Parallelism has become a central concern in modern decoding frameworks aiming to meet stringent throughput and latency requirements. Guessing Random Additive Noise Decoding (GRAND) is a recently proposed decoding paradigm that tests candidate Error Patterns (EPs) until a valid codeword is found. Among its variants, Soft GRAND (SGRAND) achieves maximum-likelihood (ML) decoding but relies on real-time generation and likelihood ordering of EPs, making parallel execution nontrivial under the ML optimality constraint. In this work, we introduce a unified binary tree representation of EPs, termed the EP tree, which formalizes the hierarchical structure underlying SGRAND and Ordered Reliability Bits (ORB) GRAND algorithms, enabling structured organization of EPs and algorithmic-level parallel exploration. Building upon this unified framework, we propose a parallel design of SGRAND that preserves ML optimality while significantly reducing decoding complexity through pruning strategies and tree-based computation. Furthermore, we develop an enhanced ORBGRAND algorithm based on the same EP tree representation, improving decoding performance toward ML while retaining parallel efficiency. Numerical experiments show that the proposed parallel SGRAND achieves a 3.96× reduction in decoding latency compared with its serial counterpart, while the enhanced ORBGRAND achieves a 4.21× speedup, demonstrating the effectiveness of the unified tree-based framework and its strong potential for future algorithmic and hardware optimizations.

Radio frequency (RF)-based imaging plays a pivotal role in sensing applications due to its robustness against visibility constraints and environmental factors. Among various RF imaging technologies, impulse radio ultra-wideband (IR-UWB) stands out by offering both fine temporal resolution and penetration capabilities. Existing work is limited to planar or depth imaging with multipath suppression. However, achieving depth imaging exploiting multipath IR-UWB signals and further realizing penetration imaging remains a challenge. To address it, this paper proposes a novel neural network architecture that uses IR-UWB signals to reconstruct depth and penetration images. First, we give a feasibility analysis of the dual imaging tasks using IR-UWB signals in an enclosed environment with multipath effects. Then, a theoretical spatial geometric inverse model is presented to formulate depth and penetration imaging as a joint optimization problem. Next, to address the ill-posed and intractable nature of the optimization problem, we propose a neural network architecture named the attention-enhanced depth and penetration imaging network (AEDPI-Net) as an approximate solver. AEDPI-Net employs a shared encoder to extract common features from IR-UWB signals, which are subsequently mapped by two parallel decoders into depth and penetration images. Finally, extensive experimental studies on an IR-UWB imaging platform verify the effectiveness of AEDPI-Net. The results reveal that AEDPI-Net achieves superior depth and penetration imaging performance compared to the baseline methods.

High-accuracy indoor localization is a key enabler of ubiquitous location-based services (LBSs) in the Internet of Things (IoT), with applications in mobile robots, asset tracking, and beyond. For indoor localization, it has been reported that the methods based on probabilistic models have high localization accuracy and strong generalization in the presence of nonline-of-sight (NLOS) conditions and multipath effects. To further leverage such advantages, this article proposes two fusion localization methods based on Bayesian filters which fuse an inertial measurement unit (IMU) motion model with a probabilistic model constructed by soft information (SI) framework to enhance localization performance. First, we propose a method based on particle filter (PF) to directly fit the posterior probability density distribution (PDF), called PF-SI. This method reduces accuracy loss caused by linearization and achieves high accuracy. Then, to reduce the high computational complexity of the PF-SI method, we utilize an error state Kalman filter (ESKF) to construct linearized error state transition and error observation equations and update the filter with distance residual, as ESKF-SI. This method has slightly lower localization accuracy but significantly improves computational efficiency. Finally, experimental results in a real indoor scenario based on ultrawideband (UWB) signals are presented. The results show that the two proposed fusion methods can achieve a root mean square localization error of less than 0.25 m in a complex NLOS scenario.

Efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we conduct the rounding error analysis and derive the lower bound of the achievable rate for single-input-multiple-output (SIMO) using maximal ratio combining (MRC) and multiple-input-single-output (MISO) systems using maximal ratio transmission (MRT) with finite-precision arithmetic. Then, considering the multi-user scenario, the rounding error analysis of zero-forcing (ZF) detection and precoding is derived by using the normal equations (NE) method. The corresponding lower bounds of the achievable sum rate are also derived and asymptotic analyses are presented. Built upon insights from these analyses and lower bounds, we propose a mixed-precision architecture for massive MIMO systems to offset performance gaps due to finite-precision arithmetic. The corresponding analysis of rounding errors and computational costs is obtained. Simulation results validate the derived bounds and underscore the superiority of the proposed mixed-precision architecture to the conventional structure.

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods used for precise estimation of channel parameters, the proposed PF-assisted method is utilized to narrow down the search range for the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The theoretical analysis demonstrates that the PF-assisted JCD method can achieve equivalent performance to the Bayesian-optimal DF-based method, while greatly reducing the computational complexity. Simulation results are also presented to validate the analytical results.

The efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we propose a mixed-precision architecture for massive MIMO systems based on blocked matrix computations. Then the corresponding analysis of rounding errors and computational costs is derived. Finally, simulation results underscore the superiority of the proposed mixed-precision architecture to the conventional structure.

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods, the proposed PF-assisted method is utilized to capture the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The simulation results show that the PF-assisted JCD method can achieve near the same performance as the Bayesian-optimal DF-based method, while greatly reducing the computational complexity.

Efficient implementation of Tomlinson-Harashima precoding (THP) is crucial in massive multiple-input-multiple-output (MIMO) systems with a large number of antennas at the base station (BS) serving many user equipments (UEs). To address the high computational complexity of THP, in this paper, we first propose novel THP update algorithms that can avoid recomputing the THP filters when a new UE arrives or departs. Specifically, by using the Gram-Schmidt process and a series of Givens matrices, the THP filters are computed without full matrix operations. Then we extend the THP update algorithms to a more general scenario when multiple multi-antenna UEs arrive or depart. In this case, the proposed algorithms use both direct and iterative approaches. Moreover, the computational complexity of the proposed algorithms is derived and compared with that of the conventional THP. Finally, to further align with the practical scenario, we analyze and derive the approximate close-form expressions for the sum achievable rate of the proposed algorithms under imperfect channel state information (CSI). Simulation results are provided to illustrate the effectiveness of the proposed algorithms. The impact of quasi-static fading and slow time-varying scenarios with imperfect CSI on the communication performance of the proposed algorithms is also evaluated.

Pulse-based integrated sensing and communication (ISAC) systems have the advantages of high ranging resolution and strong resistance to self-interference, compared with continuous wave (CW) based systems. However, for pulse-based ISAC systems, multi-path channels pose various challenges to data recovery and ranging by providing diversity gain for data recovery but incurring the interference to the identification of the first path in ranging. In this paper, we design a pulse-based ISAC receiver for multi-path channels. The designed receiver can obtain the diversity gain by correlating the received signal with the estimated template signal. Meanwhile, it can detect the arrival of the first path by using a threshold detection method based on a constant false alarm rate (CFAR). Furthermore, we extend the pulse-based ISAC design to a low-resolution analog-to-digital converter (ADC) scenario. A low-cost receiver design is provided for the pulse-based ISAC system that can recover data and estimate range simultaneously considering the non-linear effect caused by the low-resolution ADC. Simulation results show that compared with the generalized maximum likelihood (GML) based receiver, the proposed full-resolution pulse-based ISAC receiver has 1dB signal-to-noise ratio (SNR) loss in bit error rate (BER) and almost the same mean squared error (MSE) performance with the significantly reduced computational complexity. Also, compared with the full-resolution ISAC receiver, the ISAC receiver with 3-level quantization incurs only 0.8dB SNR loss in BER and 1dB SNR loss in MSE.

