Estimating closely spaced frequency components of a signal is a fundamental problem in statistical signal processing. In this letter, we introduce 1-D real-valued and complex-valued shifted window (Swin) transformers, referred to as SwinFreq and CVSwinFreq, respectively, for line-spectra frequency estimation on 1-D complex-valued signals. Whereas 2-D Swin transformer-based models have gained traction for optical image super-resolution (SR), we introduce for the first time a complex-valued Swin module designed to leverage the complex-valued nature of signals for a wide array of applications. The proposed approach overcomes the limitations of the classical algorithms and the state-of-the-art deep learning approach cResFreq. SwinFreq and CVSwinFreq boast superior performance at low signal-to-noise ratio (SNR) and improved resolution capability while requiring fewer model parameters than cResFreq, deeming them more suitable for edge and mobile applications. SwinFreq achieves an average improvement of 0.03 in structural similarity index measure (SSIM) over cResFreq and a 5% gain in probability of resolution for closely spaced frequency components. We find that the real-valued Swin-Freq outperforms its complex-valued counterpart CVSwinFreq for several tasks while touting a smaller model size. Finally, we apply the proposed techniques for radar range profile SR using real data. The results from both synthetic and real experimentation validate the numerical and empirical superiority of SwinFreq and CVSwinFreq to the state-of-the-art deep-learning-based techniques and traditional frequency estimation algorithms. The code and models are publicly available athttps://github.com/josiahwsmith10/spectral-super-resolution-swin.

Ranging solutions for Internet of Things (IoT) localization applications seek to provide high accuracy with low cost of implementation. Among candidate IoT technologies that may fit this criterion, Bluetooth is a desirable choice as Bluetooth low energy (BLE) support is ubiquitous in modern smartphones, providing low implementation costs and low power consumption. Recent advancements in BLE ranging technology employ the Multicarrier Phase Difference technique which takes two-way channel frequency response (CFR) measurements. However, accurate ranging with these measurements is challenging due to many closely spaced multipath components from squaring the one-way CFR, a single or low number of snapshots, and model imperfections that arise in practical scenarios. To overcome these challenges, we propose a data-driven support vector regression (SVR) approach. Using real-world BLE measurements, our proposed SVR method demonstrates decimeter-level accuracy with single antenna devices, whereas multiple signal classification (MUSIC), a popular model-based method, requires multiple antennas to obtain comparable performance. Moreover, we show robustness in different multipath environments, including indoor, outdoor, and nonline-of-sight conditions, we determine generalization capabilities with training size, and we analytically establish the reduction in computational complexity compared to MUSIC.

In this paper, we introduce an innovative super resolution approach to emerging modes of near-field synthetic aperture radar (SAR) imaging. Recent research extends convolutional neural network (CNN) architectures from the optical to the electromagnetic domain to achieve super resolution on images generated from radar signaling. Specifically, near-field synthetic aperture radar (SAR) imaging, a method for generating high-resolution images by scanning a radar across space to create a synthetic aperture, is of interest due to its high-fidelity spatial sensing capability, low cost devices, and large application space. Since SAR imaging requires large aperture sizes to achieve high resolution, super-resolution algorithms are valuable for many applications. Freehand smart-phone SAR, an emerging sensing modality, requires irregular SAR apertures in the near-field and computation on mobile devices. Achieving efficient high-resolution SAR images from irregularly sampled data collected by freehand motion of a smartphone is a challenging task. In this paper, we propose a novel CNN architecture to achieve SAR image super-resolution for mobile applications by employing state-of-the-art SAR processing and deep learning techniques. The proposed algorithm is verified via simulation and an empirical study. Our algorithm demonstrates high-efficiency and high-resolution radar imaging for near-field scenarios with irregular scanning geometries.

A sequential sweeping receiver with fast switching time has been recently shown to be much more effective at spectrum scanning for cognitive radio applications than a compressed sensing receiver under similar hardware constraints. This paper presents a design of such rapid spectrum sweeping scanner. The design leverages the harmonics of hard-switching current-driven passive mixer to sense an arbitrary subband in the spectrum and downconvert it to baseband without the need for a wide-range tunable local oscillator (LO). The design operates as a frequency multiplexer with fast transient switching. The proposed scanner is designed and simulated in 65nm CMOS technology and achieves 4.8-dB minimum noise Figure and total power consumption of 38 mW from 1V supply voltage. The switching time to select a frequency bin is approximately three time constants of the corresponding frequency bin bandwidth.

