Acquiring synthetic aperture radar (SAR) images is inherently difficult and laborious. To mitigate this data scarcity, generative models for SAR images aim to learn underlying data distributions from large-scale datasets, ensuring robust performance. Generative models mitigate data scarcity but rely on centralized frameworks, posing privacy risks and limiting deployment in Internet of Things (IoT) scenarios. To tackle the aforementioned challenges, we introduce an innovative federated angle-aware generative diffusion (FAGD) framework for secure SAR image generation. This framework integrates three key innovations: federated learning (FL), the angle-aware generative diffusion (AAGDiff) model, and the local data knowledge distillation (LDKD) strategy. Specifically, we introduce the FL framework, which enables clients to collaboratively train a generative model by transmitting model weights, thereby eliminating raw data exchange and enhancing security. We then propose the AAGDiff model for client-side high-quality generation, leveraging denoising diffusion probabilistic models (DDPMs) to synthesize high-quality SAR images from noise using an angle-conditioned encoder, enabling the generation of SAR images at different target azimuth angles. Additionally, the LDKD strategy alleviates overfitting during federated training under Non-Independent and Non-Identically Distributed (non-IID) data distributions by allowing the model to distill knowledge primarily from less frequent classes, thus enhancing generalization and robustness. Evaluated extensively on MSTAR and OpenSARShip datasets, the proposed framework ensures secure SAR image generation while achieving superior target recognition performance. Overall, the FAGD framework offers an effective and scalable solution for privacy-preserving SAR image synthesis.

Edge intelligence has emerged as a promising strategy to deliver low-latency and ubiquitous services for mobile devices. Recent advances in fine-tuning mechanisms of foundation models have enabled edge intelligence by integrating low-rank adaptation (LoRA) with federated learning. However, in wireless networks, the device heterogeneity and resource constraints on edge devices pose great threats to the performance of federated fine-tuning. To tackle these issues, we propose to optimize federated fine-tuning in heterogenous wireless networks via online learning. First, the framework of switching-based federated fine-tuning in wireless networks is provided. The edge devices switches to LoRA modules dynamically for federated fine-tuning with base station to jointly mitigate the impact of device heterogeneity and transmission unreliability. Second, a tractable upper bound on the inference risk gap is derived based on theoretical analysis. To improve the generalization capability, we formulate a non-convex mixed-integer programming problem with long-term constraints, and decouple it into model switching, transmit power control, and bandwidth allocation subproblems. An online optimization algorithm is developed to solve the problems with polynomial computational complexity. Finally, the simulation results on the SST-2 and QNLI data sets demonstrate the performance gains in test accuracy and energy efficiency.

Artificial intelligence (AI) integration in communication is crucial for 6G. It optimizes terrestrial communication and computing resource usage in the Internet of Things (IoT) using AI techniques, such as supervised learning for data analysis and reinforcement learning for resource allocation. However, in remote areas, i.e., oceans and deserts, IoT devices lose connection due to limited terrestrial coverage. Low Earth Orbit Satellite (LEOS) offers low-latency, high-bandwidth access in these unconnected regions. However, power and computing limitations on both IoT devices and LEOSs present challenges for continuous service. To this end, we present an LEOS-assisted open radio access network (RAN) Architecture (LO-RAN) where an RAN intelligence controller (RIC) is integrated to provide AI abilities. We formulate a joint Offloading decision, Path selection, and Resource allocation problem (OPR) to minimize the weighted energy consumption and latency of LO-RAN. We proposed a Joint Optimization for the Offloading decision, Path selection, and Resource allocation (JOOPR) algorithm. It selects contact and processing LEOSs for path selection, uses proximal policy optimization (PPO) for offloading decisions, and applies Karush-Kuhn–Tucker (KKT) to solve resource allocation. The outputs from path selection and resource allocation contribute to the reward that feeds into the PPO. We conduct numerical simulations to compare the proposed JOOPR with the state-of-the-art approaches. The results show that JOOPR reduces energy consumption and latency by at most 28.75% and 33.01%, respectively.

