To fully exploit the benefits of spatial multiplexing gains within Frequency Division Duplex (FDD) Multiple-Input Multiple-Output (MIMO) systems, the development of a robust Channel State Information (CSI) feedback compression methodology with low over- the-air CSI overhead while achieving high reconstruction accuracy is critical. Such methodology must be able to effectively reduce communication overhead without compromising system level performance. Traditional codebook-based approach, as outlined in the 3rd Generation Partnership Project (3GPP) standards encounters significant challenges to balance air-interface overhead and computational complexity. In this paper, we introduce a novel and adaptable Deep Leaning (DL) based CSI codebook technique leveraging vector quantization known as the CSI-Look-Up Table (CSI-LUT). Our numerical results show that the CSI - L UT has potential in reducing > 90 % of CSI overhead for max rank 1 while still achieving better average reconstruction accuracy and system level performance than traditional codebook-based approach. We anticipate that such advancements will play a pivotal role in enhancing the efficiency and performance of CSI feedback in MIMO systems, contributing significantly to the evolving landscape of 5G and beyond.

Discrete Fourier transform (DFT) codebook-based solutions are well-established for limited feedback schemes in frequency division duplex (FDD) systems. In recent years, data-aided solutions have been shown to achieve higher performance, enabled by the adaptivity of the feedback scheme to the propagation environment of the base station (BS) cell. In particular, a versatile limited feedback scheme utilizing Gaussian mixture models (GMMs) was recently introduced. The scheme supports multi-user communications, exhibits low complexity, supports parallelization, and offers significant flexibility concerning various system parameters. Conceptually, a GMM captures environment knowledge and is subsequently transferred to the mobile terminals (MTs) for online inference of feedback information. Afterward, the BS designs precoders using either directional information or a generative modeling-based approach. A major shortcoming of recent works is that the assessed system performance is only evaluated through synthetic simulation data that is generally unable to fully characterize the features of real-world environments. It raises the question of how the GMM-based feedback scheme performs on real-world measurement data, especially compared to the well-established DFT-based solution. Our experiments reveal that the GMM-based feedback scheme tremendously improves the system performance measured in terms of sum-rate, allowing to deploy systems with fewer pilots or feedback bits.

Ingredient recognition has received significant attention due to its numerous industrial applications, such as intelligent retail terminals and intelligent cooking devices. However, ingredient recognition has the following challenges: 1) dynamic changes in the number of categories; 2) greater diversity and regionality of ingredients; and 3) large visual differences among different states of ingredients. In this article, we propose an adaptive multi-centric network (AdMNet) to solve the problem of ingredient recognition. AdMNet is based on the idea of retrieval, which consists of two main parts, the adaptive multi-centric nearest-neighbor central mean (AdM-NCM) classifier, and the context-aware attentional pooling (CAP) module. The AdM-NCM classifier adaptively establishes category-centric vector groups to recognize ingredients via optimizing the minimum clustering variance, where each state of the ingredient has its corresponding centric vector. The CAP module combines contextual information and multiple attention mechanisms. It captures more focused and discriminative features with higher weights assigned to fine-grained features, which results in better feature representation. In addition, we collect a large-scale ingredient dataset, ISIA Ingredient-201 with 201 classes and 100 442 images. To prove the greater robustness and generalization of our method, we compare the metrics in basic scenarios and realistic scenarios with those of other methods. Specifically, the base scenario is the regular setup, and the real scenario is similar to the class incremental learning setup. The experimental results show that our method reaches the state of the art on both basic scenarios and realistic scenarios with small samples.

We introduce multiple access schemes for a novel non-coherent single-carrier (SC) modulation, called modulation on conjugate-reciprocal zeros (MOCZ), to enable a high-spectral efficient and mobile machine-type-communication for up- and downlink transmissions. The modulation can be used for a time-division and frequency-division multiple access (FDMA). To utilize FDMA we will adapt SC-FDMA techniques and demonstrate that a time-overlay of multiple users (MU) can significantly reduce the peak-to-average-ratio (PAPR) in the downlink. Furthermore, we compare the MU-MOCZ schemes to standard modulations in narrow-band-Internet-of-things (NBIoT) scenarios with smallest transport block size in the LTE bands. To adapt the standard numerology of the transport channel we introduced soft decoding for higher order MOCZ designs. We demonstrate that our proposed schemes outperforms the standardized scheme in highly mobile and frequency-selective fading channels by a slight PAPR reduction in the downlink.

