The performance of millimeter-wave (mmWave) and sub-terahertz (sub-THz) communication systems is significantly impaired by sensitivity to sudden blockages. In this work, we employ machine learning (ML) and our physics-based simulation tool to warn about the upcoming blockage tens of 5G frames ahead for highway speeds, providing a sufficient time for a proactive response. Performance of this ML-aided early-warning-of-blockage (ML-EW) algorithm is analyzed for realistic outdoor mobile environments with diverse reflectors and antenna arrays placed at the base station (BS) and user equipment (UE) over a range of mmWave and sub-THz frequencies. ML accuracy of about 90% or higher is demonstrated for highway UE, blocker, and reflector speeds, multiple-input-multiple-output (MIMO) systems, and frequencies in the mmWave/sub-THz range.

Freshwater scarcity continues to pose a significant threat to communities worldwide. With the growing integration of distributed energy resources (DERs), there is an increasing opportunity to couple energy generation with desalination technologies to meet water and energy demands in a unified system. This paper investigates the convergence behavior of distributed consensus algorithms in such coupled water-energy systems, where the interconnection between energy and water processes introduces additional complexity. Specifically, the study examines the impact of different weight-matrix-construction schemes on the convergence speed of the distributed consensus algorithm and its robustness to packet loss. The simulation results show that using the optimal-constant-weight scheme achieves the fastest and most robust convergence but requires global knowledge of the network. However, the maximum-constant-weight scheme, despite requiring global information, has slower convergence speed and is more sensitive to packet loss. In contrast, the local-degree-weight scheme provides comparable performance to the optimal-constant-weight scheme without requiring the system’s global information.

A bargaining game is investigated for cooperative energy management in microgrids. This game incorporates a fully distributed and realistic cooperative power scheduling algorithm [cooperative and distributed energy scheduling (CoDES)] as well as a distributed Nash bargaining solution based method of allocating the overall power bill resulting from CoDES. A novel weather-based stochastic renewable generation (RG) prediction method is incorporated in the power scheduling. We demonstrate the proposed game using a four-user grid-connected microgrid model with diverse user demands, storage, and RG profiles and examine the effect of weather prediction on day-ahead power scheduling and cost/profit allocation. Finally, the impact of users' ambivalence about cooperation and /or dishonesty on the bargaining outcome is investigated, and it is shown that the proposed game is resilient to malicious users' attempts to avoid payment of their fair share of the overall bill.

In cognitive radio (CR) networks, the secondary users (SUs) sense the spectrum licensed to the primary users (PUs) to identify and possibly transmit over temporarily unoccupied channels. Cooperative sensing was proposed to improve the sensing accuracy, but in heterogeneous scenarios, SUs do not contribute equally to the cooperative sensing result because they experience different received PU signal quality at their sensors. In this paper, a two-layer cooperative game is developed for distributed sensing and access in multichannel CR ad hoc networks, where the SUs' transmission opportunities are commensurate with their sensing contributions, thus fostering cooperation and eliminating free-riders. Numerical results show that the proposed two-layer game is computationally efficient and outperforms previously investigated collaborative sensing and spectrum access approaches in heterogeneous multichannel CR scenarios in terms of energy efficiency, throughput, SU fairness, and complexity. Moreover, it is demonstrated that this game is robust to changes in the network topology and the number of SUs. Finally, a new physical-layer approach is proposed to distribute the network-level miss-detection constraints fairly among the interfering SUs for guaranteed PU protection and demonstrate the performance advantages of the AND-rule combining of spectrum sensing results for heterogeneous SUs.

