Fast-varying Channels Research Articles

Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication

Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this paper addresses the question: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how can the best relay be selected with minimal overhead from beam management?</i> In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam indices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numerical experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels.

Learning Based Multi-Channel Access in Vehicular Networks With Illegal Operators and Users

This paper investigates the multi-channel access (MCA) problem for vehicular networks in presence of illegal operators and users. Due to the limited spectrum resource, vehicle-to-vehicle (V2V) links need to reuse the frequency spectrum allocated for vehicle-to-infrastructure (V2I) links in networks. For high-mobility environments of vehicular networks, the fast-varying channel conditions will inevitably lead to the significant uncertainty of the acquired channel state information (CSI). Hence, the traditional centralized MCA methods, which rely on the CSI, could not be performed in a timely manner. We formulate the MCA of the vehicle users as a distributed optimization problem, by maximizing the sum capacity of V2I links while guaranteeing the reliability of V2V links as well as reducing the cost due to frequency hopping. A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework is introduced to tackle this problem, where each vehicle user, connecting with either a V2I or V2V link, acts as a learning agent to make spectrum access decisions rapidly and locally to meet the latency requirement. In order to adapt to the dynamic environment, we further propose a delayed interaction aided MADDPG algorithm, which enables online training for the distributed MCA problem. Moreover, to guarantee the proposed algorithm to converge stably, we optimize the selecting weights of experience traces and propose a Batch Prioritized Experience Replay (BPER) strategy to ensure that the high priority traces can be learned timely. Compared with baselines, simulation results showcase remarkable performance gain of our proposed algorithm.

Practical Distributed Reception for Wireless Body Area Networks Using Supervised Learning

Medical applications have driven many areas of engineering to optimize diagnostic capabilities and convenience. In the near future, wireless body area networks (WBANs) are expected to have widespread impact in medicine. To achieve this impact, however, significant advances in research are needed to cope with the changes of the human body’s state, which make coherent communications difficult or even impossible. In this paper, we consider a realistic noncoherent WBAN system model where transmissions and receptions are conducted without any channel state information due to the fast-varying channels of the human body. Using distributed reception, we propose several symbol detection approaches where on-off keying (OOK) modulation is exploited, among which a supervised-learning-based approach is developed to overcome the noncoherent system issue. Through simulation results, we compare and verify the performance of the proposed techniques for noncoherent WBANs with OOK transmissions. We show that the well-defined detection techniques with a supervised-learning-based approach enable robust communications for noncoherent WBAN systems.

On the spectral efficiency of cell‐free massive MIMO system in irregular 5G mobile networks

SummarySpectral efficiency (SE) is one of the eminent requirements in 5G mobile networks. Cell‐free (CF) massive MIMO is deemed a key technology to provide substantial SE in 5G compared with the cellular and small cell approaches. Most of the prior studies have been focusing on the access points (APs) uniform distribution to assess the SE performance. However, 5G networks are typically dense, irregularly distributed, and mostly constrained by channel impairments. Therefore, considering a uniform distribution of APs is unrealistic. This paper considers a practical network distribution by taking into account the irregular and adaptive APs distribution based on the Poisson point process approach and over the Rician fading channels. Therein, the downlink (DL) SE of CF massive MIMO system is accurately investigated bearing in mind the AP's irregular distribution and fast channel variation for both perfect and imperfect channel state information (CSI) cases. The simulation results have shown that the DL SE of the CF massive MIMO system is considerably affected when considering the irregular deployment of APs compared with the uniform distribution, especially when the phase noise effect is tense. The SE gain performance is reduced by 37.9% in the DL transmissions, compared with the uniform model. Besides, the results have proven that the DL SE gain is remarkably improved when the APs are largely distributed within the network. However, the gained DL SE is affected when increasing the user's density and length of the uplink training period.