We study distributed algorithms for joint adaptation of precoding and combining filters in frequency division duplex (FDD) multiple-input multiple-output (MIMO) cellular systems. Our approach extends bi-directional training (BiT), designed for time division duplex (TDD) systems, to FDD systems where uplink/downlink reciprocity may not apply. An analysis of the performance loss due to different uplink-downlink frequencies in a point-to-point scenario, shows that a direct application of BiT gives mismatched precoders with substantial performance degradation. We first propose an algorithm assuming each base transceiver station (BTS) knows the uplink and downlink channels of mobiles within the cell. We then consider the scenario where neither the BTSs nor the mobiles have a priori channel state information. Our proposed approaches assume angular reciprocity characterized by angles of arrival/departure that vary predictably with frequency. Hence spatial beams corresponding to angles of arrival can be turned around to the corresponding angles of departure in the paired band. We present three methods, differing in how the angular decomposition is applied and used, namely, to reconstruct the combiner directly, or to reconstruct the received signal. Simulation results indicate that when the multipath is sufficiently sparse, most of achievable gain with channel reciprocity and TDD can be recovered.

We study distributed algorithms for joint adaptation of precoding and combining filters in frequency-division duplex (FDD) multiple-input multiple-output (MIMO) cellular systems. Neither the base stations nor the mobiles have a priori channel state information, and the transmit/receive filters are directly adapted through training. We propose extensions of bi- directional training (BiT), designed for time- division duplex (TDD) systems, to FDD systems where uplink-downlink reciprocity may not apply. A direct application of BiT can give mismatched precoders with substantial performance degradation. Our approach assumes angular reciprocity, that is, the channel multipath, which causes the frequency selectivity, is characterized by angles of arrival/departure that vary predictably with frequency. Hence spatial beams corresponding to angles of arrival can be turned around to point towards the corresponding angles of departure in the paired band. We present three methods based on this general approach with different complexity for angular estimation and selection. Simulation results indicate that when the multipath is sufficiently sparse, most of the achievable gain with channel reciprocity and TDD can be recovered.

To further improve the spectrum efficiency of 5G massive MIMO networks, bi-directional training (BiT) was developed for TDD systems to maximize the downlink weighted sum rate. However, the previous work was limited to narrowband systems. In this paper, we extend BiT for 5G wideband systems. A global, centralized optimization problem is first formulated for a wideband system. The (sub-optimal) solution is then distributed across the base stations and user equipment (UE), resulting into a wideband BiT algorithm that iteratively adapts transmission and reception filters for each base station and each UE with only local information. The algorithm may be seen as a narrowband BiT operating on an optimal narrowband representation of a group of subcarriers each with a different channel, and the optimal narrowband representation maintains the first and second moments of all the channels. Simulation results are provided to evaluate the performance of the algorithm in a wideband system.

The fifth-generation (5G) wireless communication system is expected to provide increasing throughput with advanced transmitting and receiving techniques. This paper considers the problem of maximizing the downlink data rate in a multi-cell system by jointly designing transceiver filters, transmission ranks, and user associations. The base stations (BSs) are equipped with massive multiple-input multiple-output (MIMO) antennas, and can cooperatively serve the user equipments (UEs) in the network. A global optimization problem is formulated to maximize the downlink weighted sum rate (WSR). We first propose a centralized solution by assuming the existence of a central controller and global channel state information (CSI). We use the re-weighted l1 method to approximate the problem, and then solve it using the fractional programming (FP) algorithm. Since the centralized algorithm only requires local channel and signal information, we decompose it and propose a distributed algorithm based on the idea of bi-directional training (BIT). By iterative uplink and downlink training, the precoders, receivers, and transmission ranks are adaptively optimized without explicit channel estimation. An uplink power control scheme is proposed to improve the fairness among the UEs. Simulation results are presented to evaluate the performance of proposed algorithms.

Millimeter-wave (mmWave) systems use directional beams to support high-rate data communications. Small misalignment between the transmit and receive beams (e.g., due to the mobility) can result in a significant drop of the received signal quality, especially in line-of-sight communication channels. In this paper, we propose and evaluate high-resolution angle tracking strategies for wideband mmWave systems with mobility. We custom design pairs of auxiliary beams as the tracking beams, and use them to capture the angle variations, toward which the steering directions of the data beams are adjusted. Different from conventional beam tracking designs, the proposed framework neither depends on the angle variation model nor requires an on-grid assumption. For practical implementation of the proposed methods, we examine the impact of the array calibration errors on the auxiliary beam pair design. Numerical results reveal that by employing the proposed methods, good angle tracking performance can be achieved under various antenna array configurations, channel models, and mobility conditions.