Multi-agent networked linear dynamic systems have attracted the attention of researchers in power systems, intelligent transportation, and industrial automation. The agents might cooperatively optimize a global performance objective, resulting in social optimization, or try to satisfy their own selfish objectives using a noncooperative differential game. However, in these solutions, large volumes of data must be sent from system states to possibly distant control inputs, thus resulting in high cost of the underlying communication network. To enable economically viable communication, a game-theoretic framework is proposed under the communication cost, or sparsity, constraint, given by the number of communicating state/control input pairs. As this constraint tightens, the system transitions from dense to sparse communication, providing the tradeoff between dynamic system performance and information exchange. Moreover, using the proposed sparsity-constrained distributed social optimization and noncooperative game algorithms, we develop a method to allocate the costs of the communication infrastructure fairly and according to the agents' diverse needs for feedback and cooperation. Numerical results illustrate utilization of the proposed algorithms to enable and ensure economic fairness of wide-area control among power companies.

In Cognitive Radio (CR) ad hoc networks, secondary users (SU) attempt to utilize valuable spectral resources without causing significant interference to licensed primary users (PU). While there is a large body of research on spectrum opportunity detection, exploitation, and adaptive transmission in CR, most existing approaches focus only on avoiding PU activity when making sensing decisions. Since the myopic sensing strategy results in congestion and poor throughput, several collision-avoidance sensing approaches were investigated in the literature. However, they provide limited improvement. A channel-aware myopic sensing strategy that adapts the reward to the fading channel state information (CSI) of the SU link is proposed. This CSI varies over the CR spectrum and from one SU pair to another due to multipath and shadow fading, thus randomizing sensing decisions and increasing the network throughput. The proposed joint CSI adaptation at the medium access control (MAC) and physical layers provides large throughput gain over randomized sensing strategies and/or conventional adaptive transmission methods. The performance of the proposed CSI-aided sensing strategy is validated for practical network scenarios and demonstrated to be robust to CSI mismatch, sensing errors, and spatial channel correlation.

In many Vehicular Ad-hoc Network applications, such as hazard warning and traffic coordination, the message dissemination in unreliable and highly mobile network environment is a key challenge. In order to understand the relationship between dissemination latency and reliability, we analyze the latency of gaining α-reliability that a node correctly receives a message with probability larger than α (0min(α). Besides dissemination reliability requirement α, tmin(α) also depends on node's original distance from the source, node mobility, channel reliability, and traffic flow. Numerical analysis discloses several interesting insights that 1) tmin(α) is dominated by the first attempt to send the message to a destination, 2) node mobility has little impact on tmin(α) in emergency information dissemination, and 3) transmission range and node density greatly affect dissemination latency and reliability.

Mobile ad hoc networks (MANETs) are self-configuring wireless networks that lack permanent infrastructure and are formed among mobile nodes on demand. Rapid node mobility results in dramatic channel variation, or fading, that degrades MANET performance. Employing channel state information (CSI) at the transmitter can improve the throughput of routing and medium access control (MAC) protocols for mobile ad hoc networks. Several routing algorithms in the literature explicitly incorporate the fading signal strength into the routing metric, thus selecting the routes with strong channel conditions. While these studies show that adaptation to the time-variant channel gain is beneficial in MANETs, they do not address the effect of the outdated fading CSI at the transmitter. For realistic mobile node speeds, the channel gain is rapidly varying, and becomes quickly outdated due the feedback delay. We analyze the link throughput of joint rate adaptation and adaptive relay selection in the presence of imperfect CSI. Moreover, for an 802.11 network that employs geographic opportunistic routing with adaptive rate and relay selection, we propose a novel method to reduce the effect of the feedback delay at the MAC layer in the presence of Rayleigh fading. This method exploits channel reciprocity and fading prediction and does not require significant modification to the existing 802.11 frame structure. Extensive network simulations demonstrate that the proposed approach significantly improves the throughput, delay, and packet delivery ratio for high mobile velocities relative to previously proposed approaches that employ outdated CSI at the transmitter.