In this article, we propose a framework for contactless human-computer interaction (HCI) using novel tracking techniques based on deep learning-based super-resolution and tracking algorithms. Our system offers unprecedented high-resolution tracking of hand position and motion characteristics by leveraging spatial and temporal features embedded in the reflected radar waveform. Rather than classifying samples from a predefined set of hand gestures, as common in existing work on deep learning with mmWave radar, our proposed imager employs a regressive full convolutional neural network (FCNN) approach to improve localization accuracy by spatial super-resolution. While the proposed techniques are suitable for a host of tracking applications, this article focuses on their application as a musical interface to demonstrate the robustness of the gesture sensing pipeline and deep learning signal processing chain. The user can control the instrument by varying the position and velocity of their hand above the vertically-facing sensor. By employing a commercially available multiple-input-multiple-output (MIMO) radar rather than a traditional optical sensor, our framework demonstrates the efficacy of the mmWave sensing modality for fine motion tracking and offers an elegant solution to a host of HCI tasks. Additionally, we provide a freely available software package and user interface for controlling the device, streaming the data to MATLAB in real-time, and increasing accessibility to the signal processing and device interface functionality utilized in this article.

Narrowband Internet of Things (NB-IoT) is a new technology being implemented into the Long-Term Evolution (LTE) standards to support machine-to-machine communications. Applications of IoT are expected to rapidly increase and to expand into many sectors. Increased number of IoT devices trying to access the medium with typically small data packets will overwhelm the network. Cognitive radios coupled with random access strategies can help alleviate excessive collisions in the network by many IoT access attempts. On the other hand, most IoT devices do not have hardware capabilities to perform sophisticated spectrum sensing methods over long durations. Therefore, it is important to reduce the spectrum sensing overhead while maximizing the NB-IoT network level throughput. In this paper, we derive a unique set of optimal sensing parameters which achieve the maximum throughput of a narrowband cognitive radio IoT (NB-CR-IoT) network. We show that accurate sensing from a single device perspective is not always the best solution for maximizing the throughput in an NB-CR-IoT network.

In this paper, a multi-user uplink channel with correlated Rayleigh fading coefficients is considered. Optimal data packet length is derived for throughput maximization of the system with stop and wait automatic repeat request (SW-ARQ). For the throughput formulation of the SW-ARQ system, the packet error rate (PER) is analytically derived using a two-state Markov chain modelling of the signal-to-interference ratio (SIR) of the time-varying channel. For the PER formulation, second-order channel statistics including average level crossing rate (LCR) and average outage duration (AOD) are derived. Numerical results indicate the accuracy of the obtained theoretical results. The effect of increasing the number of interferers on the throughput is investigated w.r.t given SIR threshold.

Many works considered the CSI uncertainty effect on interference alignment (IA) in MIMO narrow-band interference channel (IC). However, much less attention has been paid to CSI uncertainty effect on IA in SISO frequency-selective IC. This is partially due to the impractical requirement of infinitely large number of subcarriers to implement IA in such channel. Recently, a new approach was proposed to alleviate this limitation through applying IA in time domain while utilizing the guard interval [1]. In this paper, we investigate the performance of this approach under CSI imperfection. We also propose robust transceiver design algorithm that maximizes the received SINR accounting for the CSI imperfection in SISO frequency selective IC. In addition, the average sum stream rate under CSI imperfection is derived.

Interference alignment (IA) has proven to approach achieving optimal degrees of freedom (DoF) of interference channel. While IA over space dimensions is usually achieved through applying iterative algorithms, closed-form solutions exist for IA over frequency. However, these solutions put strict constraints on the number of subcarriers required and the number of DoF assigned to each user rendering it impractical to support more than 5 users [1]. In this paper, we propose two new approaches to relax these constraints and make it practical to use IA over frequency selective channel in SISO systems for arbitrary number of users, subcarriers and DoF. The two approaches apply the iterative methods developed for MIMO channels on SISO systems in both time and frequency domains. Moreover, we show the limitations of these iterative methods when applied on SISO systems and revisit the IA feasibility conditions deriving an upper bound on the DoF allowable for the users. Simulation results show the benefits of the proposed techniques over existing solutions and confirm the conjectures made in this paper.