In millimeter wave (mmWave) Multiple-Input Multiple-Output (MIMO) multiuser communication systems, Hybrid BeamForming (HBF) has been widely studied to make a balance between performance and hardware costs. However, the design of HBF requires either full Channel State Information (CSI) or the knowledge of equivalent channel, which brings heavy training and feedback overhead. In the paper, we propose a novel pure Analog BeamForming (ABF) method to reduce the training overhead and suppress the inter-user interference without digital beamforming. More specifically, we split the whole ABF process into two stages: standard beamforming at a special direction and spatial angle modulation. The former is realized by a common amplitude-weighted network shared by all links, while the latter is realized by a phase shifter network. Compared with the existing HBF methods, the proposed two-stage ABF method does not require extra analog hardware components, and the digital beamforming process is removed. The training overhead of channel estimation can also be greatly reduced. Both theoretical analysis and numerical results show that this two-stage ABF method can outperform the HBF methods, especially in the medium and low SNR regime of pilot signals.

MmWave communication is a promising communication technique of future 5G system. Large bandwidth and high directional gain are the two advantages of mmWave communication solutions. High directional gain leads the usage of massive units. The system maintenance and status management play more and more important role in future wireless communication systems and contribute higher operation cost than before. In our paper we proposed a fault finding and location methods based on two deep neural network (DNN) with different complexity, the simple network is designed to fault finding with low cost, the other precision network start to fault location when the former one has detected the faults. Simulation results show that our network can work well at low SNR region without manual inspection involved, thus the operation cost is reduced.

In this paper, we consider a single-cell hybrid millimeter wave (mmWave) massive multi-user multipleinput multiple-output (MU-MIMO) system with in- phase and quadrature-phase imbalance (IQI) at the radio frequency chains. We investigate the impact of IQI on the achievable sum rate of the system. Results show that IQI limits the sum rate to a finite ceiling as the number of antennas at the base station or transmit power goes to infinity, and its impact on performance of the hybrid beamforming system is more obvious than that of the fully digital beamforming system. With the finite resolution phase shifters, the impact of the finite resolution is less severe than that of IQI. Moreover, the sum rate decreases with the number of users in a small value region, and the impact of IQI decreases as well.

The downlink communication of a single‐cell millimetre wave (mmWave) massive multi‐user multiple‐input multiple‐output (MU‐MIMO) system with hybrid beamforming (HBF) is studied. The authors consider the system with in‐phase and quadrature‐phase imbalance (IQI) at the radio‐frequency (RF) chains. Firstly, they provide the HBF designs based on the complex‐valued effective channel and augmented real‐valued equivalent channel, respectively. Then, they study the impact of IQI on the achievable downlink sum rate and derive the approximation of the achievable sum rate on various HBF schemes. Results show that the sum rate of the receiver designed based on real‐valued equivalent channel increases without bound, but the performance of the receiver designed based on complex‐valued presentation exhibits a finite ceiling, as the number of BS antennas increases. Furthermore, both receivers limit to finite ceiling rates due to IQI as the transmit power goes to infinity. Moreover, the HBF system reaches a peak sum rate at a certain value of the number of users, and IQI has an ignorable impact on the value. Meanwhile, the impact of the amplitude imbalance is more significant on the performance degradation of the HBF system than that of the phase imbalance.

Hybrid beamforming(HBF) is widely adopted for power saving and cost reduction in millimetre wave(mmWave) massive MIMO systems. It includes analog beamforming in phase shifted array and digital beamforming in baseband. Phase shifted array is usually made up with a large amount of phase shifters. Due to production process in mmWave, phase shifters are unavoidable to have phase-shifting error and gain error. In the paper we study the influence of imperfect phase shifter upon the performance of the mmWave massive MIMO systems. We derived the closed-form expression of the loss of spectral efficiency in multi-stream point-to-point scenario. Both the theoretical results and simulation results show that there is a spectral efficiency ceiling due to phase-shifting error and gain error. The analytic procedure can be applied for both the sparse channel and the rich-scattering channel.