Adaptive bit-interleaved coded modulation (ABICM) is attractive for rapidly varying mobile radio channels due to its robustness to imperfect channel state information (CSI). A novel ABICM method that exploits the expurgated bound to maintain the target bit error rate (BER) for diverse CSI conditions is proposed and evaluated for an adaptive mobile radio orthogonal frequency division-multiplex (OFDM) system aided by the long-range fading prediction. It is demonstrated that ABICM is much less sensitive to prediction errors than adaptive modulation techniques that do not employ interleaving. However, reliable fading prediction is still necessary for ABICM to achieve high spectral efficiency for practical channel conditions.

Code diversity integrates space-time coding with beamforming by using a small number of feedback bits to select from a family of space-time codes. Different codes lead to different induced channels at the receiver, where Channel State Information (CSI) is used to instruct the transmitter how to choose the code. Feedback can be combined with sub-optimal low complexity decoding of the component codes to match Maximum-Likelihood (ML) decoding performance of any individual code in the family. It can also be combined with ML decoding of the component codes to improve performance beyond ML decoding performance of any individual code. Prior analysis of code diversity did not take into account the effect of the mobile speed and the delay in the feedback channel. This paper demonstrates the practicality of code diversity in space-time coded systems by showing that performance gains based on instantaneous feedback are largely preserved when long-range prediction of time-varying correlated fading channels is employed to compensate for the effect of the feedback delay. To maintain prediction accuracy for realistic SNR, noise reduction that employs oversampled pilots is used prior to fading prediction. We also propose a robust low pilot rate method that utilizes interleaving to improve the spectral efficiency. Simulations are presented for two channel models: the conventional Jakes model and a realistic physical channel model where the parameters associated with the reflectors vary in time and the arrival rays have different strengths and asymmetric arrival angles.

Multicarrier code-division multiple access (MCCDMA) system with adaptive frequency hopping (AFH) has attracted attention of researchers due to its excellent spectral efficiency. A suboptimal water-filling (WF) channel allocation algorithm was previously proposed for the reverse link of this system. To overcome the limitations of the WF algorithm in the presence of fading-induced near-far problem, a new allocation algorithm is proposed and demonstrated to improve performance when the conventional matched filter (MF) receiver is employed. Moreover, the allocation methods are extended to accommodate multiuser detectors (MUDs) at the receiver for MC-CDMA system with AFH. It is demonstrated that the combination of the improved allocation algorithm and the linear MUDs is very efficient in mitigating the fading and multi-access interference (MAI) for realistic mobile radio channels with correlated subcarriers, channel state information (CSI) mismatch, and imperfect power control. Numerical results show that the proposed adaptive transmission method has much greater system capacity than conventional non-adaptive MC direct-sequence (DS)-CDMA system.

Subchannel allocation for the reverse link of multi-carrier code-division multiplex-access (MC-CDMA) system with adaptive frequency hopping (AFH) is investigated. An efficient allocation method that can be employed with the matched filter (MF) receiver as well as with multiuser detectors (MUDs) by exploiting the appropriate signal to interference and noise ratio (SINR) analysis is described. It is demonstrated that combined improved subcarrier allocation and the linear decorrelating detector (LDD) are very efficient in mitigating multiple-access interference (MAI), resulting in much larger system capacity than for a non-adaptive MC direct-sequence (DS)-CDMA system. This conclusion is confirmed for realistic fading channels with correlated subcarriers and imperfect channel state information (CSI) at the transmitter.

The long range fading prediction algorithm for Slow Frequency Hopping (SFH) systems is proposed and demonstrated to enable combined adaptive modulation and adaptive frequency diversity to mitigate the effects of fading and partial-band interference. Significant performance gains are demonstrated relative to non-adaptive methods in realistic mobile radio SFH channels where the total bandwidth does not exceed approximately 15 times the coherence bandwidth.