Switching operation happens extensively in nowadays wireless devices between various functional modules. The transient responses during the hardware switching are non-ideal due to the non-ideal behavior of hardware components. The non-ideal transient may cause distortion on signals and degenerate system performance before the transient effect is settling down. We explore the advanced solutions to compensate the transient impairments rather than, as the conventional treatment, inserting guard intervals. In this paper, we present the transient carrier frequency offset (CFO) observed in hardware testbeds built for wireless communications research, which are configured working in time division duplexing (TDD) mode. The transient CFO arises as a result of the switching between transmission and reception radio functions. The transient CFO cannot be estimated by the conventional CFO estimators because it is time varying. We present the modeling and estimation of the transient CFO in this paper. A weighted KumaresanTufts (KT) algorithm is proposed by exploiting both low-rank approximation and subspace structure to optimize the estimation of the transient CFO. We compare the proposed algorithm with the existing methods as well as the Crámer-Rao bound (CRB). The proposed algorithm is also confirmed by the experimental results obtained from the hardware testbed.

In this paper, distributed antenna selection algorithms are proposed to improve uncoded bit-error-rate (BER) in multiple-input multiple-output (MIMO) interference channels. The well known distributed power control algorithm by Yates in 1995 is used in all proposed schemes together with distributed antenna search algorithms, explicitly distributed exhaustive search, relaxed exhaustive search, and cross-entropy optimization techniques. While maximization of minimum signal-to-interference plus noise ratio (SINR) is the antenna selection metric, power minimization subject to SINR constraint is the power control problem used in the proposed schemes. It is shown that the proposed algorithms improve the BER of the system with acceptable sum-rate and fairness achieved in the system. Prioritization of streams can improve the BER of the system such as by assigning higher priorities to more important streams. Although for simplicity, equal stream priority, i.e., fairness among streams, is considered in this work, the proposed schemes can be extended to prioritization scenarios.

Discontinuous reception (DRX) saves battery power of user equipment (UE) usually at the expense of potential increase in latency in the Long Term Evolution (LTE) networks. Therefore, an optimization is needed to find the best tradeoff between latency and power saving. In this paper, we first develop an analytical model to estimate power saving achieved and latency incurred by DRX mechanism for active and background mobile traffic. A tradeoff scheme is then formulated to maintain a balance between these two performance parameters based on operator's preference for power saving and latency requirement of traffic. The analytical model is validated using system level simulation results obtained from OPNET Modeler. The results show that the proposed tradeoff scheme is efficient in keeping a balance between power saving and latency. The results also indicate that DRX short cycles are very effective in reducing latency for active traffic, while shorter inactivity timer is desirable for background traffic to enhance power saving. We also proposed a mechanism to switch DRX configuration based on traffic running at UE, using UE assistance procedure recently adopted by 3GPP in Release 11. DRX configuration switching increases the power saving significantly without any noticeable increase in latency of active traffic.

Future wireless devices have to support many applications (e.g., remote robotics, wireless automation, and mobile gaming) with extremely low latency and reliability requirements over wireless connections. Optimizing wireless transceivers while switching between wireless connections with different circuit characteristics requires addressing many hardware impairments that have been overlooked previously. For instance, switching between transmission and reception radio functions to facilitate time-division duplexing can change the load on the power supply. As the supply voltage changes in response to the sudden change in load, the carrier frequency drifts. Such a drift results in transient carrier frequency offset (CFO) that cannot be estimated by conventional CFO estimators and is typically addressed by inserting or extending guard intervals. In this paper, we explore the modeling and estimation of the transient CFO, which is modeled as the response of an underdamped second order system. To compensate for the transient CFO, we propose a low complexity parametric estimation algorithm, which uses the null space of the Hankel-like matrix constructed from phase difference of the two halves of the repetitive training sequence. Furthermore, to minimize the mean squared error of the estimated parameters in noise, a weighted subspace fitting algorithm is derived with a slight increase in complexity. The Crámer-Rao bound for any unbiased estimator of the transient CFO parameters is derived. The performance of the proposed algorithms is also confirmed by the experimental results obtained from the real wireless transceivers.