For greedy block sparse recovery with unknown sparsity level, we theoretically derive a stopping condition to terminate the iteration process and thus to estimate the sparsity level. Focused on the block orthogonal matching pursuit (BOMP) algorithm, we model the energy of residual signals from a probabilistic perspective and we use its distribution to derive a threshold, the iteration process is terminated when the residual energy is below the given threshold. Specifically, a communication scenario of small packet transmission is considered, which uses the interference cancellation based BOMP (ICBOMP) algorithm to recover sparse signal, we apply the stopping condition to terminate the iterations, and thus to estimate the number of active users. Simulation results show that our derived condition can accurately estimate the sparsity level and at the same time guarantees a favorable recovery accuracy, both for the BOMP and ICBOMP recoveries.

Modern mobile terminals produce massive small data packets. For these short-length packets, it is inefficient to follow the current multiple access schemes to allocate transmission resources due to heavy signaling overhead. We propose a many-access scheme that is well suited for the future communication systems equipped with many receive antennas. The system is modeled as having a block-sparsity pattern with unknown sparsity level (i.e., unknown number of transmitted messages). Block precoding is employed at each single-antenna transmitter to enable the simultaneous transmissions of many users. The number of simultaneously served active users is allowed to be even more than the number of receive antennas. Sparsity-aware recovery is designed at the receiver for joint user detection and symbol demodulation. To better recover the transmitted block-sparse signal vector, interference cancellation based block orthogonal matching pursuit (ICBOMP) algorithm is developed upon the known BOMP algorithm for the recovery.Simulation results demonstrate the effectiveness of the proposed scheme in small packet services, as well as the advantages of ICBOMP in improving signal recovery accuracy and reducing computational cost.

For an energy harvesting wireless network (EHWN), power oscillation will occur in uplink signal‐to‐interference‐plus‐noise ratio‐based power control if some energy‐non‐supported nodes exist. Power oscillation will destroy power control algorithm's convergence and influence the system's stabilisation no matter the EHWN is feasible or infeasible. Unfortunately, existing algorithms cannot avoid the power oscillation. Therefore, the authors propose a new distributed algorithm which contains a soft removal mechanism to solve this problem in this study. Some energy harvesting nodes should be removed softly both in terms of their energy state information and channel state information. The convergence of the authors’ proposed algorithm can be guaranteed and power oscillation can be avoided in both feasible and infeasible EHWN. Simulation results verify their analysis and show that their algorithm brings less outage ratio than other algorithms.

Modern mobile terminals produce massive small data packets. For these short-length packets, it is inefficient to follow the current multiple access schemes to allocate transmission resources due to heavy signaling overhead. We propose a non-orthogonal many-access scheme that is well suited for the future communication systems equipped with many receive antennas. The system is modeled as having a block-sparsity pattern with unknown sparsity level (i.e., unknown number of transmitted messages). Block precoding is employed at each single-antenna transmitter to enable the simultaneous transmissions of many users. The number of simultaneously served active users is allowed to be even more than the number of receive antennas. Sparsity-aware recovery is designed at the receiver for joint user detection and symbol demodulation. To reduce the effects of channel fading on signal recovery, normalized block orthogonal matching pursuit (BOMP) algorithm is introduced, and based on its approximate performance analysis, we develop interference cancellation-based BOMP (ICBOMP) algorithm. The ICBOMP performs error correction and detection in each iteration of the normalized BOMP. Simulation results demonstrate the effectiveness of the proposed scheme in small packet services, as well as the advantages of ICBOMP in improving signal recovery accuracy and reducing computational cost.

In this paper, we study the power control problem for energy-harvesting wireless network (EHWN). In an EHWN, if there are some nodes whose energy- harvesting rate is less than its transmit power, power oscillation will be caused even when the system is feasible. In order to avoid the power oscillation, we take energy-harvesting node's energy status, i.e., energy-harvesting rate, transmit power and stored energy, into account and present a distributed power control algorithm. In our algorithm, some energy-harvesting nodes should be soft removed according to their inappropriate channel and energy status. Theoretical analysis, which is verified by simulation results, shows that our algorithm can avoid power oscillation, converge to a unique fixed point and bring less outage in both feasible and infeasible systems.