Adaptive transmission methods can potentially aid the achievement of high data rates required for mobile radio multimedia services. To realize this potential, the transmitter needs accurate channel state information (CSI) for the upcoming transmission frame. In most mobile radio systems, the CSI is estimated at the receiver and fed back to the transmitter. However, unless the mobile speed is very low, the estimated CSI cannot be used directly to select the parameters of adaptive transmission systems, since it quickly becomes outdated due to the rapid channel variation caused by multipath fading. To enable adaptive transmission for mobile radio systems, prediction of future fading channel samples is required. Several fundamental issues arise in the design and testing of fading prediction algorithms for adaptive transmission systems. These include complexity, robustness, choice of an appropriate channel model for algorithm validation, channel estimation and noise reduction required for reliable prediction, and design and analysis of adaptive transmission methods aided by fading prediction algorithms. We use these criteria in the review of recent advances in the area of fading channel prediction. We also demonstrate that reliable fading prediction makes adaptive transmission feasible in diverse wireless communication systems.

Adaptive orthogonal frequency division multiplexing (AOFDM) modulation is a promising technique for achieving high data rates required for wireless multimedia services. To accomplish efficient adaptive channel loading, the channel state information (CSI) needs to be fed back to the transmitter. Since the fading channel varies rapidly for fast vehicle speeds, long range fading prediction (LRP) is required for mobile radio AOFDM to insure reliable adaptation. We use past channel observations to predict future CST and perform adaptive bit and power allocation for the OFDM system. We derive the minimum mean-square-error (MMSE) long-range channel prediction that utilizes the time and frequency domain correlation functions of the Rayleigh fading channel. Since the channel statistics are usually unknown, robust prediction methods that do not require the knowledge of the correlation functions are developed. Statistical model of the prediction error is created and used in the design of reliable adaptive modulation. In addition, several methods that significantly reduce the feedback load for mobile radio AOFDM systems are developed and compared. We use a standard sum-of-sinusoids model and our realistic physical model to validate performance of proposed methods. Simulation results demonstrate reliable performance and robustness of the proposed techniques, thus validating feasibility of AOFDM for rapidly varying mobile radio channels

Transmitter diversity in the downlink of code-division multiple-access (CDMA) systems achieves similar performance gains to the mobile-station receiver diversity without the complexity of a mobile-station receiver antenna array. Pre-RAKE precoding at the transmitter can be employed to achieve the multipath diversity without the need of the RAKE receiver at the mobile station. We examine feasibility of several transmitter diversity techniques and precoding for the third-generation wideband CDMA (WCDMA) systems. In particular, selective transmit diversity, transmit adaptive array and space-time pre-RAKE (STPR) techniques are compared. It is demonstrated that the STPR method is the optimal method to combine antenna diversity and temporal precoding. This method achieves the gain of maximum ratio combining of all space and frequency diversity branches when perfect channel state information is available at the transmitter. We employ the long range fading prediction algorithm to enable transmitter diversity techniques for rapidly time varying multipath fading channels.

We propose a transmitter (Tx)-based nonlinear decorrelating interference cancellation method, Tomlinson-Harashima precoding (THP), to combat multiple access interference (MAI) for the downlink of direct sequence code division multiple access (DS-CDMA) systems in multipath fading channels. Since diversity combining and MAI cancellation are employed in the transmitter, the mobile user receivers remain as simple as in single-path single user channels. Two THP designs, preRake THP and multipath decorrelating THP (MDTHP), are derived and compared with previously investigated decorrelating techniques. It is shown that the proposed methods improve upon linear precoding and detection techniques and on the decision-feedback detectors for multipath CDMA channels, while retaining low complexity. Moreover, the MDTHP precoder is attractive for rapidly varying mobile radio channels since its filters do not need to be updated as fading coefficients vary.