The integration of autonomous aerial vehicles (UAVs) and low-Earth orbit (LEO) satellites has become attractive for Internet of Things (IoT) task processing, as it can overcome obstacles in terrestrial network coverage, such as those in oceans or desert areas. However, it lacks a collaborative approach for allocating the communication and computing resources among UAVs and LEO satellites and optimizing the hovering point of UAVs to prolong their endurance. In this article, we investigate energy-efficient resource allocation in LEO-assisted UAV networks for the IoT. A novel optimization algorithm, that jointly IoT tasks’ offloading decision, UAVs’ region selection, hovering point chosen, and communication and computing resource allocation (ORHCC), is proposed to optimize UAV trajectories and hovering points, enhancing endurance and minimizing energy consumption. In particular, the UAVs’ region selection and IoT tasks offloading are under the dueling deep Q-network (DuDQN) framework, the hovering point chosen and communication and computing resource allocation via the convex solution. The results show that the proposed ORHCC reduces 12.5% and 20.76% energy consumption compared with the proximal policy optimization and greedy baseline, respectively.

The evolution of communication architecture shifts towards virtualization and cloud-native network functions, setting the stage for the flexibility and integration of emerging technologies. Artificial Intelligence (AI) and Machine Learning (ML) as intrinsic elements in network design are some of the crucial visions and requirements for 6G. This paper, from the perspective of O-RAN, explores how current network architectures should evolve towards the integration of communication and intelligence in 6G. It begins with a comprehensive analysis and comparison of the AI-related work conducted by various standard organizations. Building on this, an end-to-end AI integration framework is proposed, which leverages AI technologies, data services, and digital twin (DT) technologies to achieve an integrated intelligent 6G communication system. After that, the key enabling technologies for cross-domain AI, service-based RAN, programmable RAN and digital twins are discussed. At last, the paper analyzes the challenges and opportunities for O-RAN evolution.

Mobile network traffic data offers unprecedented opportunities for innovative studies within and beyond networking. However, progress is hindered by the very limited access that the research community at large has to the real-world mobile network data that is needed to develop and dependably test mobile traffic data-driven solutions. As a contribution to overcome this barrier, we propose CartaGenie, a generator of realistic mobile traffic snapshots at city scale. Taking a deep generative modeling approach and through a tailored conditional generator design, CartaGenie can synthesize high-fidelity and artifact-free spatial traffic snapshots using only contextual information about the target geographical region that is easily found in public repositories. Hence, CartaGenie allows researchers to create their own realistic datasets of spatial traffic from open data about their region of interest. Experiments with real-world mobile traffic measurements collected in multiple metropolitan areas show that CartaGenie can produce dependable network traffic loads for areas where no prior traffic information is available, significantly outperforming a comprehensive set of benchmarks. Moreover, tests with practical case studies demonstrate that the synthetic data generated by CartaGenie is as good as real data in supporting diverse research-oriented mobile traffic data-driven applications.

Service-level mobile traffic data enables research studies and innovative applications with a potential to shape future service-oriented communication systems and beyond. However, real-world datasets reporting measurements at the individual service level are hard to access as such data is deemed commercially sensitive by operators. APPSHOT is a model for generating synthetic high-fidelity city-scale snapshots of service level mobile traffic. It can operate in any geographical region and relies solely on easily available spatial context information such as population density, thus allowing the generation of new and open traffic datasets for the research community. The design of APPSHOT is informed by an original characterization of service-level mobile traffic data. APPSHOT is a novel conditional GAN design instantiated by a convolutional neural network generator and two discriminators. The model features several other innovative mechanisms including multi-channel and overlapping patch based generation to address the unique challenges involved in generating mobile service traffic snapshots. Experiments with ground-truth data collected by a major European operator in multiple metropolitan areas show that APPSHOT can produce realistic network loads at the service level for areas where it has no prior traffic knowledge, and that such data can reliably support service-oriented networking studies.

Increased network traffic demands have led to ex-tremely dense network deployments. This translates to significant growth in energy consumption at the radio access networks, resulting in high network operation costs (OPEX). In this work, we apply deep reinforcement learning to reduce the energy consumption at the base station in dense wireless networks, by allowing cells that overlap in geographical areas to be put in standby mode according to the changing network conditions. We start by formulating the problem of the cell on/off energy saving in dense wireless networks as a Markov decision process. Then, a deep reinforcement learning (DRL) solution is proposed. This DRL solution takes into account different key performance indicators (KPIs) of both the network and user equipment and aims to reduce the energy consumed by the network without significantly impacting the overall KPIs. The performance of the proposed solution is evaluated using a practical network simulator.

