A novel cross-layer optimization (CLO) scheme to significantly enhance the spectral efficiency (SE) of a secondary user (SU) transmission in an underlay cognitive radio system (UCRS) is proposed. The scheme jointly optimizes the physical layer (PHY) and data link control layer (DLC) of the SU transmission. At the PHY, a joint bit and power loading (JBPL) with coding adaptation (CA) using rate-matched low-density-parity-check (LDPC) coding is employed. A truncated hybrid automatic repeat request (THARQ) using chase combining (CC), known as ARQ type II, is implemented at the DLC. An analytic lower bound on the SE for coded transmission is derived and maximized subject to different quality-of-service (QoS) and stochastic chance-based constraints as well as third-party colored Gaussian interference at the SU receiver. The system performance is compared in terms of the number of required retransmissions and the achievable SU SE for different code rates. A significant improvement in system performance is observed by using CC for the SU transmission.

Co-operative automated driving (CAD) use cases involve group-based vehicle-to-vehicle (V2V) communications with a wide range of quality-of-service (QoS) requirements. This work introduces and exploits fifth generation mobile networks (5G) functional architecture support for vehicle-to-everything (V2X) applications to address V2V sidelink radio resource management (RRM) for CAD use cases. A QoS requirement-aware sidelink resource allocation optimization problem is formulated for multicast group V2V communications with reliability constraints and half-duplex limitation. Furthermore, the problem is analyzed for cloud-based sidelink RRM and a dynamic vehicular environment. Accounting for the challenges in acquiring channel state information (CSI), a low-complexity scheduling scheme is presented that makes use of slowly varying large-scale channel parameters (e.g. path loss). Simulation results show significant gains in terms of packet delay performance while meeting the reliability requirements on V2V links.

Co-operative automated driving (CAD) is a key fifth generation mobile networks (5G) use case in which autonomous vehicles communicate over vehicle-to-vehicle (V2V) links requiring a wide range of rate-reliability-delay performance. One key 5G enabler for CAD sidelink radio resource management (RRM) in a multi-operator environment is the virtualization of RRM at the cloud server. This, however, is challenging due to an increase in control plane delay, signaling overhead and complexity. This paper introduces an edge cloud-enabled end-to-end vehicle-to-everything (V2X) architecture to support sidelink RRM in CAD scenarios. Analyzing the problem of a cloud-based sidelink resource allocation for CAD, a utility-based multi-objective optimization problem is described and is translated to three tasks: 1) a vehicle cluster formation as a solution to the clique partitioning problem ensuring vehicle reachability on the control plane, 2) an inter-cluster resource block pool (RB-pool) allocation as a solution to a max-min fairness problem and 3) an intra-cluster resource allocation. The proposed solution framework aims to achieve high modularity, low complexity and decouples cluster formation and RB-pool assignment from the intra-cluster optimum resource allocation, which may be performed on different time scales at different edge cloud entities. Simulation results in a realistic vehicular deployment show significant gains in terms of sidelink throughput and delay while maintaining high link quality.

Co-operative automated driving (CAD) is a key 5th generation mobile networks (5G) use case with stringent quality of service (QoS) requirements. The current autonomous resource selection mode of vehicle-to-vehicle (V2V) communications is susceptible to the hidden node problem and half- duplex limitations and has fixed-size transmission sub-channels which limit its ability to meet QoS requirements. Therefore, in order to increase the reliability, this paper proposes a communication session establishment for CAD operation where the session members jointly select and reserve a pool of favorable radio resources for intra-session communications. Furthermore, an inter-session coordination by means of cluster formation is proposed which enables resource pool sharing among sessions to allow for QoS aware resource allocation. Simulation results show that the proposed scheme significantly outperforms LTE-V Mode-4 in terms of sidelink quality and packet delivery ratio (PDR).

Co-operative automated driving (CAD) is a key fifth generation mobile networks (5G) use case having a wide range of rate-reliability-delay requirements on vehicle-to-vehicle (V2V) communications. One key 5G enabler for the management and optimization of sidelink resources in a multi-operator environment is the virtualization of radio resource management (RRM) at the cloud server. This, however, is challenging due to an increase in control plane delay, signaling overhead and complexity limiting the possibility to exploit the benefits of the virtualization of sidelink RRM. This paper analyzes the problem of cloud-based sidelink resource allocation for multicast group transmissions and describes the multi-objective optimization problem being translated to three tasks: 1) a vehicle cluster formation for ensuring optimal vehicle reachability in the control plane, 2) an inter-cluster resource block pool (RB-pool) allocation and 3) an intra-cluster resource allocation. A graph-based solution framework is proposed aiming at achieving high modularity, low complexity and decoupling of cluster formation and RB-pool assignment from the intra-cluster optimum resource allocation, which may be performed on different time scales at different entities. Simulation results in a realistic vehicular deployment show significant gains in terms of sidelink throughput and delay while maintaining high link quality.