Massive multiple-input multiple-output (MIMO) has been proposed as a key technology for the future fifth generation (5G) cellular networks. In time division duplex (TDD) massive MIMO systems, pilot contamination caused by channel estimation error is crucial to the system performance. In this paper, we propose a pilot sequences allocation strategy to mitigate the pilot contamination. In this strategy, the pilot sequences sets are identical for center users, but mutually orthogonal for edge users in different cells. With mitigated pilot contamination, we analytically determine the approximate system capacity which is accurate when the number of antennas at the base station tends to infinite. The simulation results show that the proposed pilot sequences allocation strategy achieves higher system capacity than the traditional pilot sequences allocation strategy whose sequences reuse rate is one or three. There also exists an optimal number of pilot sequences in different SNR to maximize the system capacity.

Interference alignment (IA) is a scheme to approach the capacity at high signal-to-noise ratio (SNR) in multiuser multiple-input multiple-output (MIMO) interference networks. To implement the IA scheme in a frequency-division duplexing (FDD) system, transmitter channel state information (CSIT) is fed back from the receiver with finite bits. However, such CSIT is subject to quantization errors and delays of feedback channels. In this paper, we verify that interference leakage is bounded by chordal distance in the MIMO channel. Besides, a joint real Grassmannian quantization strategy is proposed to reduce chordal distance to improve CSIT quality. Meanwhile, under the noise-limited criterion, the lower bound of the codebook size of our proposed strategy is much smaller than that of the conventional complex Grassmannian quantization strategy. Simulations demonstrate that our proposed strategy provides substantial performance gains compared with the conventional strategy.

Interference alignment (IA) is a scheme to achieve degrees of freedom (DOF) of interference network at high signal-to-noise ratio (SNR). In order to implement IA scheme in frequency-division duplexing (FDD) system, receivers feedback channel state information to transmitters. The key problem is to acquire accurate transmitter channel state information (CSIT) in the presence of the quantization error. In this paper, a joint real Grassmannian quantization strategy is proposed to reduce codebook size in single-input single-output (SISO) frequency-selective channel with K user. More concretely, this strategy quantizes the real part and imaginary part of channel vector respectively to reduce the chordal distance. Meanwhile, a noise-limited criterion is assumed that interference leakage is smaller than thermal noise. Under this criterion, the codebook size using the proposed strategy is much smaller than the codebook size using conventional complex Grassmannian quantization strategy. With the same codebook size, simulations show a significant sum rate gain at high SNR compared with the conventional strategy.

Impulse radio ultra wideband (IR-UWB) technique has attracted interest in indoor localization thanks to its sub-nanosecond (ns) narrow pulse feature offering high timing resolution. However, measuring these short pulses demands high performance analog-to-digital converter (ADC), i.e. 4 giga Hz 8-bit ADC, which is hard to implement in real systems. In this paper, a finite-resolution (FR) quantization based localization method is proposed, revealing that high performance ADCs can be replaced with high speed comparators since a 2-bit quantization is good enough. At first we approximate the post-quantization signal as Gaussian distributed using Bussgang theorem, facilitating the following derivation. Then, practical TOA estimation and iterative Taylor-series (TS) localization algorithm are derived. Subsequently the analytical expressions of Cramer-Rao lower bound (CRLB) of proposed scheme is obtained, theoretically quantifying the performance loss caused by FR quantization. Finally, compared with the full-resolution and signal-strength (SS) approaches via simulation, we prove that our finite-resolution algorithm achieves almost the same performance as traditional full-resolution scheme while dramatically decreasing the complexity.