RAN energy consumption is a major OPEX source for mobile telecom operators, and 5G is expected to increase these costs by several folds. Moreover, paradigm-shifting aspects of the 5G RAN architecture like RAN disaggregation, virtualization and cloudification introduce new traffic-dependent resource management decisions that make the problem of energy-efficient 5G RAN orchestration harder. To address such a challenge, we present a first comprehensive virtualized RAN (vRAN) system model aligned with 5G RAN specifications, which embeds realistic and dynamic models for computational load and energy consumption costs. We then formulate the vRAN energy consumption optimization as an integer quadratic programming problem, whose NP-hard nature leads us to develop GreenRAN, a novel, computationally efficient and distributed solution that leverages Lagrangian decomposition and simulated annealing. Evaluations with real-world mobile traffic data for a large metropolitan area are another novel aspect of this work, and show that our approach yields energy efficiency gains up to 25% and 42%, over state-of-the-art and baseline traditional RAN approaches, respectively.

This paper addressed mobile edge computing (MEC) mobility management using machine learning methods. The mobility decision was based on a reference signal received power (RSRP) value and uncertainty predictor. Neural networks (NNs) were used to establish the predictor, which output the RSRP mean values and standard deviations of different neighbor cells. Closed-form expressions of handover probabilities were derived. With these probabilities, the MEC server was able to cache user services in advance in order to minimize disruptions during handover. Real field data were collected in a typical dense urban area and used to evaluate the performance of the proposed algorithm.

The significant increase of Machine-Type Communication (MTC) devices has left cellular networks facing unprecedented challenges to support them. One of the major challenges is the Random Access (RA) process performed by idle or disconnected devices. As MTC devices can be highly synchronized, with numerous devices attempting to establish a connection at the same time, the limited available resources during RA allow only for a small number of them to connect to the network, while the rest are required to try again later, increasing their delay. In this paper, we tackle the challenge of the RA process and propose a novel algorithm based on the recent advancements of Non-Orthogonal Random Access (NORA) schemes and the addition of Reinforcement Learning (RL), that is able to increase the number of successful connections, and decrease the network access delay. Our results show that our approach significantly outperforms the currently used approaches.

Millimetre wave (mm-wave) communication is considered as one of the most important enablers for the fifth generation communication (5G) system to support data rate of Gbps and above. In some scenarios, it is crucial to maintain a line of sight (LOS) link for users enjoying 5G immersive experiences and thus requiring very high data rate. In this paper, we investigate the LOS probability in mm-wave systems. In particular, we study the impact of access point (AP) and blockage height on the LOS probability and propose a solution to effectively enhance the LOS coverage by using high-rise APs on top of low-rise APs normally installed on street furniture, e.g., lamp poles. Two deployment options are explored: 1) irregular deployment and 2) regular deployment, where LOS probability is derived for both cases. Simulation results show that the impact of AP height on LOS probability is significant and using coordinated high-rise APs jointly deployed with low-rise APs will substantially improve the LOS probability.

Millimetre-wave communication (licensed or unlicensed) is envisaged to be an important part of the fifth generation (5G) multi-RAT ecosystem. In this paper, we consider the spectrum bands shared by 5G cellular base stations and some existing networks, such as WiGig. Sharing the same band among such multiple radio access technologies (RATs) is very challenging due to the lack of centralized coordination and demands novel and efficient interference mitigation and coexistence mechanisms to reduce the mutual interference. To address this important challenge, we propose in this paper a novel multi-RAT coexistence mechanism where neighbouring 5G and WiGig base stations, each serving their own associated UEs, schedule their beam configurations in a distributed manner such that their own utility function, e.g. spectral efficiency, is maximized. We formulate the problem as a combinatorial optimization problem and show via simulations that our proposed distributed algorithms yield a comparable spectral efficiency for the entire networks as that using an exhaustive search, which requires global coordination among coexisting RATs and also has a much higher algorithmic complexity.