This paper presents an adaptive zone configuration scheme for vehicle-to-vehicle (V2V) communications in terms of resource block pool (RB-pool) and power allocation. A cloud-assisted graph-based dynamic zone configuration framework is proposed for efficient resource utilization which can be well applicable to distributed V2V scenarios. Heuristic solutions are presented by means of vertex clique covering (VCC) and vertex coloring for RB-pool allocation considering the resource demands of each zone to avoid the hidden node problem. The presented scheme is evaluated for LTE-V Mode-4 using system level simulations for an intersection movement assist (IMA) use case. Results show that the dynamic zone configuration approach significantly outperforms both existing zone-less and static zone configurations in terms of packet reception rate (PRR) for high vehicular densities.

A novel scheme for optimum joint bit and power loading for link adaptation in discrete Fourier transform modulated filter bank based transceivers for underlay cognitive radio is proposed aiming to minimize the secondary user average bit-error rate for bit-interleaved coded modulation and to increase the spectral efficiency. The proposed scheme is subject to a stochastic chance-based constraint as well as a colored Gaussian interference caused by a third-party interference. The performance analysis shows that a bit-error rate saturation of schemes employing exclusive power or bit loading can be avoided over a large range of the average expected bit-energy-to- interference-plus-noise ratio which is mandatory for improving the spectral efficiency of cognitive radio systems.

A novel adaptive transmission scheme for discrete Fourier transform modulated filter bank based transceivers for underlay cognitive radio is proposed aiming to minimize the secondary user average bit-error rate (BER) for bit-interleaved coded modulation subject to a stochastic chance-based interference constraint. For a given confidence level, exact expressions for the BER are derived based on assumed perfect channel state information on conditionally Gaussian channel coefficients. Simulations are used to evaluate the BER performance. It is shown that the achievable performance gains as compared to a non-adaptive scheme can be substantial depending on the channel statistics and interference levels.

In this paper, a novel time-frequency spectrum occupancy prediction scheme in a cognitive radio network is presented which is based on a continuous hidden Markov model (CHMM) as opposed to the conventional discrete hidden Markov model (DHMM) based approaches. The primary user signal observations at a secondary user receiver equipped with an analysis filter bank for spectrum sensing are assumed to be embedded in non-Gaussian co-channel interference. The prediction scheme is carried out in three phases comprising a pilot phase, a learning phase and a prediction phase. The performance of the CHMM based scheme is investigated in different occupancy traffic scenarios and it is shown that for a sufficient length of the learning phase, the prediction can overcome the performance limitation of its DHMM counterpart resulting from quantization of observations at the receiver.

In this paper, a recently proposed sensing scheme for cognitive radio systems based on an estimate of the differential entropy is extended to achieve reliable sensing performance in the presence of severe fading by cooperation among J active receivers. The cooperating users being equipped with multiple antennas send their individual sensing decisions to a fusion center which in turn applies different combining techniques for the final joint decision. To assess the performance of the proposed algorithm, we consider a wireless channel with Nakagami-m fading. It turns out that the extended sensing approach leads to a substantial improvement as compared to the non-cooperative case in corresponding fading channels. The performance of the proposed algorithm is studied for different fading levels and the kth nearest neighbor differential entropy estimator in combination with maximum-ratio combining is identified as a scheme which shows a good error performance and can be applied in a variety of fading characteristics.

A realistic Recursive Spatial Multiplexing (RSM) scheme for the case of a wireless feedback channel with limited capacity is considered where the identifier for the critical subspaces are encoded using either Grassmannian or vector quantizers. Optimum and suboptimum encoding schemes are proposed and analyzed. The threshold for the eigenvalues of the channel matrix required in the receiver for determining the critical subspaces is chosen based on optimizing channel capacity and uncoded biterror rates within the inner transceiver. It is shown that the limited feedback channel capacity limits the RSM performance, critically determines the achievable bit-error rates and defines the range of signal-to-noise ratio values where RSM outperforms open-loop multiple-input multiple-output transmission schemes.