It is expected that future cellular networks will be densely deployed with both macro cells and small cells to meet the ever-increasing traffic demand, and a cost-effective and reliable backhaul connectivity will be essential to such deployment scenarios. As wired backhaul solutions may not be always practical or feasible, in this paper we consider wireless in-band backhaul for a network with macro cells and small cells. To improve the overall user experience in the network, efficient resource allocation and transmission schemes taking into account of both backhaul links and access links are proposed and associated optimization problems are formulated. Numerical results for the proposed schemes demonstrate significant improvements over several other schemes.

This paper investigates the performance of longitudinal control of a platoon of vehicles using communication of messages through a cellular network. We describe a platooning algorithm that utilizes, at each following vehicle, information communicated by the preceding vehicle and the lead vehicle of the platoon. The information received from the lead vehicle includes the position, speed and acceleration of the lead vehicle, whereas the information received from the preceding vehicle includes the target acceleration the preceding vehicle is attempting to achieve. The information is transmitted in messages that are received by a cellular base station, which then retransmits the messages to the intended vehicles. We perform an analysis of communication system capacity for a highway setting with dense configurations of platoons. We show that (a) the additional latency due to transmission through the infrastructure does not impact platooning performance, and (b) LTE can even support extreme vehicle densities with no impact to platooning.

The multi-carrier ultra-dense network (MC-UDN) has been considered as a promising candidate for future 4.5G and 5G wireless networks to support the explosive growth of data traffic. In a MC-UDN, it is crucial to efficiently allocate network resources including the network nodes and carriers according to traffic loads and channel and interference conditions. In this paper, carrier- level resource allocation jointly in the time domain and frequency domain is proposed. We formulate optimization problems based on queuing theory for two traffic models to optimize the average user experience in the network. Simulation and analytic results show that our proposed scheme can greatly enhance user experience and provide a much more efficient allocation strategy than existing ones, such as resource allocation in the frequency domain only.

We study topology adaptation, interference management, and cross-tier load balancing/shifting in densely deployed heterogenous networks (HetNets). A novel HetNet model is proposed, which considers dynamic traffic and interference. Long term average performance metrics, such as user throughput and base station utilization ratio, are used to evaluate the performance of the network. Due to lack of closed form representation, the proposed topology adaptation scheme focus on improving user experience, system stability and energy saving by adjusting the topology of the network on different system levels. A distributed algorithm is proposed to mitigate interference and adapt network topology. Topology adaptation is achieved by deciding the user assignment and pico base station on/off status. Numerical results suggest the proposed algorithm provides significant performance gains in user throughput, system stability, and energy saving.

With the ever-growing popularity of smartphones, new services are emerging where the local and positioning aspects becomes more important. Additionally, new types of traffic, such as vehicle-to-vehicle communications are being developed. In order to address these new trends, release-12 of LTE has started to evaluate, and possibly standardize, solutions for device-to-device (D2D) communication. This provides the foundations for the deployment of a D2D overlay. However, this should be viewed as a first step, and future LTE releases should encompass more advanced D2D communication techniques and architectures. In this paper, after reviewing the standardization progress for LTE in release-12, several possible improvements are examined for future releases both D2D discovery and communication. It is shown that the future D2D architecture should exploit the network when possible in order to provide an improved user experience and satisfy the need for more local services.

This work is concerned with proposing an adaptive method to dynamically adjust genetic operator probabilities throughout the evolutionary process. The proposed method relies on the individual preferences of each chromosome, rather than the global behavior of the whole population. Hence, each individual carries its own set of parameters, including the probabilities of the genetic operators. The carried parameters undergo the same evolutionary process as the carriers--the chromosomes - do. We call this method Evolved Evolutionary Algorithm (E2A) as it has an additional evolutionary process to evolve control parameters. Furthermore, E2A employs a supplementary mutation operator (DE-mutation) which utilizes the previously overlooked numerical optimization model known as the Differential Evolution to expedite the optimization rate of the genetic parameters. To leverage our previous work, we used Gene Expression Programming (GEP) as a benchmark to determine the performance of our proposed method. Nevertheless, E2A can be easily extended to other genetic programming variants. As the experimental results on a wide array of regression problems demonstrate, the E2A method reveals a faster rate of convergence and provides fitter ultimate solutions. However, to further expose the power of the E2A method, we compared it to related methods using self-adaptation previously applied to Genetic Algorithms. Our benchmarking on the same set of regression problems proves the supremacy of our proposed method both in the accuracy and simplicity of the final solutions.