Adaptive transmission techniques, such as adaptive modulation and coding, adaptive power control, adaptive transmitter antenna diversity, etc., generally require precise channel estimation and feedback of channel state information (CSI). For fast vehicle speeds, reliable adaptive transmission also requires long-range prediction of future CSI, since the channel conditions are rapidly time variant. In this paper, we propose using past channel observations of one carrier to predict future CSI and perform adaptive modulation without feedback for another correlated carrier. We derive the minimum mean-square error (MMSE) long-range channel prediction that uses the time- and frequency-domain correlation function of the Rayleigh fading channel. An adaptive MMSE prediction method is also proposed. A statistical model of the prediction error that depends on the frequency and time correlation is developed and is used in the design of reliable adaptive modulation methods. We use a standard stationary fading channel model (Jakes model) and a novel physical channel model to test our algorithm. Significant gains relative to nonadaptive techniques are demonstrated for sufficiently correlated channels and realistic prediction range.

Modified branch metrics are proposed for single-user Viterbi decoders in two-stage detectors for convolutionally-encoded code-division multiple-access (CDMA) systems with random spreading sequences. The modifications are based on modeling the residual multiple-access interference (RMAI) after subtractive interference cancellation as conditionally Gaussian with time-dependent variance, where the conditioning is on the time-varying user crosscorrelations. A novel estimate of the variance of the total RMAI is presented, and used in the proposed branch metrics. Significant performance gains are demonstrated over the Euclidean branch metric of the standard Viterbi decoder.

Adaptive transmission techniques, such as adaptive modulation and coding, adaptive power control, adaptive transmitter antenna diversity, etc., generally require precise channel estimation and channel state information (CSI) feedback. For fast vehicle speeds, reliable adaptive transmission also requires prediction of future CSI since the channel conditions are rapidly time-variant. We propose to use past channel observations of one carrier to predict future CSI and perform adaptive modulation without feedback for another correlated carrier. A statistical model of the prediction error that depends on the frequency and time correlation is developed and is used in the design of reliable adaptive modulation methods. Significant gains relative to non-adaptive techniques are demonstrated for sufficiently correlated channels and realistic prediction range.

Development of novel signal processing and communication techniques for 3G wireless systems is motivated by high data rate service requirements. These techniques include adaptive modulation and transmitter antenna diversity. In rapidly time variant channels, these methods need the knowledge of future fading conditions. Thus, they require accurate long range fading prediction. We investigate three combined adaptive modulation and transmitter diversity schemes in conjunction with our previously proposed long range channel prediction (LRP) algorithm. It is demonstrated that the novel combined schemes can achieve higher data rates than the conventional adaptive modulation methods when aided by the LRP.

We compare performance of several multiuser detectors for differentially encoded data combined with simple, disjoint, decision-directed Kalman channel estimators over flat Rayleigh fading channels. Simpler detectors with noncoherent differential detection are also compared. Different performance trends relative to the case of perfect channel estimation are observed. We find that in the presence of channel mismatch, the linear decorrelator is the most robust detector in terms of the bit-error rate and the near-far resistance. Parameter adjustment for fading channel modeling and estimation in the decision-directed mode are also discussed.

Previously, the authors introduced a novel algorithm for long range prediction of flat fading channels that can reliably predict future fading coefficients far beyond the coherence time. This prediction capability provides enabling technology for power and bandwidth efficient adaptive modulation and coding methods. In this paper, we extend our results to the direct sequence code division multiple access (DS/CDMA) channels. It is demonstrated that frequency selective channels encountered in CDMA can also be predicted far ahead, and that the proposed prediction methods can be combined with RAKE receivers. In addition, we utilize prediction to improve performance of the selective transmitter diversity system proposed for the third generation CDMA.

This paper investigates how closely randomly generated binary source sequences can be matched by convolutional code codewords. What distinguishes it from prior work is that a randomly chosen subsequence with density /spl lambda/ is to be matched as closely as possible. The so-called marked bits of the subsequence could indicate overload quantization points for a source sample generated from the tails of a probability distribution. They might also indicate bits where the initial estimate is considered reliable, as might happen in iterated decoding. The capacity of a convolutional code to interpolate the marked subsequence might be viewed as a measure of its ability to handle overload distortion. We analyze this capacity using a Markov chain whose states are sets of subsets of trellis vertices of the convolutional code. We investigate the effect of memory on the probability of perfect interpolation and calculate the residual rate on the unmarked bits of the binary source sequence. We relate our interpolation methodology to sequence-based methods of quantization and use it to analyze the performance of convolutional codes on the pure erasure channel.