Discontinuous reception (DRX) can be configured as a power saving mechanism for user equipment (UE) in connected mode in the long term evolution advanced (LTE-A) network. Since DRX saves UE's battery power usually at the cost of increased latency, a tradeoff between these two conflicting performance metrics are inevitable. DRX parameters can be optimized for either maximizing power saving or minimizing latency based on the applications running at the UE. In this paper, we first develop an analytical model to estimate power saving achieved and latency incurred by DRX operation. We then propose a scheme to tune DRX parameters in order to keep a balance between these conflicting performance metrics by formulating a multi-objective optimization problem. The tradeoff between power saving and latency is achieved based on the operator's relative preferences for these metrics. We also validate our analytical model by system level simulation results from OPNET Modeler. OPNET simulation results are well aligned with the MATLAB results based on analytical model and the results show that proposed scheme efficiently optimizes the DRX configuration for the battery power saving and latency requirements of various diverse data applications based on the preferences determined by operator.

In LTE networks, mobile applications and their higher data rate requirements are the latest phenomenon causing a tremendous need in power saving for mobile devices. Discontinuous reception (DRX) is one of the key power saving mechanisms in LTE. Since DRX saves battery power of user equipment usually at the expense of potential increase in latency, an optimization is needed to find the best tradeoff between latency and power saving. In this paper, we first develop an analytical model to estimate power saving achieved and latency incurred by DRX operation. Then, a tradeoff scheme is formulated to maintain a balance between these two performance parameters based on operator's preference for power saving. We validate analytical model using system level simulation results obtained from OPNET Modeler. The results show that the proposed tradeoff scheme is highly efficient in keeping a balance between power saving and latency. We have shown that proposed scheme can achieve a significant delay improvement with a small decrease in power saving. The results also indicate that short DRX cycles are very effective in reducing latency for active traffic.

This paper discusses different approaches used for millimeter wave imaging of 2D objects. Imaging of a 2D object requires reflected wave data to be collected across two distinct dimensions. In this paper, we propose a reconstruction method that uses narrowband waveforms along with 2D beam steering. The beam is steered in azimuthal and elevation direction, which forms the two distinct dimensions required for the reconstruction. The reconstruction technique uses inverse Fourier transform along with amplitude and phase correction factors. In addition, this reconstruction technique does not require interpolation of the data in either wavenumber or spatial domain. Use of the 2D beam steering offers better performance in the presence of noise compared with the existing methods, such as switched array imaging system. Effects of radio-frequency impairments such as quantization of the phase of beam steering weights and timing jitter, which add to phase noise, are analyzed.

Simultaneous multiuser beamforming in multiantenna downlink channels can entail dirty paper (DP) precoding (optimal and high complexity) or linear precoding (suboptimal and low complexity) approaches. The system performance is typically characterized by the sum capacity with homogenous users with perfect channel state information at the transmitter. The sum capacity performance analysis requires the exact probability distributions of the user signal-to-noise ratios (SNRs) or signal-to-interference plus noise ratios (SINRs). The standard techniques from order statistics can be sufficient to obtain the probability distributions of SNRs for DP precoding due to the removal of known interference at the transmitter. Derivation of such probability distributions for linear precoding techniques on the other hand is much more challenging. For example, orthogonal beamforming techniques do not completely cancel the interference at the user locations, thereby requiring the analysis with SINRs. In this paper, we derive the joint probability distributions of the user SINRs for two orthogonal beamforming methods combined with user scheduling: adaptive orthogonal beamforming and orthogonal linear beamforming. We obtain compact and unified solutions for the joint probability distributions of the scheduled users' SINRs. Our analytical results can be applied for similar algorithms and are verified by computer simulations.

We derive the joint probability distribution of zero-forcing (ZF) V-BLAST gains under a greedy selection of decoding order and no error propagation. Unlike the previous approximated analyses, a mathematical framework is built by applying order statistics rules and an exact closed-form joint probability density function expression for squared layer gains is obtained. Our analysis relies on the fact that all orderings are equiprobable under independent and identical Rayleigh fading. Based on this idea, we determine the joint distribution of the ordered gains from the joint distribution of the unordered gains. Our results are applicable for any number of transmit and receive antennas. Although we present our analysis in a ZF V-BLAST setting, our analytical results can be directly applied for the dual cases of ZF V-BLAST. Under the assumption of a low rate feedback of decoding order to the transmitter, a benefit of having exact expressions is illustrated by the calculation of the cutoff value under optimal power allocation that maximizes the sum of the substream outage capacities under a given sum power constraint. We provide numerical results and verify our analysis by means of simulations.