Two-Timescale Resource Management for Ultrareliable and Low-Latency Vehicular Communications

Ultra-reliable low-latency communication (URLLC) is essential for future vehicle-to-vehicle (V2V) networks to improve traffic safety and enhance driving experience. Due to the fast-varying channel caused by high mobility, guaranteeing latency and reliability performance of the V2V links is a tremendous challenge. In this paper, we propose a novel resource allocation framework to support ultra-reliable low-latency V2V communications. The proposed framework includes both large-scale and small-scale resource optimizations. The large-scale resource allocation is performed at the central base station based on large-scale channel information periodically collected from vehicles. On the other hand, the small-scale resource allocation is performed at the vehicles according to instantaneous channel and queuing information. We develop optimal solutions for both resource allocation problems. With the proposed optimal solutions, the latency performance at the occurrence of extreme events is enhanced by enabling spectrum sharing among the vehicles. Simulation results demonstrate that the proposed algorithm can effectively improve the URLLC performance compared against the benchmark algorithm.

Efficient Non-Uniform Pilot Design for TDCS.

The Internet of Things (IoT) leads the era of interconnection, where numerous sensors and devices are being introduced and interconnected. To support such an amount of data traffic, wireless communication technologies have to overcome available spectrum shortage and complex fading channels. The transform domain communication system (TDCS) is a cognitive anti-interference communication system with a low probability of detection and dynamic spectrum sensing and accessing. However, the non-continuous and asymmetric spectrum brings new challenges to the traditional TDCS block-type pilot, which uses a series of discrete symbols in the time domain as pilots. Low efficiency and poor adaptability in fast-varying channels are the main drawbacks for the block-type pilot in TDCS. In this study, a frequency domain non-uniform pilot design method was proposed with intersecting, skewing, and edging of three typical non-uniform pilots. Some numerical examples are also presented with multipath model COST207RAx4 to verify the proposed methods in the bit error ratio and the mean square error. Compared with traditional block-type pilot, the proposed method can adapt to the fast-varying channels, as well as the non-continuous and asymmetric spectrum conditions with much higher efficiency.

Coordinates-Based Resource Allocation Through Supervised Machine Learning

Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies. Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions. In such cases, an estimate of the terminals’ position information provides an alternative to estimating the channel condition. In this work, we propose a coordinates-based resource allocation scheme using supervised machine learning techniques, and investigate how efficiently this scheme performs in comparison to the traditional approach under various propagation conditions. We consider a simple system setup as a first step, where a single transmitter serves a single mobile user. The performance results show that the coordinates-based resource allocation scheme achieves a performance very close to the CSI-based scheme, even when the available user’s coordinates are erroneous. The performance is quite consistent, especially when complex learning frameworks like random forest and neural network are used for resource allocation. In terms of applicability, a training time of about 4 s is required for coordinates-based resource allocation using random forest algorithm, and the appropriate resource allocation is predicted in less than 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> with a learnt model of size < 1 kB.

WITHDRAWN: Outage analysis of half-duplex decode-and-forward relaying with cooperative NOMA in vehicular networks

Abstract Vehicular networks are the emerging as a promising technology in 5G communication with many challenges still unaddressed. Due to the dynamic nature of the vehicular networks, fast-varying channel conditions many of the researchers restricted their work to block fading channels. In this paper, in contrast to the orthogonal multiple accesses, cooperative non-orthogonal multiple accesses is considered for two-way decode and forward relaying with network coding to mitigate the impact of continuous-time varying channels. An explicit closed form expression is derived for sum outage probabilities at source and destination. Simulation results manifest that packet error rate decreases for optimal data phases and the position of the decodable relay from source and transmit power affirm the impact over sum outage probabilities compared to two-way amplify and forward relaying and decode and forward with coding and decoding schemes.

Antenna isolation method using sub-array structure with soft-surface for Tx/Rx separated FDD hybrid beamforming system

The FDD method is normally used for the stability of the Tx and the Rx links in a fast-varying channel. It is required to physically separate the Tx and Rx arrays for independent Tx and Rx beam control. In this system, it is important to reduce the Tx self-interference (SI) that causes the problem of active element saturation and ADC dynamic range in the Rx beamformer. To suppress Tx SI, we proposed two simple techniques. The first is the general soft-surface which attenuates the surface current flow from the Tx tile array to the Rx array. The second is the sub-array structure of the Rx hybrid beamforming system. Based on the Cauchy–Schwartz inequality, the electrical sum by the sub-array acts as a destructive interference of the complex SI signals, and it can be shown as an additional isolation effect. The proposed idea was validated through measurement.