Code division multiple access (CDMA) systems have become a serious contender in the technology market for wireless communications systems. Conventional or single-user detection so far has been preferred in CDMA receivers despite many shortcomings due to its simplicity. Multiuser detectors on the other hand can offer dramatically higher capacity at the expense of some increase in complexity. We consider the performance improvements afforded by multiuser detection of coded CDMA systems. We focus on two-stage detection schemes and particularly the two-stage detector with a conventional first stage and show that it is a robust detector under different operating conditions. We then describe an alternative receiver structure for two-stage detectors in coded systems, which when combined with an independent user interleaving scheme gives significant performance improvements.

It is important to identify simple and reliable interference rejection methods for code-division multiple-access (CDMA) channels, since the conventional matched filter receiver has high error rates, and the optimal detector is too complex. We introduce decision-feedback and partial feedback detectors for asynchronous CDMA channels. Two-stage detectors with decision-feedback in the second stage are also studied. The derivation of the decision-feedback detector is based on spectral factorization which leads to a white-noise channel model. We also describe two implementations of the maximum-likelihood detector for this model. Comparisons among the proposed detectors, the conventional detector, and the linear decorrelating detector are undertaken for several asynchronous CDMA channels. In these examples, high bandwidth efficiency systems are explored, and user energies are varied form being similar to being very different. We find that decision-feedback detectors compare favorably with more complex two-stage methods and maintain good performance under diverse channel conditions.>

A decorrelating decision-feedback detector (DF) for synchronous code-division multiple-access (CDMA) that uses decisions of the stronger users when forming decisions for the weaker ones is described. The complexity of the DF is linear in the number of users, and it requires only one decision per user. It is shown that performance gains with respect to the linear decorrelating detector are more significant for relatively weak users and that the error probability of the weakest user approaches the single-user bound as interferers grow stronger. The error rate of the DF is compared to those of the decorrelator and the two-stage detector.>

The author studies minimum mean square error (MMSE) linear and decision feedback (DF) equalisers for multiple input/multiple output (MIMO) communication systems with intersymbol interference (ISI) and wide-sense stationary (WSS) inputs. To derive these equalizers, one works in the D-transform domain and uses prediction theory results. Partial-response MMSE equalizers are also found. As an application, the author considers a pulse amplitude modulation (PAM) communication system with ISI and cyclostationary inputs. The MMSE linear and DF equalizers are determined by studying an equivalent MIMO system. The resulting filters are expressed in compact matrix notation and are time-invariant, whereas the corresponding single input/single output filters are periodically time-invariant. The author also considers MMSE equalizers for a wide-sense stationary process by introducing a 'random phase'. To aid in the performance evaluation of various equalizers, the author derives their mean square errors.>

The authors consider the model for runlength coded systems of P.H. Siegel (1982) that is based on peak or edge detection. The model does not attempt to account for failures of the qualifying circuit; it is assumed that every transition is successfully declared (i.e., no missing or false qualifiers). In this model it is the error in the estimate of the location of the transition that introduces uncertainty at the channel output. The model is motivated by the problem of pulse location in white Gaussian noise. Through a series of lemmas and theorems, bounds on the capacity of this model are obtained. These bounds are evaluated and used to suggest that improvements in storage capacity are possible through the use of codes with designed noise tolerance.>

The authors delayed-decision-feedback sequence estimation (DDFSE) for uncoded PAM signals is considered. Estimates on the performance of the algorithm are given, and simulation results are provided for several examples. A more general form of DDFSE applicable to coded modulation systems is also presented. As an example, detection of trellis-coded QPSK (quadrature phase-shift keyed) signals over intersymbol interference channels is discussed.>