In this paper, an orthogonal training signal relaying technique for channel estimation in a dual-hop amplify-and-forward (AF) relay network with multiple relays is proposed. In an existing technique, the cross interference occurs between the precoded training signals of the relays since their precoded noise vector terms are not orthogonal. This leads to an increased channel estimation mean squared error (MSE) at the destination. To tackle the cross interference problem without changing the channel estimator at the destination, we propose to use an additional step at each relay prior to training signal precoding. That is, converting the received training signal vector to a scalar channel estimate term via estimating the channel of the first hop at each relay. In this case, the received noise vector at each relay is converted to an scalar channel estimation error term. When multiplying the mutually orthogonal precoding vectors with the scalar values (i.e. the estimated channels at the relays), the resultant precoded training signals of the relays remain mutually orthogonal. Hence the cross interference is eliminated. The proposed technique can provide a significantly lower channel estimation MSE at the destination as the number of relays M increases. Results show that the proposed technique yields 0.5 dB, 1.5 dB and 2 dB of BER performance gains over the existing method at a bit error rate (BER) of 0.001 when M are 2, 4 and 8 respectively.

In this paper, we propose the pilot design and channel estimation schemes for uplink block-spread code division multiple access (BS-CDMA) systems. In particular, we propose the use of a common spreading code to spread the pilot signals of all users such that a high bandwidth efficiency is achieved. The expense, however, is the multi-user interference (MUI) introduced in uplink channel estimation. On that account, three pilot design schemes are proposed to eliminate the MUI. We show through simulations that, a BS-CDMA system using the proposed pilot design schemes with recursive least squares (RLS) channel tracking algorithm achieves a comparable performance to a similar system using the pilot block based channel estimation (as employed in the LTE uplink) in low mobility scenario, while significantly outperforming that using the pilot block based channel estimation in high mobility scenario. In particular, the performance of BS-CDMA using the proposed channel estimation methods remains robust at a Doppler frequency of 400Hz.

In a cognitive radio system, the accuracy of spectrum sensing is critical for both primary and secondary systems. Both cooperative sensing and single-user multiple-antenna techniques have been proposed in the literature to enhance the accuracy of spectrum sensing. However, these techniques may require additional signaling and hardware costs, and can lead to user reliability problems or energy consumption issues. To avoid these problems, we propose a scheme that requires no extra signaling and low hardware costs, while still offering substantial diversity gains for enhancing the sensing performance. This is achieved by combining antenna selection with the spectrum sensing mechanism. We analyze the diversity order of the proposed scheme and show that no loss occurs relative to the case of single-user multiple-antenna sensing. This benefit is achieved while keeping a low implementation complexity as only a subset of RF chains are used in a given time period. Simulations show that a substantial sensing gain can be obtained compared to using other traditional multiple-antenna techniques in some practical cases.

Block-spread code division multiple access (BS-CDMA) systems have been shown to achieve multiuser interference (MUI) free reception when signals of all the users arrive at the base station synchronously. In practice, imperfect timing synchronization destroys the orthogonality among users and MUI occurs. In this paper, based on the fact that unequal interference power distribution occurs in an uplink BS-CDMA system where varying degrees of interference power occur among users and across the despread message, we apply unequal error protection (UEP) to mitigate the MUI due to quasi-synchronous reception. Two UEP methods are studied. It is shown through simulations that BS-CDMA systems using these methods achieve significant performance improvement in terms of packet-error-rate (PER) compared to conventional quasi-synchronous BS-CDMA systems where the same error correcting codes are applied to the entire information packet for all users.

In this paper, we propose an antenna selection method in a wireless cognitive radio (CR) system, which we term difference selection, whereby a single transmit antenna is selected at the secondary transmitter out of M possible antennas such that the weighted difference between the channel gains of the data link and the interference link is maximized. We analyze the mutual information and the outage probability of the secondary transmission in a CR system with difference antenna selection, and propose a method of optimizing these performance metrics subject to practical constraints on the peak secondary transmit power and the average interference power as seen by the primary receiver. The optimization is performed over two parameters: the peak secondary transmit power and the difference selection weight δ∈[0,1]. Furthermore, we show that the diversity gain of a CR system employing difference selection is an impulsive function of δ, in that a value of δ=1 yields the full diversity order of M and any other value of δ gives no diversity benefit. Finally, we demonstrate through extensive simulations that, in many cases of interest, difference selection using the optimal values of these two parameters is superior to the so-called ratio selection method disclosed in the literature.