Long-Term Evolution (LTE) will provide substantial enhancements to UMTS 3G systems including improved system capacity and coverage, low latency, reduced operating costs, multi-antenna support, flexible bandwidth operations and seamless integration with existing systems. LTE supports both Frequency Division Duplex (FDD) and Time Division Duplex (TDD) modes to provide deployment flexibility in accordance with operator’s preference and spectrum allocation. This paper presents an overview of LTE TDD design and highlights key differences with FDD. Design challenges unique to TDD are presented together with adopted technical solutions. Finally, simulation results are provided to demonstrate typical TDD system performance with data applications.

Gene Expression Programming (GEP) is an evolutionary algorithm that incorporates both the idea of a simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and the tree structure of different sizes and shapes used in Genetic Programming (GP). As with other GP algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this work, we describe a new approach of constant generation using Differential Evolution (DE), a real-valued GA robust and efficient at parameter optimization. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variations.

Long term evolution (LTE) of the UMTS Terrestrial Radio Access and Radio Access Network is aimed for commercial deployment in 2010. Goals for the evolved system include support for improved system capacity and coverage, high peak data rates, low latency, reduced operating costs, multi-antenna support, flexible bandwidth operations and seamless integration with existing systems. To reach these goals, a new design for the air interface is currently being specified in the 3GPP standards body. The Uplink (UL) for LTE is based on Single Carrier Frequency Division Multiple Access. The UL control channel carries non-data associated control signaling like CQI, ACK/NACK, Scheduling request etc. To maintain the low PA power de-rating, the single carrier property of the UL has to be maintained. As such, special consideration should be given to the UL control channel design. This paper discusses in detail the LTE UL control channel design and its performance.

A generalized class of normalized distance functions called Q-Metrics is described in this paper. The Q- Metrics approach relies on a unique functional, using a single bounded parameter λ, which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property of the proposed model, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. We present a formal mathematical proof that Q-Metrics satisfy the standard metric axioms. A novel artificial neural network is completely defined and constructed using Q-Metrics. This new network is shown to outperform a conventional feed forward back propagation network with the same size when tested on real data sets.

A novel class of highly adaptive nonlinear digital aggregation connectives called Q-aggregates is described in this paper. The Q-aggregate approach relies on a unique control parameter lambda to characterize the three conventional fuzzy intersection, union, and averaging aggregation operators. In addition to this universal coverage property of the proposed model, a distinguishing and extremely interesting characteristic of the Q-aggregate connective is that even for a fixed value of the control parameter lambda, a given operator can behave as "more than one" family of the conventional connectives depending on the input values. We present the Q-aggregate in application to a real-valued signal processing task, with an optimization algorithm, so that the parameters of the operators can be tuned automatically. The Q-aggregate operator is tested on electrocardiogram (EKG) data. The experiments show that the proposed model can be used to map input signals to their corresponding target signals through learning.

An advanced approach for adaptive nonlinear digital data processing is described in this article. Three primal computational structures referred to as Q-Measures, Q-Metrics, and Q-Aggregates are introduced and utilized in unison as highly adaptive data analysis handlers. The proposed approach relies on universal functionals using few parameters to characterize dynamic system behaviors in broad ranges of unconventional measure, metric, and aggregation spaces. We present this unique approach in application to real-valued signal processing tasks, with suitable optimization algorithms, so that the parameters of the proposed models can be tuned automatically. The new approach is tested on real data sets and the experiments show promising results.

Radial basis function artificial neural networks using different types of nodes are described in this paper. Two new functions referred to as Q-metrics and Q-aggregates are utilized together to provide highly nonlinear and adaptive node operations. Q-metric distance functions are used in place of the conventional distance functions of the radial basis functions in the hidden layer of the network. Q-aggregate operators are used in the output layer to summarize the information delivered by the hidden layer. Separate classification and regression networks relying on these functions that use simple mathematical formation to characterize dynamic system behaviors in broad ranges of unconventional metric and aggregation spaces are proposed here. We present this approach in application to real-valued signal processing tasks, with suitable optimization algorithms, so that the parameters of the models can be tuned automatically. The new approach has been used to solve real-world problems and it shows promising results.