Data-Aided Doppler Compensation for High-Speed Railway Communications Over mmWave Bands

Millimeter wave communications show great potentials in many applications, one of which is the high-speed railway(HSR) communication system. However, a major challenge is the Doppler effect caused by the relative-movement between the train and the base station (BS), which leads to fast channel variation. To compensate for the Doppler shift, an accurate channel model is indispensable, and the far-field channel model is generally employed, which assumes that the dimensions of the antenna arrays are negligible compared to the distance between transmitter and receiver. This model is widely used in Cellular systems, but the underlining assumption is not always true for railway communication systems. In this paper, the modeling of the Doppler effect for millimeter wave in HSR communications is conducted, and data-aided Doppler estimation and compensation algorithms are designed based on the new model. We show that the conventional far-field channel model is based on the first-order Taylor expansion of the actually channel, and the second-order component cannot be ignored for HSR communications. Extensive simulations are conducted to verify the validity of the new model and the effectiveness of the proposed algorithms.

Energy-efficiency maximization in D2D-enabled vehicular communications with consideration of dynamic channel information and fairness

Device-to-device (D2D) communications in a cellular network can be implemented on vehicular communications. However, high mobilities of vehicles generate fast channel variations in the communications. Channel description has to be specified in the D2D-enable vehicular communications network. In this paper, the vehicle speed and the sampling time are involved to the channel description such that the dynamic information of vehicle is integrated with the channel gain. By doing so, the channel description is more practical. The power control and spectrum sharing problem is formulated in order to maximize the energy-efficiency (EE) of total vehicle-to-infrastructure (V2I) links. It also attempts to ensure reliable communications in a multi-cellular users multi-D2D users frequency division multiple access (FDMA) cellular environment. The reformulated objective function in the fractional form is proved to be quasi-concave, and it can be solved by the Dinkelbach method which requires low computational complexity. In addition, the Hungarian algorithm is used to determine the optimal match of cellular user equipments (CUEs) and V2V pairs. User fairness is further considered under the goal of maximizing overall energy efficiency. Then, the approach of minimum EE maximization is implemented on all V2I links in order to improve system fairness. Finally, the system performance of the proposed scheme is validated by numerical simulations. The results show that the algorithm is effective to improve the energy efficiency and robustness in the dynamic communications environment.

A Reinforcement Learning Method for Joint Mode Selection and Power Adaptation in the V2V Communication Network in 5G

A 5G network is the key driving factor in the development of vehicle-to-vehicle (V2V) communication technology, and V2V communication in 5G has recently attracted great interest. In the V2V communication network, users can choose different transmission modes and power levels for communication, to guarantee their quality-of-service (QoS), high capacity of vehicle-to-infrastructure (V2I) links and ultra-reliability of V2Vlinks. Aiming atV2V communication mode selection and power adaptation in 5G communication networks, a reinforcement learning (RL) framework based on slow fading parameters and statistical information is proposed. In this paper, our objective is to maximize the total capacity of V2I links while guaranteeing the strict transmission delay and reliability constraints of V2V links. Considering the fast channel variations and the continuous-valued state in a high mobility vehicular environment, we use a multi-agent double deep Q-learning (DDQN) algorithm. Each V2V link is considered as an agent, learning the optimal policy with the updated Q-network by interacting with the environment. Experiments verify the convergence of our algorithm. The simulation results show that the proposed scheme can significantly optimize the total capacity of the V2I links and ensure the latency and reliability requirements of the V2V links.