Future communication system requires large bandwidths to achieve high data rates, rendering high-resolution analog-to-digital converter (ADC) a key bottleneck due to its high complexity and large power consumption. In this paper, we consider monobit digital receivers for QPSK modulation. First, the optimal monobit receiver under Nyquist sampling is derived. Its performance is calculated in the form of deflection ratio. Then a suboptimal but low-complexity monobit receiver is obtained. The impact of the phase offset is investigated, and the interface with error-control decoder is given. Numerical simulations show that the low-complexity suboptimal receiver suffers 3dB signal to noise ratio (SNR) loss in AWGN channels and only 1dB SNR loss in multipath channels compared with the matched-filter based monobit receiver with full channel state information (CSI).

Impulse radio ultra-wideband (IR-UWB) technology offers an accurate ranging ability by exploiting the time of arrival (TOA) information of the narrow pulse. However, capturing this sub-nanosecond (ns) width pulse in dense multipath environment requires expensive and power-hungry high performance analog-to-digital converters (ADCs) and complex digital signal processing. To reduce the complexity, a finite resolution (FR) receiver that limits quantization to only a few bits has been proposed recently. In this paper, we develop the Cramer-Rao lower bound (CRLB), as a guidance to evaluate the FR quantization influence on IR-UWB TOA estimation. Firstly we define the quantization efficiency and prove that a 2-bit (4-level) quantization is good enough for TOA estimation because it can reach 88% efficiency as a full resolution method. Subsequently, overlapping coefficient is defined to quantify the performance degradation caused by overlapping between the first path and the following multipaths. Finally, we introduce a sub-optimum but more practical FR quantization scheme whose performance asymptotically converges to optimum as the signal to noise ratio (SNR) decreases. FR quantization scheme is demonstrated to achieve a much higher feasibility while holding an acceptable accuracy loss.

Future communication system requires large bandwidths to achieve high data rates, thus rendering analog-to-digital conversion (ADC) a bottleneck due to its high power consumption. In this paper, we consider monobit receivers for QPSK. The optimal monobit receiver under Nyquist sampling is obtained and its performance is analyzed. Then, a suboptimal but low-complexity receiver is proposed. The effect of imbalances between In-phase (I) and Quadrature (Q) branches is carefully examined. To combat the performance loss due to IQ imbalances, monobit receivers based on double training sequences and eight-sector phase quantization are proposed. Numerical simulations show that the low-complexity suboptimal receiver suffers 3dB signal-to-noise-ratio (SNR) loss in additive white Gaussian noise (AWGN) channels and only 1dB SNR loss in multipath channels compared with matched-filter monobit receiver with perfect channel state information (CSI). It is further demonstrated that the amplitude imbalance has essentially no effect on monobit receivers. In AWGN channels, receivers based on double training sequences can efficiently compensate for the SNR loss without complexity increase, while receivers with eight-sector phase quantization can almost completely eliminate the SNR loss caused by IQ imbalances. In dense multipath channels, the effect of imbalances on monobit receivers is slight.

In this paper, we propose a novel complete two-dimensional shadow fading modeling on system level characterized by both spatial auto-correlation and site-to-site cross-correlation. Numerical results confirm that the proposed algorithm based on two-dimensional filter to generate shadowing auto-correlation is more approaching theoretical results comparing with the previous method, and the empirical cross-correlation coefficient is embedded well in our proposed model. Additionally, the linear interpolation scheme of the proposed model significantly reduces the computational complexity on system level simulation.

The aim of this paper is to propose a fast convergence algorithm of precoder to achieve feasible interference alignment. By limiting the optimization only on the transmitters' side, it relaxes the assumption of channel reciprocity which will alleviate the significant overhead induced by alternating between the forward and reverse communication links. A lower complexity and higher robustness modified steepest descent (SD) algorithm in the complex space is introduced first. Then we reform the optimization problem on the complex Stiefel manifold and derive a novel SD algorithm to achieve perfect interference alignment. Simulation results suggest that comparing with previous methods, the novel SD algorithm on Stiefel manifold has better convergence performance and higher system capacity.