In this paper, Cognitive Radio sensing Based on the joint distribution of pseudo WIshart matrix Eigenvalues (CRAB-WISE) is extended to include Wishart matrix based sensing to improve the performance of detecting a primary user signal in an interweave CR scenario. The resulting filter-bank based scheme is applied in a cooperative sensing approach where J users transmit their individual sensing decisions to a fusion center applying different combining methods for the joint decision. The performance of the scheme is evaluated for different system parameter values including wireless channels with Nakagami-m fading. It turns out that the extended sensing approach outperforms CRABWISE with and without cooperation. Furthermore, cooperation leads to a substantial improvement as compared to the non-cooperative case in the aforementioned fading channels.

We consider a simple closed-loop spatial multiplexing architecture which can be incorporated into an outer system with off-the-shelf single-input single-output encoders/decoders. Instead of relying on transmitter-side channel state information for matching the layers of the multidimensional signal to the eigenmodes of the multiple-input multiple-output channel through a precoding, the eigenmodes with high attenuation are identified in the receiver and a retransmission of the affected signal parts requested through a feedback channel. The requested backup facilitates a linear array signal reconstruction subject to limited noise amplification. Since the backup is always embedded in a subsequent signal frame, the multiplexing becomes a recursive procedure. We propose appropriate transmit antenna selection policies for limiting the number of recursions and thereby the memory requirement and incurred delay. Optimal policies are obtained using the theory of Markov decision processes. Moreover, the use of a simple unsupervised learning method lets the system adapt to unknown channel conditions while always guaranteeing a target signal-to-noise ratio at the decoder input.

We consider the uplink of a wireless sensor network with single-antenna sensors, an access point (AP) equipped with multiple antennas, and a common radio channel. To reduce the number of outages resulting from the noise amplification by the zero-forcing (ZF) within the successive interference cancellation (SIC) procedure, the AP is given the possibility to request a selected number of sensors to retransmit their signals in a subsequent time slot. If necessary the AP suspends the ZF-SIC until the requested signals have been retransmitted, making the signal recovery a recursive procedure. The number of sensors required to retransmit depends on the order of the processed sensor signals within the ZF-SIC. We propose two algorithms for determining the decoding order: an algorithm which finds a decoding order necessitating a minimal number of retransmissions, and a suboptimal alternative algorithm with significantly lower complexity. By numerical means the performance and complexities of the considered algorithms are analyzed.

We propose a closed-loop multiple-input multiple-output (MIMO) architecture which incorporates unitary precoding and recursive spatial multiplexing. Both techniques take advantage of a feedback channel in a similar way for the transmission of data at high rates with limited signal processing complexity. Unitary precoding is effective in situations with accurate channel state information (CSI) at the transmitter end. In situations with outdated CSI due to time-variant channels, a linear signal reconstruction at the receiver end may result in severe noise amplification. Recursive multiplexing mitigates the noise enhancement by means of retransmissions of signal parts in critical subspaces. This significantly increases the ergodic capacity achieved by precoding based on outdated CSI, as shown for the cases of time-variant (4x4) and (8x8) MIMO channels.

Multiple-antenna concepts for wireless communication systems promise high spectral efficiency and low error rates by proper exploitation of the randomness in multipath propagation. In this paper, we investigate the impact of channel uncertainty caused by channel estimation errors on the error rate performance. We consider a training-based multiple-antenna system that reserves a portion of time to sound the channel. Training symbols are used to estimate the channel by means of an arbitrary linear filter at the receiver. No channel state information (CSI) is assumed at the transmitter. We present a new framework to analyze training-based multiple-antenna systems by introducing an equivalent system model that specifies the channel by the estimated (and hence, known) channel coefficients and an uncorrelated, data-dependent, multiplicative noise. We derive the maximum-likelihood (ML) detector and highlight its behavior in the limiting cases of perfect CSI and no CSI, and its relation to several mismatched detectors. We deduce new exact expressions and Chernoff bounds of the pairwise error probability (PEP) used to assess word-error and bit-error rate bounds for ML and mismatched detection. Finally, we review the code design guidelines in terms of the deleterious effect of channel uncertainty for coherent and noncoherent signaling schemes, and present numerical results.