Efficient Joint Channel Equalization and Tracking for V2X Communications Using SC-FDE Schemes

Our aim with this paper is to present a solution suitable for vehicle-to-everything (V2X) communications, particularly, when employing single-carrier modulations combined with frequency-domain equalization (SC-FDE). In fact, we consider the V2X channel to be doubly-selective, where the variation of the channel in time is due to the presence of a Doppler term. Accordingly, the equalization procedure is dealt by a low-complexity iterative frequency-domain equalizer based on the iterative block decision-feedback equalization (IB-DFE) while the tracking procedure is conducted employing an extended Kalman filter (EKF). The proposed system is very efficient since it allows a very low density of training symbols, even for fast-varying channels. Furthermore only two training symbols are required to initialize the tracking procedure. Thus, ensuring low latency together with reduced channel estimation overheads.

Optimal Power Allocation and Capacity Analysis for D2D-Enabled Vehicular Communications

Wireless Communication is important to recover transmitted information by accommodating reliable Information flow to allow safety, mobility and environmental applications. In cellular communication resources are shared with the users to improve spectral reuse and enhance channel capacity. Device-to-Device (D2D) communication has become a promising technology for wireless engineers to optimize the network performance. In vehicular environment, the design of resource allocation schemes for D2D-enabled networks need to be properly addressed because of the fast channel variations due to high mobility. In this work, Radio Resource Management (RRM) for D2D-based V2X (Vehicle to Everything) communications including both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication are implemented. Power is allocated based on slowly varying large-scale fading information of wireless channels of LTE standard The objective is to maximize the ergodic capacity of V2I connections by ensuring reliability for each V2V link. Sum ergodic capacity of all V2I links is first taken as the optimization goal to maximize the general V2I link throughput. Minimum ergodic capacity maximization is then taken into consideration to offer a more uniform capacity performance throughout all V2I links. Various algorithms that gives optimal power allocation are proposed and compared. Here, the capacity maximization between highway areas and urban areas are compared and concluded that capacity maximization will be higher in urban areas then on highways.

Optimal Power Allocation and Capacity Analysis for D2D-Enabled Vehicular Communications

Wireless Communication is important to recover transmitted information by accommodating reliable Information flow to allow safety, mobility and environmental applications. In cellular communication resources are shared with the users to improve spectral reuse and enhance channel capacity. Device-to-Device (D2D) communication has become a promising technology for wireless engineers to optimize the network performance. In vehicular environment, the design of resource allocation schemes for D2D-enabled networks need to be properly addressed because of the fast channel variations due to high mobility. In this work, Radio Resource Management (RRM) for D2D-based V2X (Vehicle to Everything) communications including both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication are implemented. Power is allocated based on slowly varying large-scale fading information of wireless channels of LTE standard The objective is to maximize the ergodic capacity of V2I connections by ensuring reliability for each V2V link. Sum ergodic capacity of all V2I links is first taken as the optimization goal to maximize the general V2I link throughput. Minimum ergodic capacity maximization is then taken into consideration to offer a more uniform capacity performance throughout all V2I links. Various algorithms that gives optimal power allocation are proposed and compared. Here, the capacity maximization between highway areas and urban areas are compared and concluded that capacity maximization will be higher in urban areas then on highways.

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.

Downlink Channel Prediction for Time-Varying FDD Massive MIMO Systems

Accurate channel estimation is important for massive multiple-input multiple-output (mMIMO) to ensure good performance. Considering mMIMO application in 5G, frequency division duplexing (FDD) can provide higher data rate and wider coverage than the time division duplexing (TDD) mode. However, since the uplink/downlink channel is not straightforwardly reciprocal, FDD downlink channel estimation requires heavier training and computation than TDD mode due to the massive number of antennas. In addition, fast channel variation renders the real time estimation even more difficult. In this paper, we propose a downlink channel prediction scheme for FDD mMIMO, which requires only the TDD overhead. Specifically, the downlink channel matrix is represented by three components: steering matrix (frequency dependent), fading coefficients (time varying), and time delays (semi-static). By the proposed scheme, these three components can be obtained through uplink training. In addition, fast tracking and prediction is leveraged to obtain the real time channel state information. Simulation results show that accurate channel prediction is obtained via low cost and complexity by the proposed scheme.