As the standardization of Evolved-UTRA or LTE is ongoing in 3GPP, it is important to ensure the coexistence between two LTE systems or between LTE and legacy UMTS systems sharing the same frequency band. This paper is aimed at presenting a complete view of the status and challenges of the coexistence issue. The high-level methodology, UL power control, and ACLR modeling, etc. are discussed in detail. To ensure coexistence without tightening the RF requirements and hence increasing the cost for LTE, mitigation techniques like interference-aware resource allocation are proposed and shown to be effective by simulation results.

Comprehensive long term evolution of the Universal Mobile Telecommunications System (UMTS) specifications is currently ongoing to provide significant improvement over the current release. Important goals for the evolved system include significantly improved system capacity and coverage, low latency, reduced operating costs, multi-antenna support, flexible bandwidth operations and seamless integration with existing systems. To ensure low latency, users must be able to establish a connection to the network quickly. This paper provides a preliminary design and procedure for the random access channel used to establish a connection when the mobile is not yet time-synchronized to the network in the uplink.

Evolved UTRA (EUTRA) is currently under study by 3GPP standards organizations as a long term evolution (LTE) for current generation universal terrestrial radio access (UTRA) networks. One of the key aspects of EUTRA wireless systems will be providing voice services in packet domain using voice over IP (VoIP). This paper provides system simulation results evaluating uplink VoIP capacity for EUTRA. Simulations indicate that if efficient control signaling and scheduling mechanisms are in place, EUTRA systems can support 80 to 130 Erlangs of VoIP traffic per sector in a 1.08 MHz spectrum allocation as part of a 5 MHz LTE carrier.

A highly adaptive nonlinear digital filtering technique called the Q-Filter is described. The Q-Filter is defined as a Choquet integral with respect to a Q-Measure over a finite window of observations. In addition to robustness, the advantage of the Q-Filter is that it can behave as a combination of different filters, so that a single Q-Filter can be used instead of applying an expensive sequence of conventional filtering operations. In this paper we present the Q-Filter in application to a real-valued signal processing task, with a regression algorithm, so that the parameters of the filter can be tuned automatically. This algorithm also resolves the signal scaling and shifting issues associated with the direct filtering operation. The Q-Filter model is tested on acoustic heartbeat sound data. The experiments show that the proposed model can be used to map input signals to their corresponding target signals through learning.

Modularity and building blocks have drawn attention from the genetic programming (GP) community for a long time. The results are usually twofold: a hierarchical evolution with adequate building block reuse can accelerate the learning process, but rigidly defined and excessively employed modules may also counteract the expected advantages by confining the reachable search space. In this work, we introduce the concept of emergent loose modules based on a new linear GP system, prefix gene expression programming (P-GEP), in an attempt to balance between the stochastic exploration and the hierarchical construction for the optimal solutions. Emergent loose modules are dynamically produced by the evolution, and are reusable as sub-functions in later generations. The proposed technique is fully illustrated with a simple symbolic regression problem. The initial experimental results suggest it is a flexible approach in identifying the evolved regularity and the emergent loose modules are critical in composing the best solutions

The problem of interestingness of discovered rules has been investigated by many researchers. The issue is that data mining algorithms often generate too many rules, which make it very hard for the user to find the interesting ones. Over the years many techniques have been proposed. However, few have made it to real-life applications. Since August 2004, we have been working on a major application for Motorola. The objective is to find causes of cellular phone call failures from a large amount of usage log data. Class association rules have been shown to be suitable for this type of diagnostic data mining application. We were also able to put several existing interestingness methods to the test, which revealed some major shortcomings. One of the main problems is that most existing methods treat rules individually. However, we discovered that users seldom regard a single rule to be interesting by itself. A rule is only interesting in the context of some other rules. Furthermore, in many cases, each individual rule may not be interesting, but a group of them together can represent an important piece of knowledge. This led us to discover a deficiency of the current rule mining paradigm. Using non-zero minimum support and non-zero minimum confidence eliminates a large amount of context information, which makes rule analysis difficult. This paper proposes a novel approach to deal with all of these issues, which casts rule analysis as OLAP operations and general impression mining. This approach enables the user to explore the knowledge space to find useful knowledge easily and systematically. It also provides a natural framework for visualization. As an evidence of its effectiveness, our system, called Opportunity Map, based on these ideas has been deployed, and it is in daily use in Motorola for finding actionable knowledge from its engineering and other types of data sets.