This work considers the problem of multiuser transmission in a downlink transmission system where a transmitter with multiantenna elements sends information to a set of scheduled users with each having a single antenna. We investigate the sum rate performance of an orthogonal beamforming (OBF) scheme called adaptive OBF, which is a suboptimal low-complexity alternative to the capacity-achieving dirty paper coding approach. We develop a simple order statistics framework to evaluate the probability density functions (pdfs) of the scheduled users' signal-to-interference plus noise ratio (SINR) values based on the knowledge of channel statistics and selection procedure. In particular, the pdf expressions for first and second scheduled users' SINR values are obtained in closed-form. The analysis is verified by numerical results.

The main advantages of random access networks are the ability to work with little or no infrastructure and independent transmission of users. Thus, users themselves in a random access network decide when to access the channel. Design of cognitive radio (CR) users in random access networks necessitates integrating unique functions such as spectrum sensing, policy and configuration databases, and self-configuration, etc. Spectrum sensing is especially crucial in terms of determining the channel availability and characterization. The traditional approach to cognitive radio network assumes a centralized spectrum sensing through cooperation of the CR users. However, such centralized approach adds additional level complexity which may not be desired in random access networks. In this paper, we investigate autonomous sensing and random access protocols for cognitive radios such that independent transmission of CR users is still allowed. The proposed methodology eliminates the control channel required in traditional collaborative sensing while preserving gains from cooperation and maintaining tolerable interference levels to the primary users of the spectrum. Our proposed design methodology is demonstrated using the slotted ALOHA protocol as the MAC layer due to its simplicity.

For the K user, single input single output (SISO), frequency selective interference channel, a new low complexity transmit beamforming design that improves the achievable sum rate is presented. Jointly employing the interference alignment (IA) scheme presented by Cadambe and Jafar in and linear minimum mean square error (MMSE) decoding at the transmitters and receivers, respectively, the new IA precoding design improves the average sum rate while preserving the achievable degrees of freedom of the Cadambe and Jafar scheme, K/2.

In this paper, we compute the throughput of Hybrid ARQ schemes over slotted quasi static multiantenna channels. Specifically, we consider slotted Aloha, Hybrid ARQ Chase combining (HARQ-CC), and Hybrid ARQ incremental redundancy (HARQ-IR) schemes. Using reward-renewal theorem, we adopt closed-form throughput equations in [1] for multiantenna channels. Moreover, we investigate the affect of number of antennas on the throughput of these HARQ schemes with respect to offered load and target rate.

The K user interference alignment scheme proposed by Cadambe and Jafar (CJ) achieves K/2 degrees of freedom for networks with frequency selective channels. In this paper, we provide two new algorithms that optimize the precoding subspaces which maximize the data rate performance of the CJ scheme while maintaining the achievable degrees of freedom. One design is obtained as a global solution of a constrained convex (concave) optimization problem that maximizes the sum rate. The other design provides a low complexity closed-form solution to a constrained maximization problem with a suboptimal sum rate objective function. We also show that both designs can be combined with Shen, Host-Madsen and Vidal orthonormalization that achieves further gains in sum rate by optimizing the precoding vectors generated by the proposed algorithms.

Temporal fading is an inherent characteristic of wireless channels. Furthermore, in a multiuser spread spectrum network, the instantaneous value of the multiple access interference varies with time. As a consequence, the instantaneous signal-to-interference-plus-noise ratio varies from one frame to another. This variation can even occur within a given frame. In this work, we present a new set of analytical and numerical results on Incremental Spreading (IS) protocols that adaptively increase the duration of a frame, or the spreading gain, based on the past instantaneous channel and interference conditions. The control operation is carried out mainly at the receiver end, resulting in a distributed system, unlike other spreading gain control algorithms which are centralized. These protocols are ready to be implemented in single- and multi-carrier code division multiple access (CDMA) type technologies and can enhance the throughput and capacity of wireless networks.

In recent years, digital beamforming has evolved into an excellent technology to improve wireless communications over fading channels. Especially in slow fading environments with a sufficient number of users in the system, opportunistic beamforming through multiuser diversity [1] has offered some advantages over true beamforming methods that rely on full channel feedback and/or robust channel estimation methods. Opportunistic beamforming achieves good throughput with only signal-to-noise ratio (SNR) feedback from the users. The quality of the SNR feedback such as the degree of SNR quantization is essential for opportunistic beamforming because the base station selects the best receiving user based on the SNR measurements sent by the users. In this paper, we develop an optimum SNR quantization method performed by the users and analyze its impact on the system throughput. While keeping the fairness among the users, we show that the opportunistic beamforming gain can still be realized with the help of the proposed quantization method. Theoretical analysis and computer simulation results show the feasibility and effectiveness of the method which provides insights for engineers to implement opportunistic beamforming in practice.