Design of coherence-aware channel indication and prediction for rate adaptation

A number of rate adaptation protocols have been proposed using instantaneous channel quality to select the physical layer data rate. However, the indication of channel quality varies widely across platforms from simply a received signal strength level to a measurement of signal-to-noise ratio (SNR) across sub-carriers, with each channel quality indicator having differing levels of measurement error. Moreover, due to fast channel variations, even aggressive channel probing fails to offer an up-to-date notion of channel quality. In this paper, we propose a coherence-aware Channel Indication and Prediction algorithm for Rate Adaptation (CIPRA) and evaluate it analytically and experimentally, considering both the effects of measurement errors and the staleness of channel quality indicators. CIPRA uses the minimum mean square error (MMSE) method and first-order prediction. Our evaluation shows that CIPRA jointly considers the time interval over which the prediction will occur and the coherence time of the channel to determine the optimal window size for previous channel quality indicator measurements. Also, we demonstrate that CIPRA outperforms existing methods in terms of prediction fidelity and throughput via experimental results. By combining a strong channel indicator with the coherence-aware MMSE first-order channel prediction algorithm, CIPRA nearly doubles the throughput achieved in the field from the indication and prediction method currently used by off-the-shelf WiFi interfaces.

Efficient Beam Alignment for Millimeter Wave Single-Carrier Systems With Hybrid MIMO Transceivers

Communication at millimeter wave (mm-wave) bands is expected to become a key ingredient of the next generation (5G) wireless networks. Effective mm-wave communications require fast and reliable methods for beamforming at both the user equipment (UE) and the base station sides, in order to achieve a sufficiently large signal-to-noise ratio after beamforming. We refer to the problem of finding a pair of strongly coupled narrow beams at the transmitter and receiver as the beam alignment problem. In this paper, we propose an efficient BA scheme for single-carrier mm-wave communications. In the proposed scheme, the BS periodically probes the channel in the downlink via a pre-specified pseudo-random beamforming codebook and pseudo-random spreading codes, letting each UE estimate the angle-of-arrival/angle-of-departure (AoA-AoD) pair of the multipath channel for which the energy transfer is maximum. We leverage the sparse nature of mm-wave channels in the AoA-AoD domain to formulate the BA problem as the estimation of a sparse non-negative vector. Based on the recently developed non-negative least squares technique, we efficiently find the strongest AoA-AoD pair connecting each UE to the BS. We evaluate the performance of the proposed scheme under a realistic channel model, where the propagation channel consists of a few multipath components each having different delays, AoAs-AoDs, and Doppler shifts. The channel model parameters are consistent with the experimental channel measurements. The simulation results indicate that the proposed method is highly robust to fast channel variations caused by the large Doppler spread between the multipath components. Furthermore, we also show that after achieving BA, the beamformed channel is essentially frequency-flat, such that single-carrier communication needs no equalization in the time domain.

Realizing Multi-Gbps Vehicular Communication: Design, Implementation, and Validation

This paper proposes a vehicular communication system that can achieve multi-Gbps data rate transmission for train and car applications. Employing a millimeter-wave frequency band around 25 GHz, the proposed system provides mobile backhaul connectivity for vehicle user equipments (UEs). In order to support a very high data rate with such a high carrier frequency while guaranteeing sufficient robustness against high mobility-related behaviors such as fast channel variation and unstable handover, we employ a relaying network architecture consisting of a backhaul link to a vehicle UE and an in-vehicle access link. Based on that, we provide a set of fundamental design elements including numerology, frame structure, the reference signal, multi-antenna scheme, and handover. We then validate the proposed vehicular communication system by implementing an experimental testbed consisting of baseband modem, RF front end, an array antenna units, and by performing field trials in an actual subway tunnel and urban road environments. The experimental validation results reveal that providing multi-Gbps backhaul transmission is possible and worthwhile for both train and car scenarios.