Multiple-antenna concepts for wireless communication systems promise high spectral efficiencies and improved error rate performance by proper exploitation of the randomness in multipath propagation. In this paper, we investigate the impact of channel uncertainty caused by channel estimation errors on the capacity of Rayleigh and Ricean block-fading channels. We consider a training-based multiple-antenna system that reserves a portion of time to sound the channel. The training symbols are used to estimate the channel state information (CSI) at the receiver by means of an arbitrary linear estimation filter. No CSI is assumed at the transmitter. Our analysis is based on an equivalent system model for training-based multiple-antenna systems which specifies the channel by the estimated (and hence, known) channel coefficients and an uncorrelated, data-dependent, multiplicative noise. This model includes the special cases of perfect CSI and no CSI. We present new upper and lower bounds on the maximum instantaneous mutual information to compute ergodic and outage capacities, and extend previous results to arbitrary (and possibly mismatched) linear channel estimators and to correlated Ricean fading. Several numerical results for single- and multiple-antenna systems with estimated CSI are included as illustration.

Recent results on the throughput achievable with wireless networks have not fully considered multiple antennas and multi-user links. We introduce these topics by giving the transport capacity of the multiple-antenna multiple-access and broadcast channels. We use these topologies at the physical layer of an ad-hoc network to obtain achievable distance-weighted rates for a multi-antenna wireless network. These values are obtained by maximizing the distance-weighted rate over all combinations of point-to-point, uplink, and downlink topologies, respectively. A distributed algorithm which seeks a Nash equilibrium is used to optimize the transmit covariance matrices for the centralized topology search. Numerical examples with a uniform per-node power constraint show the benefit of uplink topologies over downlink and point-to-point topologies, especially at high transmit power, high numbers of antennas, and a large number of nodes

Multiple-antenna systems have been shown to achieve very high spectral efficiencies. In this paper, we derive simple single-integral expressions for the ergodic and outage capacity of a diversity system in correlated Ricean fading channels, where the channel coefficients are assumed to be known to the receiver only. For illustration purpose, we present numerical results showing the effect of channel correlation, Ricean components, angular spread and multipath components in an orthogonal frequency-division multiplexing (OFDM) system

We derive a power loading procedure optimizing the bit-error rates of multiple-input single-output (MISO) bit-interleaved coded modulation (BICM) orthogonal frequency-division multiplexing (OFDM) systems performing hard-decisions at the receiver and ideal interleaving. The adaptive subcarrier power allocation is based on either perfect or outdated channel state information at the transmitter. The scheme has the same complexity as the one for the single transmit antenna case. For a standard BICM-OFDM system in Rayleigh fading, Monte Carlo simulations show that the relative signal-to-noise ratio gain by the adaptation is up to 4 dB at an average bit-error rate level of 10-66. The relative gain in MISO systems decreases to 1.05 dB for increasing the number of transmit antennas. The achievable gain decreases for decreasing cross-correlation of the outdated and the actual channel state

Multiple-antenna concepts for wireless communication systems promise high spectral efficiencies by proper exploitation of the randomness in multipath propagation. In this paper, we investigate the impact of channel uncertainty caused by channel estimation errors on the capacity of Rayleigh and Ricean block-fading channels. We consider a training-based multiple-antenna system that reserves a portion of time to sound the channel. The training symbols are used to estimate the channel state information (CSI) at the receiver by means of an arbitrary linear estimation filter. No CSI is assumed at the transmitter. We present an equivalent system model for training-based multiple-antenna systems, which specifies the channel by the estimated (and hence, known) channel coefficients and an uncorrelated, data-dependent noise. Based on this equivalent model, which includes the special cases of perfect CSI and no CSI, we derive upper and lower bounds on the maximum instantaneous mutual information and the ergodic capacity, and extend previous results to arbitrary (and possibly mismatched) linear channel estimators and to correlated Ricean fading

Novel and simple concepts for adaptive block-equalizers to be used in the downlink of the Universal Mobile Telecommunication System (UMTS) terrestrial radio access (UTRA) with frequency-division duplex (FDD) are presented. The adaptation of the block-equalizers using modified least-squares (LS) or minimum mean-square error (MMSE) schemes is based on the continuously transmitted, code-multiplexed common pilot channel (CPICH). In contrast to classical adaptive filter approaches, the number of coefficients to be adapted corresponds to the number of propagation channel paths and is thus independent of the chosen block size. In time-varying multipath propagation scenarios, a remarkable performance gain in terms of uncoded bit-error rate is obtained with respect to block-equalizers constructed using conventionally estimated channel impulse responses.

