed. The MIMO-OFDM channel matrix is first estimated blindly up to an ambiguity matrix using the subspace identification algorithm based on the second-order statistics of the channel outputs. The ambiguity matrix is then estimated using a few training symbols. It shows that the number of training symbols required by the proposed semiblind method is significantly less than that used in the training sequence based channel estimation algorithm. The simulation results demonstrate the effectiveness of the proposed algorithm.

Recently, an iterative detection method is proposed for a pre-transformed OFDM system, where significant performance gain is shown to be achieved through linear processing (Z.Lei et al., Apr. 2003). An analytical treatment of the subject is given. The optimum transform design and reconstruction criteria are given to maximize the minimum SNR. In the case when no iteration is applied (i = 0), we compare the effect of the transform on the performance in a channel with and without diversity. Under the assumption that the previous detection is error free, it is shown analytically that the iterative method achieves a diversity advantage of i + 1 in the i/sup th/ iteration, thus providing an explanation on its superior performance. Simulations are conducted to corroborate the analysis. Due to the generality of the transform design, the analysis conducted is applicable for other common systems (such as MC-CDMA and SC-FDE systems) as well.

In this paper, we propose a low complexity iterative interference cancellation and decoding (IICD) scheme for serial concatenated coded and pre-transformed communication systems. In this scheme, soft reliability information of the transmitted bits is exchanged between the maximum a posteriori (MAP) decoder and the detector employing interference cancellation (IC) technique. The complexity of the proposed scheme is only linear with respect to the size of the pre-transform and constellation set. This leads to significant reduction in computation complexity compared to conventional iterative detection and decoding (IDD), where the complexity is exponential with respect to the transform size and constellation size. We show that the proposed scheme can fully exploit the diversity of the fading channel and it reaches the BER bound of IDD at high SNR values after 3 to 4 iterations.

In this paper, an iterative interference cancellation (IIC) scheme is proposed for the ordered zero-forcing (ZF) algorithm to regain the signal energies lost during the interference suppression (IS) process in groupwise space lime trellis coded (GSTTC) systems. Both hard decision and soft decision based IIC are studied. With hard decision based IIC, significant improvement over the ordered ZF algorithm is achieved and a performance comparable to that of the complex ordered maximum SNR (MaxSNR) algorithm is reached. With soft decision IIC, the proposed scheme can even outperform the ordered MaxSNR algorithm and provides a better performance-complexity tradeoff in systems with large number of antennas and simple space time trellis codes.

Robust minimum mean-square error (MMSE) channel estimation for orthogonal frequency-division multiplexing (OFDM) systems with practical timing synchronization is considered. The correlation between the channel coefficients at the different subcarriers depends upon the channel power delay profile (PDP) and the probability density function (pdf) of the timing synchronization offset. Since both of these are not known at design time, there is a need for a robust estimator that results in mean-square error performance independent of the actual channel power delay profile (PDP) and the actual timing synchronization offset pdf. We develop the notion of effective PDP, given by the convolution of the channel PDP and the pdf of the timing synchronization offset. We show that designing with a uniform PDP of the same length as the effective PDP leads to a robust solution. This follows from the robustness of the uniform PDP for MMSK estimation, a known result for which we provide an alternative derivation. We illustrate the performance of the robust estimator using a numerical example.

This paper describes how to perform low complexity frequency offset estimation in the presence of direct current (DC) offset based on a received periodic preamble. By designing a suitable preamble, we perform frequency and DC offsets estimation concurrently, followed by a simple compensator which reduces the frequency offset error induced by the presence of either a residual or actual DC offset. Its performance is illustrated based on the preamble of a wireless LAN system defined in IEEE 802.11a, as well as another preamble design that offers additional performance gain. Compared to the case when no compensator is used, the proposed solution gives superior performance over a wide range of DC offset power in both AWGN and multipath quasi-static channels.

The iterative detector and decoder (IDD) in the serial concatenated coded and pre-transformed system is studied in this paper. The performance of the IDD in the single-carrier system over flat fading channel and the orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channel are thoroughly evaluated. Numerical results show that in the single-carrier system, IDD with 2 to 3 iterations over flat fading channel achieves performance only about 1dB away from that over the additive white Gaussian noise (AWGN) channel. Our study also finds that for OFDM system, the IDD provides considerable performance improvement compared to the coded or pre-coded systems, even when the multipath diversity is low. IN the meanwhile, the excellent performance of IDD does not rely on the optimal design of pre-transformation matrix.

A simplified iterative detector and decoder (IDD) through pre-hard-decision (preHD) is proposed in the serial-concatenated coded and pre-transformed single-carrier system over flat Rayleigh fading channel. For the first iteration, the preHD is done by linear least squares (LS) or minimum mean-squared error (MMSE) detection, and a Chase-like symbol reliability calculation method is designed to select the preHD symbols. For the following iterations, the symbol soft information from SISO decoder is used for preHD. It is shown that the complexity of combined preHD and detection declines exponentially with the number of preHD symbols. Moreover, the performance degradation of simplified IDD is marginal when the pre-transformation size is large and the number of preHD symbols is small.

In this paper we present several results of our study on training sequence assisted channel estimation for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. After developing a linear matrix algebraic model for the cyclic prefix based MIMO OFDM systems, we define a generalised preamble structure which is a simple extension of the preambles used in single-input single-output (SISO) OFDM so that its good properties, such as low peak to average power ratio, can be maintained. We then derive the least squares (LS) and linear minimum mean squared error (LMMSE) channel estimation algorithms based on the proposed preamble design. In order to reduce the preamble length, we further propose a switched subcarrier preambles scheme in which the transmit antennas are divided into groups, and preambles are transmitted in alternative subset of subcarriers in each group. A LMMSE filter-based interpolation scheme and a DFT-based LS interpolation scheme are then used to obtain the channel estimates for all the subcarriers of interest. In all the proposed schemes in this paper, the filter parameters can be fixed and robust performance is obtained even when mismatched SNR and channel statistics are used in the filter parameter calculations.

We investigate the effects of performing channel estimation on an OFDM system in AWGN and Rayleigh channels using non-ideal interpolating and decimating filters. Pilots are used to estimate the combined response of the channel and the filters. By making use of the standard Gaussian approximation to the interference for the AWGN channel, we illustrate how to estimate the symbol error rate of an M-QAM system. By using an improved Gaussian approximation for the Rayleigh fading channel for error rate estimation, we show that this is equivalent to making the assumption that the interference is Gaussian noise experienced in a Rayleigh fading channel. It is shown that the predicted symbol error rate is close to that produced by simulations for a 64QAM modulated OFDM system based on the wireless LAN system described in IEEE 802.11a.

Linear parallel interference cancellation (PIC) schemes are described and analyzed using matrix algebra. It is shown that the linear PIC, whether conventional or weighted, can be seen as a linear matrix filter applied directly to the chip-matched filtered received signal vector. An expression for the exact bit-error rate (BER) is obtained, and conditions on the eigenvalues of the code correlation matrix and the weighting factors to ensure convergence are derived. The close relationship between the linear multistage PIC and the steepest descent method (SDM) for minimizing the mean squared error (MSE) is demonstrated. A modified weighted PIC structure that resembles the SDM is suggested which approaches the minimum MSE (MMSE) detector rather than the decorrelator. It is shown that for a K-user system, only K PIC stages are required for the equivalent matrix filter to be identical to the the MMSE filter. For fewer stages, techniques are devised for optimizing the choice of weights with respect to the MSE. One unique optimal choice of weights is found, which will lead to the minimum achievable MSE at the final stage. Simulation results show that a few stages are sufficient for near-MMSE performance.