In a multiuser system, the multiuser diversity gain capitalizes primarily on the dynamic range of channel fluctuations. When the channel varies slowly, opportunistic beamforming using multiple antennas at the transmitter can be used to generate artificial temporal fading resulting in amplified multiuser diversity gain. When there exists a sufficient number of users in the system, opportunistic beamforming has been shown to achieve similar throughput as true beamforming but with much more limited feedback. In particular, it has been shown that the required number of users to achieve near true beamforming throughput on the number of transmit antennas is exponential when each user is equipped with single antenna. In this paper, a framework is presented to analyze the outage probability, diversity order and array gain of opportunistic beamforming over Rayleigh fading channels. First, we obtain an exact closed-form expression for the outage probability. Then, using some approximations, we show that the number of users must be at least the same as the number of transmit antennas in order to achieve full diversity. We quantify the array gain for the full diversity case and establish the dependence of the array gain on the number of users. Finally, we verify our analysis with numerical results.

Energy detection has been adopted as an alternative spectrum sensing method for cognitive radios due to its low computational complexity and not requiring a priori information of the signal to be detected. In this work, we analyze the accuracy of different energy detector models in approximating the exact solution both in AWGN and Rayleigh channels. Our simulations show that all of these models have almost the same performance in Rayleigh channels. Hence, we conclude that any one of them can be used to represent the energy detector's performance instead of the complicated exact solution in Rayleigh fading channels. Using Edell's model, we derive the relationship between the number of samples and the signal-to-noise ratio (SNR) for Rayleigh channels similar to the results obtained for AWGN channels. We also verify our analytical results by simulations.

Convolutional codes provide a robust means of forward error control, especially in a harsh communication medium like a wireless channel. The use of rate compatible punctured convolutional (RCPC) codes, derived by code puncturing at the transmitter, is an established method to improve the throughput of wireless networks. In this paper, we propose to exploit the idea of code puncturing at the receiver end to improve the performance of wireless systems. It is shown that utilizing the proposed protocol yields a significant reduction in the frame error rate at the expense of a slight increase in computational complexity.

In recent years, diversity techniques have evolved into highly attractive technology for wireless communications in different forms. For instance, the channel fluctuations of the users in a network are exploited as multiuser diversity by scheduling the user with the best signal-to-noise ratio (SNR). When fading is slow, beamforming at a multiple antenna transmitter is used to induce artificial channel fluctuations to ensure multiuser diversity in the network. Such a beamforming scheme is called opportunistic beamforming since the transmitter uses random beamforming to artificially induce opportunism in the network [1]. Opportunism requires a large number of users in the system in order to reach the performance of the true beamforming that uses perfect channel state information (CSI). In this paper we investigate the benefit of having partial CSI at an opportunistic transmitter. In the investigation, we focus on the maximum normalized SNR scheduling where user's feedback consists of SNR relative to its channel gain. We show that opportunism can be beneficially used to increase the average throughput of the system. Simulations support the analytical average throughput results obtained as the amount of CSI and the number of users vary.

Abstract In code division multiple access (CDMA) systems, the capacity of forward link (FL) communication to mobile receivers is limited primarily by co‐channel interference (CCI). Adaptive antenna arrays (AAAs) that use antenna arrays along with advanced signal processing at the base station (BS) have been proposed to mitigate this limitation. For a 3G CDMA cellular network, where each BS equipped with an AAA serves mixture of voice and data users within its coverage, we study FL capacity and investigate the effects of different factors (array topology, multipath angle spread, data rate, and beamforming algorithm) on this capacity under Rayleigh fading channel. By modeling the instantaneous signal‐to‐interference power ratio received at the mobile, we derive the system outage equation that considers blocking of either desired voice or data user. Simulation results show that for the same element spacing and number of antenna elements per cell, the uniform circular array (UCA) topology results in larger capacity than the sectorized uniform linear array (ULA) topology does, and that a larger angle spread or data user rate reduces FL capacity. Copyright © 2007 John Wiley & Sons, Ltd.

Opportunistic beamforming has been shown to achieve true beamforming gains with unquantized signal-to-interference ratio (SNR) feedback when there are sufficient users in the system [1], [2]. In this paper we investigate the performance of opportunistic beamforming with an SNR quantization scheme that maximizes the expected system throughput. To show that the opportunistic beamforming can still be beneficially employed with quantized SNR feedback, we derive the SNR statistics and the system throughput to optimize the SNR quantization with respect to thresholds separating quantization levels. Numerical results are used to illustrate the impact of the optimum threshold levels and the number of thresholds.