We consider a single-hop interference network withKtransmitters andJreceivers, all havingMantennas. Each transmitter emits an independent message and each receiver requests an arbitrary subset of the messages. This generalizes the well-knownK-userM-antenna interference channel, where each message is requested by a unique receiver. For our setup, we derive the degrees of freedom (DoF) region. The achievability scheme generalizes the interference alignment schemes proposed by Cadambe and Jafar. In particular, we achieve general points in the DoF region by using multiple base vectors and aligning all interferers at a given receiver to the interferer with the largest DoF. As a byproduct, we obtain the DoF region for the original interference channel. We also discuss extensions of our approach where the same region can be achieved by considering a reduced set of interference alignment constraints, thus reducing the time-expansion duration needed. The DoF region for the considered system depends only on a subset of receivers whose demands meet certain characteristics. The geometric shape of the DoF region is also discussed.

In this paper, the effect of finite-level quantization on UWB time-of-arrival (TOA) estimation is investigated. The scheme of optimized quantization threshold combined with the post-quantization processing is derived, which is shown to provide satisfactory gains in the system performance. The TOA estimation errors of several low-resolution sampling approaches are compared via Monte Carlo simulation, where the tri-level quantizer is of particular interest due to its simplicity and capability. We demonstrate that the tri-level sampling receiver, with use of the proposed scheme provides an outstanding performance in TOA estimation with an affordable cost and low complexity.

We consider a single hop interference network with K transmitters, each with an independent message and J receivers, all having the same number (M) of antennas. Each receiver requests an arbitrary subset of the messages. This generalizes the well-known K user M antenna interference channel, where each message is requested by a unique receiver. For this setup, we derive the exact degrees of freedom (DoF) region. Our achievability scheme generalizes the interference alignment scheme proposed by Cadambe and Jafar '08. In particular, we achieve general points in the DoF region by using multiple base vectors and aligning the interference at each receiver to its largest (in the DoF sense) interferer. As a byproduct of our analysis, we recover the DoF region for the original interference channel.

We investigate the Degrees of Freedom (DoF) Region of a cellular network, where the cells can have overlapping areas. Within an overlapping area, the mobile users can access multiple base stations. We consider a case where there are two base stations both equipped with multiple antennas. The mobile stations are all equipped with single antenna and each mobile station can belong to either a single cell or both cells. We completely characterize the DoF region for the uplink channel assuming that global channel state information is available at the transmitters. The achievability scheme is based on interference alignment at the base stations.

The ranging implementation for IEEE 802.15.4a systems usually requires for high sampling rate high resolution ADCs, which are too complicated and power-hungry for realization. Recent methods have been proposed to make tradeoffs, such as limiting amplitude resolution to a few bits, which results in finite-resolution quantization receiver. In this paper, the influence of the low-bit quantizer on UWB time-of-arrival (TOA) estimation is investigated. Likelihood ratio test (LRT) and involved generalized Neyman-Pearson lemma are used to optimize the TOA estimation performance. Several types of the low-bit and full-resolution digital receivers are compared via Monte Carlo simulation. We demonstrate that our scheme provides obvious superiority than the traditional uniform quantizer with little complexity increase. Furthermore, 3-bit receiver is proved good enough since it can approach the performance bound achieved by the full-resolution receiver.

In distributed space–time (DST)-coded regenerative relay networks, demodulation error produced by relays degrades the receiver performance significantly. To mitigate this disadvantage, two threshold-based selective relaying schemes are proposed, that is, centralised selecting scheme and distributed selecting scheme, where each relay forwards signals only if its received signal–noise ratio is larger than a threshold. Both proposed schemes can work well with arbitrary modulation constellation and any number of relays and no matter whether the source–destination channel is available or not. Simulation results show both proposed selective relaying schemes outperform conventional schemes significantly and the improvement increases as the scale of relay network grows. Centralised selecting has a slightly better performance than the distributed selecting. However, the latter has a far lower system cost. This contribution provides two useful relaying mechanisms to mitigate error propagation.