In this paper, we report a deployed data mining application system for Motorola. Originally, its intended use was for identifying causes of cellular phone failures, but it has been found to be useful for many other engineering data sets as well. For this report, the case study is a dataset containing cellular phone call records. This data set is like any dataset used in classification applications, i.e., with a set of attributes which can be continuous or discrete, and a discrete class attribute. In our application, the classes are normally ended calls, calls which failed to setup, and calls which failed while in progress. However, the task is not to predict any failure, but to identify possible causes that resulted in failures. Then, engineering efforts may focus on improvements that can be made to the phones. In the course of the project, various classification techniques, e.g., decision trees, naïve Bayesian classification and SVM were tried. However, the results were unsatisfactory. After several demonstrations and interaction with domain experts, we finally designed and implemented an effective approach to perform the task. The final system is based on class association rules, general impressions and visualization. The system has been deployed and is in regular use at Motorola. In this paper, we first describe our experiences with some existing classification systems and discuss why they are not suitable for the task. We then present our techniques. As an illustration, we show several visualization screens in the case study, which reveal some important knowledge. Due to confidentiality, we will not give specifics but only present a general discussion about the results.

With the emergence of packet-based wireless broadband systems such as 802.16e, it is evident that a comprehensive evolution of the universal mobile telecommunications system specifications is required to remain competitive. As a result, work has begun on long term evolution (LTE) of the UMTS terrestrial radio access and radio access network aimed for commercial deployment in 2010. Goals for the evolved system include support for improved system capacity and coverage, high peak data rates, low latency, reduced operating costs, multi-antenna support, flexible bandwidth operations and seamless integration with existing systems. To reach these goals, a new design for the air interface is envisioned. This paper provides a preliminary look at the air interface for Evolved UTRA (E-UTRA) and associated key technologies required to reach its design objectives. Initial E-UTRA system performance results show a 2 to 3x improvement over a reference Rel-6 UMTS system configuration [1, 2] for both uplink and downlink.

Multi-user scheduling and OFDM techniques are well regarded as promising candidates to provide high data- rate and reliable transmissions in future communication systems. To optimally exploit multi-user diversity and frequency selectivity, exhaustive channel information is needed for every user - an impracticality given limited feedback bandwidth. Simple and efficient feedback schemes therefore become a crucial task in designing a next generation standard. In this paper, two near-optimal feedback schemes are proposed that achieve most of the multi-user scheduling benefit with very few feedback bits. It is also shown that the feedback schemes are robust to long feedback intervals. As expected, at higher Doppler spread, the performance gap using the proposed schemes becomes larger but still provides satisfactory performance.

Evolved UTRA and UTRAN is being standardized in 3GPP standard group as a long term evolution of the 3GPP radio-access technology. The goal is to achieve 2-4 times the spectral efficiency and user throughput and much smaller latency compared to HSDPA/HSUPA. Single carrier FDMA (e.g. DFT-SOFDM) is the multiple access technique of choice for uplink transmission. Interference control is one of the key elements to achieve the target spectral efficiency and user cell- edge performance requirement, especially for uplink. In this paper, interference mitigation is implemented through slow fractional power control and interference coordination through UE alignment and FDM resource allocation. Simulation results show that these techniques significantly improve uplink sector and cell edge user throughput performance.

HRPD-A adds new forward and reverse link rates and a forward link multi-user packet capability at the MAC layer. Completed simulations reveal the performance characteristics of this system in the 1.9 GHz frequency band. These characteristics consist of capacities of voice/IP traffic and mixed data and voice/IP traffic. Downlink data traffic models consist of FTP, HTTP (Web browsing), WAP, and video streaming models. The simulation assumptions include the ITU channel models and 3GPP2 data traffic models.

Data mining techniques frequently find a large number of patterns or rules, which make it very difficult for a human analyst to interpret the results and to find the truly interesting and actionable rules. Due to the subjective nature of "interestingness", human involvement in the analysis process is crucial. In this paper, we propose a novel visual data mining framework for the purpose of identifying actionable knowledge quickly and easily from discovered rules and data. This framework is called the Opportunity Map. It is inspired by some interesting ideas from Quality Engineering, in particular Quality Function Deployment (QFD) and the House of Quality. It associates summarized data or discovered rules with the application objective using an interactive matrix, which enables the user to quickly identify where the opportunities are. The proposed system can be used to visually analyze discovered rules, and other statistical properties of the data. The user can also interactively group actionable attributes and values, and see how they affect the targets of interest. Combined with drill-down and comparative analysis, the user can analyze rules and data at different levels of detail. The proposed visualization framework thus represents a systematic and yet flexible method of rule analysis. Applications of the system to large-scale data sets from our industrial partner have yielded promising results.