Using mutually shift orthogonal spreading codes, block spread code division multiple access (BS-CDMA) systems have been shown to achieve multiuser interference (MUI) free reception when signals of all the users arrive at the base station synchronously. In practice, imperfect synchronization destroys the orthogonality among users and causes MUI. We present in this paper the design of spreading and despreading codes to reduce the MUI due to quasi-synchronous reception. A quasi- synchronous BS-CDMA system using the proposed spreading and despreading codes is shown to achieve a performance close to that of a synchronous system, while maintaining a low receiver complexity. The cost is reduced bandwidth efficiency in transmission, which becomes less significant as the number of users increases.

Recently, a block spreading code division multiple access (BS-CDMA) technique was presented whereby user-specific preceding along with orthogonal spreading codes are used to achieve multi-user interference (MUI) free when all users arrive at the base station simultaneously. In practice however, imperfect synchronization destroys the orthogonality among users and MUI occurs. This paper investigates the design of linear frequency domain equalizers to reduce the MUI in a quasi-synchronous BS-CDMA system. An optimal frequency domain linear minimum-mean squared error (LMMSE) equalizer is derived. Further simplification leads to a novel sub-optimal equalizer with reduced computational complexity. It is shown through simulation that the proposed equalizers effectively suppress the error floor due to quasi-synchronous reception when channel coding is applied.

In this paper, we propose a method of constructing a novel generalized space-time code from a linear dispersion (LD) "protocode" by using the block spreading (BS) technique. The resulting generalized space-time code, therefore referred to as the BS-LD code, is shown to achieve the same rate, capacity, and performance as its protocode in flat fading channels. Moreover, our BS-LD codes exhibit a higher bandwidth efficiency when overlayed onto a block transmission method such as orthogonal frequency division multiplexing (OFDM). This property suggests BS-LD codes are ideal for use in frequency selective fading channels.

A novel method of utilizing the channel state information at the transmitter (CSIT) for orthogonal frequency division multiplexing (OFDM) systems with multiple transmit antennas is presented in this paper. Using the conventional time reversal (TR) technique along with a specific OFDM symbol structure, the method facilitates the application of full-rate orthogonal space-time block coding (OSTBC) to systems with more than two transmit antennas for complex signalling. Compared to the conventional eigen- beamforming method, which maximizes the received signal- to-noise ratio (SNR) when CSIT is available, it is shown that the proposed system has a much reduced computational complexity. However, this complexity reduction comes at the expense of a loss of diversity in an uncoded system, which we show through an analysis of bit error rate (BER). Despite this drawback, we show through simulations that the proposed method achieves BER performance almost as well as the eigenbeamforming method when channel coding is employed, which is the case in most practical systems.

A novel methodology of transmit beamforming for systems subjected to equivalent isotropic radiated power restrictions is presented in this paper. Based on the properties of the radiation due to the optimal beamforming method compared with that due to the eigen-beamforming, beamforming vectors are designed by perturbing the scaled eigen-beamforming vector such that the peak-to-average power ratio of the spatial radiation is reduced. Two algorithms incorporating this general methodology are presented. With a computational complexity less than the optimal method, both are shown to outperform conventional sub-optimal beamforming methods. For example, in an investigated system with four transmit and one receive antennas, performance is improved by more than 1 dB at a packet-error-rate of 10−3.

Certain communications transmitters are restricted by their equivalent isotropic radiated power (EIRP) rather than the total transmitted power. Important examples are ultra-wideband systems. Use of channel knowledge at transmission for such systems, when there are multiple transmit antennas, is complicated due to the need to consider the spatial directivities of the radiation. Thus it is computationally prohibitive to implement optimal EIRP constrained beamforming schemes. This work presents a novel low complexity transmit beamforming method for such systems. Motivated by the radiation due to optimal schemes, the algorithm is based on perturbing the dominant channel eigenvector such that the peak-to-average power ratio of the spatial radiation is reduced. Simulation results confirm the improved system performance compared to existing sub-optimal beamforming methods.