Digital receivers for future high-rate high-bandwidth communication systems will require large sampling rate. This is especially true for ultra-wideband (UWB) communications with impulse radio (IR) modulation. Due to high complexity and large power consumption, multibit high-rate analog-to-digital converter (ADC) is difficult to implement. Monobit receiver has been previously proposed to relax the need for high-rate ADC. In this paper, we derive optimal digital processing architecture for receivers based on monobit ADC with a certain over-sampling rate and the corresponding theoretically achievable performance. A practically appealing suboptimal iterative receiver is also proposed. Iterative decision-directed weight estimation, and small sample removal are distinctive features of the proposed detector. Numerical simulations show that compared with full resolution matched filter based receiver, the proposed low complexity monobit receiver incurs only 2 dB signal to noise ratio (SNR) loss in additive white Gaussian noise (AWGN) and 3.5dB SNR loss in standard UWB fading channels.

In this paper, we study optimum SNR threshold based partial decode-and-forward (PDF) schemes where relays may choose to forward the demodulated symbols in form of distributed space-time coding (DSTC) or to remain silent according to instantaneous link qualities of the source to relays. Considering the possible error of Maximum likelihood decoding at intermediate relays, we model a general noise in the received signal-noise ratio (SNR) expression at the destination. First, we analyze a centralized scheme, called Centralized Selecting, where the relaying decisions are based on all average SNRs of the source-relays and relays-destiantion. The SNR threshold vector are calculated in a central way. Then, a distributed scheme, Distributed Selecting, is proposed to let every relay individually make its decision based on two related average SNRs, i.e., source-ith relay and ith relay-destination. We show that both proposed selective PDF relaying schemes have better performances than traditional partial decode-and-forward scheme, and the centralized selecting is better than distributed selecting. However, distributed selecting approaches the centralized selecting closely with a far lower system cost.

Rate and diversity impose a fundamental tradeoff in wireless communication. We propose a novel distributed space-time coding (DSTC) scheme based on linear constellation precoding (LCP) for Amplify-and-Forward relaying networks. The proposed scheme can achieve full-diversity or full-rate, and also offers a flexibility for a desired rate-diversity tradeoff. This scheme works well with arbitrary signal constellation and any number of relays. Through performance analysis, coding design criteria and decoding strategy are provided. Simulation results show that the proposed coding scheme outperforms diagonal DSTC (DDSTC) and distributed linear dispersion (DLD) code at high power. From the comparison with DDSTC, the DSTC-LCP scheme achieves the same rate using a lower modulation order, yielding almost the same performance.

Receiver design for impulse radio (IR) based ultra-wideband (UWB) communication is a challenge, because full-resolution digital receiver is difficult to implement under today's technology due to high sampling rate required. Some trade-offs can be made to the digital receiver, such as limiting amplitude resolution to only 1 bit, which results in a previously considered so-termed mono-bit receiver. In this paper, we consider the design of finite-resolution digital UWB receiver. We derive the optimal post-quantization processing, and analyze the achievable bit-error rate (BER) performance using an approximation of the log-likelihood ratio. We evaluate the effect of quantization threshold. The optimal threshold for two-bit quantization is obtained. Our work discloses the incremental gain that each sampling bit could bring and the results provide guidelines for designing IR UWB digital receivers.receivers.

Receiver design for impulse radio based ultrawideband (UWB) communication is a challenge. High sampling rate high resolution digital receiver is usually difficult to implement. Some tradeoffs can be made on the digital receiver, such as limiting amplitude resolution to only one bit, which results in a previously considered monobit receiver. In this paper, we consider the design of finite-resolution digital UWB receivers. We derive the optimal post-quantization processing, and analyze the achievable bit-error rate performance using an approximation of the log-likelihood ratio. Optimal thresholds for 4- and 3-level quantization are obtained. Training-based receiver template estimation is presented. Our work discloses the incremental gain that additional quantization levels offer and the results provide useful guidelines for designing impulse radio UWB digital receivers.