Data mining algorithms usually generate a large number of rules, which may not always be useful to human users. In this project, we propose a novel visual data-mining framework, called Opportunity Map, to identify useful and actionable knowledge quickly and easily from the discovered rules. The framework is inspired by the House of Quality from Quality Function Deployment (QFD) in Quality Engineering. It associates discovered rules, related summarized data and data distributions with the application objective using an interactive matrix. Combined with drill down visualization, integrated visualization of data distribution bars and rules, visualization of trend behaviors, and comparative analysis, the Opportunity Map allows users to analyze rules and data at different levels of detail and quickly identify the actionable knowledge and opportunities. The proposed framework represents a systematic and flexible approach to rule analysis. Applications of the system to large-scale data sets from our industrial partner have yielded promising results.

Linear genotype representation and modularity have continuously received extensive attention from the genetic programming (GP) community. The advantages of a linear genotype include a convenient and efficient implementation scheme. However, most existing techniques using a linear genotype follow the imperative programming language paradigm and a direct hierarchical composition for the functionality of the solution is underachieved. Our work is based on prefix gene expression programming (P-GEP), a new GP method featured by a prefix notation based linear genotype representation. Since P-GEP uses a functional language paradigm, its framework results in natural self-emergence of substructures as functional components during the evolution. We propose to preserve and utilize potentially useful emergent substructures via a dynamic substructure library, empowering the algorithm to focus the search on a higher level of the solution structure. Preliminary experiments on the benchmark regression problems have shown the effectiveness of this approach.

Enhanced Uplink technology is being standardized in Release-6 of 3GPP W-CDMA specifications with the completion date of 2nd quarter of 2005. Advanced features being introduced include adaptive modulation and coding, Hybrid ARQ, fast rate control or scheduling by the base station, higher peak rates (5.76 Mbps) and support of 2ms TTI. These techniques reduce overall delay and increase throughput significantly compared to earlier releases of 3GPP W-CDMA (Release-99/ 4/ 5). As a result, Enhanced Uplink will significantly improve the user mobile experience and enable new services and applications. In this paper, the scheduling and resource allocation aspects of Enhanced Uplink are discussed with accompanying analysis and system simulation results. These results show that Enhanced Uplink improves sector and user throughput by approximately 50-80% compared to earlier 3GPP W-CDMA releases.

The performance of adaptive least squares (LS) filtering is analyzed for the suppression of multiple-access interference. Both full-rank LS filters and reduced-rank LS filters, which reside in a lower dimensional Krylov space, are considered with training, and without training but with known signature for the desired user. We compute the large system limit of output signal-to-interference-plus-noise ratio (SINR) as a function of normalized observations, load, and noise level. Specifically, the number of users K, the degrees of freedom N, and the number of training symbols or observations i all tend to infinity with fixed ratios K/N and i/N. Our results account for an arbitrary power distribution over the users, data windowing (e.g., recursive LS (RLS) with exponential windowing), and initial diagonal loading of the covariance matrix to prevent ill-conditioning. Numerical results show that the large system analysis accurately predicts the simulated convergence performance of the algorithms considered with moderate degrees of freedom (typically N=32). Given a fixed, short training length, the relative performance of full- and reduced-rank filters depends on the selected rank and diagonal loading. With an optimized diagonal loading factor, the performance of full- and reduced-rank filters are similar. However, full-rank performance is generally much more sensitive to the choice of diagonal loading factor than reduced-rank performance.

The optimal power allocation scheme is investigated with random spreading sequences and linear MMSE receivers in the regime of large system. The main result is the identification of a power allocation and admission control scheme that maximizes the sum capacity

We analyze the throughput of a combined hybrid-ARQ and link adaptive packet data transmission system. An analytical approach is used to develop a method for selecting transmit data rates that will maximize system throughput based on available channel state information and the maximum number of allowable transmissions. Performance of the system in the presence of channel estimation errors is also analyzed. Our results show that the availability of channel state information improves system performance significantly, especially under slowly fading channels or when the maximum number of transmissions allowed is small. We also show that, at low SNR, significant gain can be achieved by incorporating the knowledge of TV, the maximum allowable number of transmissions, into our data rate selection process. In addition, comparison to simulation results demonstrates that this approach can also be used to accurately evaluate performance of practical systems.

In this paper, we study the optimal detection of QAM signaling with channel estimation error. Optimal detectors, maximum likelihood (MaxLike) detector and a modified minimum distance (ModDist) detector, are derived for the case a known Gaussian channel distribution and unknown channel distribution, respectively. These detectors differ from the traditional minimum distance detector, which ignores the effect of channel estimation error. Soft decision metrics for coded performance are also generated using the soft output of these detectors based on generalized likelihood ratio (GLR). Numerical results are given to compare the performance of these detectors. Although only marginal improvement is observed when using the proposed detectors without coding, while significant gains are obtained when used together with coding.