The performance of a discrete Kalman filter used as a chip-level channel equalizer in the downlink of the universal mobile telecommunication system (UMTS) with frequency-division duplex (FDD-UMTS) is analyzed in terms of chip estimation error and bit-error probability. The dependence of the chip estimation error on the signal-to-noise ratio, the transmission channel characteristics, and the filter length is derived and discussed for a two-path frequency-selective static channel. It is shown by simulation that the result for the chip error is also valid for two-path frequency-selective fading channels. A semianalytical approach is utilized to derive the bit-error probability of the Kalman equalizer, and the expression is verified by simulations in a fast-fading FDD-UMTS environment.

The matched-filter bound (MFB) for continuously dispersive Rayleigh fading channels with receive diversity is derived. The treatment is quite general, as the case of discrete multipath channels with correlated fading is included in our model as a special case, and we impose no constraints with regard to the selectivity of the channel. It is assumed that a statistical characterization of the channel is available through its direction-delay-Doppler power spectral density. The Karhunen-Loe/spl grave/ve expansion for vector processes is used to describe the received signal. The MFB is analyzed with respect to several parameters characterizing the mobile radio channel, the waveform, and the antenna configuration.

We derive power adaptation strategies optimizing the error rates of broadband bit-interleaved coded OFDM systems performing hard-decisions after the demodulation and ideal interleaving. The adaptive subchannel power allocation is based on either perfect or outdated channel state information in a time-division duplex transmission. We find that for an average bit error rate level of 10/sup -6/ a gain of up to 4 dB can be achieved by the proposed adaptation in Rayleigh fading channels.

We consider multicarrier transmission schemes in which the elementary signal pulses relate to the elements of a Weyl-Heisenberg system, i.e., resulting from a prototype function shifted in time and frequency. The overlapping of the information-bearing signal parts at the output of doubly dispersive channels and the resulting interference are confined by utilizing a prototype function whose energy is concentrated in both time and frequency. We derive a symbol detector which first calculates a sufficient statistic for the unknown data symbols from the linearly combined output signals of a filter bank, and second, performs an iterative maximization of the likelihood function. The presented receiver takes full advantage of the confined pulse overlapping to limit the computational effort. An analysis of the computational complexity and bit-error rate performance of the iterative detection scheme is provided for wide-sense stationary uncorrelated scattering channels.

This study investigates the application potential of the SAGE (space-alternating generalized expectation-maximization) algorithm to jointly estimate the relative delay, incidence azimuth, Doppler frequency, and complex amplitude of impinging waves in mobile radio environments. The performance, i.e., high-resolution ability, accuracy, and convergence rate of the scheme, is assessed in synthetic and real macro- and pico-cellular channels. The results indicate that the scheme overcomes the resolution limitation inherent to classical techniques like the Fourier or beam-forming methods. In particular, it is shown that waves which exhibit an arbitrarily small difference in azimuth can be easily separated as long as their delays or Doppler frequencies differ by a fraction of the intrinsic resolution of the measurement equipment. Two waves are claimed to be separated when the mean-squared estimation errors (MSEEs) of the estimates of their parameters are close to the corresponding Cramer-Rao lower bounds (CRLBs) derived in a scenario where only a single wave is impinging. The adverb easily means that the MSEEs rapidly approach the CLRBs, i.e., within less than 20 iteration cycles. Convergence of the log-likelihood sequence is achieved after approximately ten iteration cycles when the scheme is applied in real channels. In this use, the estimated dominant waves can be related to a scatterer/reflector in the propagation environment. The investigations demonstrate that the SAGE algorithm is a powerful high-resolution tool that can be successfully applied for parameter extraction from extensive channel measurement data, especially for the purpose of channel modeling.

A demodulation algorithm for a base station receiver in a direct sequence (DS) spread spectrum code division multiple access (CDMA) communication system is proposed which performs joint multiuser detection and estimation using the output signals of an antenna array in the absence of a tight power control. The scheme is a combination of a multistage (MS) detector for data recovery and a space-alternating generalized expectation maximization (SAGE) algorithm for channel parameter estimation. Monte-Carlo simulations show that the scheme is able to overcome the near-far problem while simultaneously combining the signal energy spread over time and space in a nearly optimum fashion.

A fluid dynamic system can be described by nonlinear partial differential equations (PDEs). Well suited for simulation purposes is a discrete passive dynamic system, which can be found using principles known from the theory of multidimensional (MD) wave digital filters (WDF). All major features of the original physical system (e.g. massive parallelism, local interdependencies, MD-passivity etc.) are transferred to the numerical integration of the PDEs. The present paper shows the application of the algorithm to the so-called Euler equations. It starts from the PDEs governing the system and finally shows experimental results from computer simulations.>