In wireless networks, average system through-put can be improved by scheduling transmissions according to instantaneous signal-to-noise ratio (SNR) values of the users assuming that channel fading is fast enough to maintain multiuser diversity. If the fading is slow, opportunistic multiple antenna approaches can introduce artificial fading in the system. These schemes require significant amount of SNR feedback. A novel channel estimation method for opportunistic beamforming is proposed based only on SNR feedback. More importantly, the effectiveness of the proposed channel estimation algorithm is verified using our multiple antenna testbed. The experimental results show that the channel estimation based beamforming can attain true beamforming gains.

In a wireless network the channel fluctuations of the users can be exploited by means of multiuser diversity where the user transmissions are scheduled when they experience a high instantaneous SNR. When fading is slow, beamforming at a multiple antenna transmitter can be used to induce artificial temporal fading to ensure multiuser diversity in the network. Such a beamforming scheme is called opportunistic beamforming [1] since the transmitter sends random beamforming vectors to artificially induce opportunism in the network. Opportunistic beamforming requires a large number of users in the system in order to achieve the performance of the true beamforming that uses perfect channel state information (CSI). In this paper we investigate the benefit of having partial CSI at the transmitter. We show that opportunism is still exploited to increase the average throughput of the system. Numerical results show that with partial CSI at the transmitter performance close to that of true beamforming can be obtained with even fewer number of users in the system.

An adaptive scheme for packet retransmission and packet. error control in DS-CDMA random access networks is presented. The system provides error correction coding (ECC) by using code redundancy into CDMA data packets and adaptive retransmission control (ARC) by using steady state Markov process. Simulation results show that packet error control can be effectively linked to ARC. A joint adaptation processing on ECC parameter and retransmission probability improves the system capacity significantly. The performance optimization is more feasible in high traffic wireless systems. The results are useful in designing such systems for deployment.

The analytical performance of the blind estimation of finite-impulse response (FIR) channels without eigendecomposition in CDMA systems is examined. The channel problem arises when CDMA signals are subject to frequency selective fading. Blind channel estimation is desired when training data is unavailable. Blind channel estimation techniques in the literature often rely on subspace decomposition of received signals. Blind channel estimation can also be coupled with a multiuser receiver design in the context of a minimum output energy (MOE) criterion. While subspace decomposition is avoided, the inverse of the data covariance matrix of the received signals is needed (Xu, Z. et al., Proc. IEEE Asilomar Conf. Signals, Syst. Comput., p.689-93, 2002). Previously (Ozdemir, O. and Torlak, M., Proc. IEEE Int. Conf. on Commun., vol.5, p.2621-5, 2004), we proposed a blind channel estimation method using simple covariance matrix transformations. We now use analytical and simulation results to show that the channel estimates converge to the eigendecomposition based channel estimates at medium to high signal-to-noise ratio (SNR).

We address the problem of blind estimation of finite-impulse response (FIR) channels without eigendecomposition in CDMA systems. The blind channel estimation problem arises in the presence of frequency selective fading, when the training data is unavailable. The blind channel estimation techniques in the literature often rely on the eigendecomposition of the received signals. The blind channel estimation can also be coupled with the multiuser receiver design in the context of minimum output energy (MOE) criterion. While MOE receiver avoids eigendecomposition, the inverse of data covariance matrix of received signals is needed [Z. Xu et al., Nov. 2002]. In this paper we propose a blind channel estimation method that avoids eigendecomposition. At the same time, the proposed method directly uses the data covariance matrix without requiring its inversion. Analytical and simulation results are provided to show that the channel estimates converge to the eigendecomposition based channel estimates at the medium to high signal-to-noise ratio (SNR).

A joint, processing protocol of packet combining and adaptive retransmission control (ARC) in DS-CDMA random access networks has been investigated. The proposed system uses diversity packet combining while adaptively adjusting the retransmission probability of backlog users to optimize packet retransmissions. A soft decision DS-CDMA analysis without fading is used to derive the optimum channel input for ARC. Simulation results show that, a joint control of packet combining and adaptive retransmission can significantly increase the channel throughput and maintain the performance very close to its optimum in the high traffic region.