Classification is one of the fundamental tasks of data mining. Most rule induction and decision tree algorithms perform a local, greedy search to generate classification rules that are often more complex than necessary. Evolutionary algorithms for pattern classification have recently received increased attention because they can perform global searches. In this paper, we propose a new approach for discovering classification rules by using gene expression programming (GEP), a new technique of genetic programming (GP) with linear representation. The antecedent of discovered rules may involve many different combinations of attributes. To guide the search process, we suggest a fitness function considering both the rule consistency gain and completeness. A multiclass classification problem is formulated as multiple two-class problems by using the one-against-all learning method. The covering strategy is applied to learn multiple rules if applicable for each class. Compact rule sets are subsequently evolved using a two-phase pruning method based on the minimum description length (MDL) principle and the integration theory. Our approach is also noise tolerant and able to deal with both numeric and nominal attributes. Experiments with several benchmark data sets have shown up to 20% improvement in validation accuracy, compared with C4.5 algorithms. Furthermore, the proposed GEP approach is more efficient and tends to generate shorter solutions compared with canonical tree-based GP classifiers.

In this paper, we introduce a new class of fuzzy measures, referred to as q-measures, based on a modification of the definition of the well-known Sugeno /spl lambda/-measure. Our proposed definition of the q-measures not only includes the /spl lambda/-measure as a special case, but also preserves all desirable properties and avoids some of the limitations of the conventional /spl lambda/-measure. The q-measure approach provides a more flexible and powerful method for constructing various fuzzy measures. We provide an iterative algorithm for constructing an interesting sequence of q-measures and analytically prove its convergence as a distinguishing characteristic of the proposed formulation.

The channel code bit log likelihood ratio (LLR) for soft decision decoding is derived for quadrature amplitude modulated signals (QAM). The effect of imperfect channel knowledge on soft decision decoding performance is studied. Our results indicate this effect increases with channel estimation error and/or QAM modulation level. A metric based on generalized log likelihood ratio (GLLR) is derived for soft decision channel decoding with imperfect channel knowledge. Numerical results show that the GLLR-based metric outperforms the conventional minimum distance decoding metric that does not take into account channel estimation error.

Wideband direct-sequence (DS)-code-division multiple-access (CDMA) is a strong candidate for both terrestrial and satellite components of UMTS. The forward-link capacity of a satellite DS-CDMA system with a conventional matched filter (MF) receiver is limited by interference from adjacent beams and possibly overlapping beams from multiple satellites. In this paper, we study the performance of the linear minimum mean squared error (MMSE) receiver for the satellite forward link. System constraints are long propagation delay, which prevents accurate closed-loop power control, and low on-board power consumption, which implies a low received bit energy to noise density ratio at the mobile receiver. We consider a "one-step" power adjustment algorithm which attempts to compensate for random shadowing and path loss, and compare the associated performance of the MMSE and MF receivers. Dual-satellite diversity is also considered. The effect of code rate on performance is studied through the use of punctured convolutional codes and the evaluation of random coding bounds. Our results indicate that linear MMSE interference suppression can improve the quality of service and increase system capacity significantly.

The performance of reduced-rank linear filtering is studied for the suppression of multiple-access interference. A reduced-rank filter resides in a lower dimensional space, relative to the full-rank filter, which enables faster convergence and tracking. We evaluate the large system output signal-to-interference plus noise ratio (SINR) as a function of filter rank D for the multistage Wiener filter (MSWF) presented by Goldstein and Reed. The large system limit is defined by letting the number of users K and the number of dimensions N tend to infinity with K/N fixed. For the case where all users are received with the same power, the reduced-rank SINR converges to the full-rank SINR as a continued fraction. An important conclusion from this analysis is that the rank D needed to achieve a desired output SINR does not scale with system size. Numerical results show that D=8 is sufficient to achieve near-full-rank performance even under heavy loads (K/N=1). We also evaluate the large system output SINR for other reduced-rank methods, namely, principal components and cross-spectral, which are based on an eigendecomposition of the input covariance matrix, and partial despreading. For those methods, the large system limit lets D/spl rarr//spl infin/ with D/N fixed. Our results show that for large systems, the MSWF allows a dramatic reduction in rank relative to the other techniques considered.