This paper presents an improved active interference cancellation (AIC) algorithm for ultra wideband (UWB) systems with antenna selection on a per subcarrier basis. The improved algorithm overcomes the drawbacks of the conventional AIC algorithms by jointly minimizing the notch depth over multiple transmitter antennas, while ensuring that the power transmitted on the AIC tones adheres to the spectral mask imposed by the Federal Communications Committee (FCC). The improved algorithm is shown, through simulation and hardware demonstration, to provide sufficiently deep spectral notches for multiple transmitter antennas and to effectively suppress the excess power of the AIC tones generated by the conventional AIC algorithms.

This paper investigates spectrum shaping in ultra-wideband (UWB) communications in order to introduce spectral nulls to limit interference with narrowband signals. Each transmitted symbol is represented by a "coded Gaussian monocycle pulse" in which Gaussian monocycles are weighted, delayed and summed in accordance with a designed codeword. The use of the Gaussian monocycle ensures that the UWB spectrum mask established by the Federal Communications Commission (FCC) is met, and the codeword is designed to generate a spectral null at the frequency or frequencies being used by existing narrowband devices. Signals obtained with different spectrum shapings (e.g., Butterworth, Chebyshev, elliptical) and by introducing nulls at multiple interference frequency bands are discussed. This approach can be used in various systems; as one application, we simulate the performance of a coded monocycle UWB system with a spectral null in the presence of narrowband interference (NBI) using single carrier block transmission with frequency domain equalization (SC-FDE), and compare its performance with that of an uncoded SC-FDE UWB system using a single Gaussian monocycle. Our results show that NBI can be effectively suppressed by transmitting and matched filtering the pulse with a spectral null at the interference frequency, therefore improving the robustness of UWB systems to NBI

We investigate in this paper the effect of carrier frequency offset (CFO) and sampling timing offset (STO) on the performance of single carrier block transmission with frequency domain equalization (SC-FDE) over ultra-wideband (UWB) channels. Signal-to-interference-plus-noise ratio (SINR) of SC-FDE UWB in the presence of CFO is analyzed and compared with that of OFDM, where OFDM is shown to be more sensitive to CFO. We also study the effect of STO on the performance of SC-FDE. Two forms of STO are considered, i.e., a shifted sampling timing instant other than the optimal timing point and a distorted sampling rate other than the symbol rate. Our results show that the performance of SC-FDE is rather channel dependent when sampling at a non-optimal time instant within one symbol duration, where the energy of the sampled equivalent channel at the sampling point determines its BER performance. Moreover, an SC-FDE system is fairly sensitive to a distorted sampling rate at the receiver, where severe performance degradation can occur when the received signal is sampled at a sampling rate rather than symbol rate.

This paper investigates spectrum shaping in ultrawideband (UWB) communications in order to introduce spectral nulls to limit interference with narrowband signals. Each transmitted symbol is represented by a monocycle "coded" Gaussian pulse, where use of the monocycle Gaussian pulse ensures that the UWB spectrum mask established by the Federal Communications Commission (FCC) is met and where the codeword is designed to generate a spectral null at the frequency or frequencies being used by existing narrowband devices. Signals obtained with different spectrum shapings (e.g., Butterworth, Chebyshev, elliptical) and by introducing nulls at multiple interference frequency bands are discussed. The performance of a monocycle coded UWB system with a spectral null in the presence of narroband interference (NBI) using single carrier block transmission with frequency domain equalization (SC-FDE) is simulated and compared with that of an uncoded SC-FDE UWB system using a single Gaussian monocycle. Our results show that NBI can be effectively suppressed by transmitting and matched filtering the pulse with a spectral null at the interference frequency, therefore improving the robustness of SC-FDE UWB to NBI.