The detection of CDMA signals in multipath channels can be easily done with the aid of training sequences. In the absence of training sequences, a number of blind adaptive and subspace-based multiuser receivers has been proposed for equalization of dispersive CDMA channels. We propose an analytical solution to direct equalization of CDMA signals. The method does not require training sequences or knowledge of interfering user signature waveforms and channel parameters. The method exploits the constant modulus property of CDMA signals and Toeplitz filtering matrix constructed from the desired user's spreading code. It requires less data samples than the blind adaptive multiuser receivers to converge. Thus, it is suitable for estimating initial receiver parameters before switching to decision directed mode. As a byproduct, the method estimates the channel parameters for the user of interest. It also provides an exact linear zero-forcing solution in the absence of noise.

CDMA systems need simultaneous multiple access interference suppression and adaptive interference suppression filters that may span three symbols. Thus, a large number of filter coefficients need to be estimated. By the use of reduced rank filtering, it is possible to lower the number of required filter coefficients with a small decrease in performance. M. Honig (see Proc. IEEE Asilomar Conf. Signals, Syst. Comput., p.1106-10, 1998) made a successful attempt to develop a reduced rank algorithm for non-dispersive CDMA signals based on the multistage Wiener (MSW) filter of J. Goldstein et al. (see IEEE Trans. on Information Theory, vol.44, no.7, p.2943-59, 1998). Motivated by MSW, we propose reduced rank advanced RAKE receivers for dispersive CDMA signals. The proposed receivers are blindly implemented in a lower dimensional space, relative to the full-rank receivers, without the aid of training sequences and channel information. By exploiting the structure of the user signature waveform, the proposed receivers exhibit performance close to that of the reduced rank minimum mean square error (MMSE) receiver implemented with the desired user's known channel information.

In asynchronous CDMA systems, transmission over frequency-selective channels subjects the signal to interchip interference and intersymbol interference, both of which cause multiple access interference (MAI). MAI cannot be easily eliminated without the knowledge of the channel parameters. Both blind adaptive and subspace based multiuser receivers have been proposed to eliminate the interference. We propose a self-recovering RAKE receiver that does not require training sequences or knowledge of interfering user signature waveforms. The contribution of this paper is the combination of constant modulus algorithm and previously derived constrains. The constrains depend on the multipath structure of the desired signal. The proposed receiver converges faster than the blind adaptive multiuser receivers. Its fast convergence and low complexity make it well suited for rapidly time-varying mobile radio frequency selective fading channels. By simulation, we compare the proposed method with blind adaptive constrained LMS RAKE receiver and minimum output energy receiver.

The problem of blindly estimating multiple digital co-channel communication signals using an antenna array is studied in the presence of multipath fading. We develop a fast sequential-estimation algorithm for separating multi-user signals based on the geometric observation made by Kansen and Xu (see Proc. ICASSP'96, p.1085-8, Atlanta, GA, 1996). When the signals are constrained to a finite alphabet, it is possible to visualize the geometric properties of the problem, which can be exploited to sequentially extract the digital co-channel communication signals. We present simulation results comparing the speed and BER performance with different methods.

In a multi-transmitter broadcast system, the weight vector for each message signal can provide an additional degree of freedom for signal enhancement and interference suppression by taking advantage of the spatial diversity among the users. The design of optimal weight vectors that maximize the overall channel capacity is an open problem. Under certain power constraints, the channel capacity R is a highly nonlinear function of the M-dimensional weight vectors {w/sub i/}, where M is the number of transmitters. Hence, a closed-form algebraic solution that maximizes R over {w/sub i/} does not seem to be tractable. We decouple the weight vectors in R to simplify the optimization problem to a search for the maxima of a smooth multidimensional function. Based on this decoupling, we derive and evaluate two algorithms for computing weight vectors for the two-user and three-user cases: orthogonal and optimal. We also propose a near-optimum algorithm for the two-user case. The optimal algorithm requires an iterative search.

By exploiting the spatial diversity among different signals, anti-jamming communications can be accomplishing using antenna arrays. In this paper, we discuss the self-recovery problem for antenna array wireless systems after the communication link is broken due to abrupt environmental changes. In particular, we derive a novel approach that can extract the desired signal from heavy interference without using a priori spatial information of the desired signal or jamming sources. We show how this new method can be modified to handle intersymbol interference and demonstrate its effectiveness using computer simulations. Results from field experiments show that the new approach can reliably reject interference or jamming sources and recover the desired signal under 0 dB C/I.