We propose a time-division multiple-access (TDMA) scheme for the binary phase-shift keying (BPSK) modulated single carrier block transmission with frequency domain equalization (SC-FDE) over ultra-wideband (UWB) channels. The transmitter and receiver matrices for the proposed TDMA SC-FDE system are derived to achieve the multiple-access interference (MAI) free and interblock-interference (IBI) free transmission without the insertion of cyclic prefix (CP) between blocks, avoiding long CP and large fast Fourier transform (FFT) size in high-speed UWB communications. The performance of the proposed TDMA SC-FDE system over UWB channels is investigated and compared with that of the block spread code-division multiple-access (CDMA) scheme. The impact of oversampling diversity and imperfect channel estimation on the performance of the proposed system are also investigated. Results show that the proposed TDMA scheme can effectively eliminate the error floor in CDMA due to insufficient CP, and it is more efficient to accommodate larger number of users than CDMA in high data rate transmission.

Recently, single-carrier block transmission with frequency-domain equalization (SC-FDE) has been shown to be a promising candidate for ultra-wideband (UWB) communications. In this paper, we address the channel estimation problem for SC-FDE transmission over UWB channels. A mean-square error (MSE) lower bound for the frequency-domain linear minimum mean-squared error (LMMSE) channel estimator is derived, and the optimal pilot sequence that achieves this lower bound is obtained. Further simplification leads to a frequency-domain channel estimator with reduced computational complexity. The performance of the simplified estimator for SC-FDE over UWB channels is evaluated and compared with that with perfect channel state information. The effects of nonoptimal and optimal pilot symbols are also investigated. Our results show that the proposed frequency-domain channel estimator performs well over UWB channels with only small performance degradation, compared with that with perfect channel estimation

This letter proposes cyclic prefixed single carrier transmission with frequency domain equalization (SC-FDE) as an alternative physical layer solution for UWB communications. The performance of SC-FDE over IEEE 802.15.3a UWB channel models is analyzed, simulated and compared with that of impulse based single carrier UWB (SC-UWB) and multicarrier UWB employing orthogonal frequency division multiplexing (OFDM-UWB). The impact of channel coding on the performance of OFDM and SC-FDE in UWB is also studied. Our results demonstrate performance advantage of the SC-FDE scheme, especially when implementation issues such as low complexity and low power consumption for UWB are taken into consideration. The performance of SC-FDE with diversity combining using oversampling is also investigated

Recently, single carrier block transmission with frequency domain equalization (SC-FDE) has been shown to be a promising candidate for ultra-wideband (UWB) communications. In this paper, we address the channel estimation problem for SC-FDE transmission over UWB channels. A mean square error (MSE) lower bound for the frequency domain LMMSE channel estimator is derived and the optimal pilot sequence that achieves this lower bound is obtained. Further simplification leads to a frequency domain channel estimator with reduced computational complexity. The performance of the simplified estimator for SC-FDE over UWB channels is evaluated and compared with that of perfect channel state information. The effects of non-optimal and optimal pilot symbols are also investigated. Our results show that the proposed frequency domain channel estimator performs well over UWB channels with only small performance degradation compared to the perfect channel estimation

We propose a time division multiple access (TDMA) scheme for the BPSK modulated single carrier block transmission with frequency domain equalization (SC-FDE) over ultra-wideband (UWB) communications. The transmitter and receiver matrices for the proposed TDMA SC-FDE system are derived to achieve the multiple access interference (MAI) free and interblock-interference (IBI) free transmission without the insertion of cyclic prefix (CP) between blocks, circumventing the need for long CP and large FFT size in high speed UWB communications. The performance of the proposed TDMA SC-FDE system over UWB channels is investigated and compared with that of the block spread code division multiple access (CDMA) scheme. The effects of oversampling diversity and imperfect channel estimation are also investigated. Results show that the proposed TDMA scheme can effectively eliminate the error floor in CDMA due to insufficient CP, and it is more efficient to accommodate larger number of users than CDMA in high data rate transmission

This paper investigates the performance of cyclic prefixed single carrier with frequency domain equalization (SC-FDE) transmission over the ultra-wideband (UWB) propagation environment. The performance of SC-FDE over IEEE 802.153a UWB channel models is analyzed, simulated and compared with that of impulse based single carrier UWB (SC-UWB) and multicarrier UWB (MC-UWB) employing orthogonal frequency-division-multiplexing (OFDM). The impact of channel coding with different code rates on the performance of OFDM and SC-FDE in UWB is also studied. Our results demonstrate conclusive performance advantage of the SC-FDE scheme, especially when implementation issues such as low complexity and low power consumption for UWB are taken into account.