Graph Neural Networks (GNNs) can capture the interaction in a wireless network and offer an effective method to optimize beamforming in wireless networks. Prevailing GNN methods focus mainly on node updates, whereas an edge feature can directly represent beamforming between a base station node and a user node. We propose a novel and computationally efficient GNN architecture that jointly updates edge and node representations to maximize the sum-rate under various power allocation strategies, making it applicable to a wide range of wireless network scenarios. Implemented for the cooperative beam-forming among multiple base stations setting, results show that the proposed GNN outperforms existing GNNs and WMMSE by achieving a higher network sum-rate, approaching the dirty-paper-coding capacity. Furthermore, our GNN structure reduces the runtime by half of the state-of-art GNN and by two orders of magnitude compared to WMMSE.

The integration of a wireless backhaul network between a macro-cell base station (MBS) and multiple small-cell base stations (SBSs) with full-duplex (FD) technology at the SBSs offers significant potential to enhance spectrum efficiency in millimeter-wave (mmWave) communication systems. However, the FD mode also introduces an inherent trade-off between better time utilization and increased network interference, necessitating careful optimization of duplex modes at each SBS. In this paper, we propose a novel framework that jointly optimizes both the duplex mode selection for each SBS and the design of hybrid beamforming (BF) at the MBS and SBSs to maximize network throughput. Our framework alternately tackles two key subproblems: hybrid BF design and SBS duplex mode selection. For hybrid BF design, we employ a two-stage approach that combines a codebook-based radio frequency beam selection via a greedy algorithm with baseband BF optimization through a generalized power iteration method. For SBS duplex mode selection, we introduce the worst-mode switching (WMS) algorithm to efficiently find a near-optimal solution in polynomial time. Simulation results demonstrate that the proposed joint algorithm not only has low computational complexity but is also robust to various interference scenarios, including different ADC resolutions and varying levels of residual self-interference suppression. This adaptability results in superior sum-rate performance compared to conventional hybrid BF schemes and configurations where all SBSs operate exclusively in either full-duplex or half-duplex mode.

We propose a novel graph neural network (GNN) architecture for jointly optimizing user association, base station (BS) beamforming, and reconfigurable intelligent surface (RIS) phase shift in a multi-RIS aided multi-cell network. The proposed architecture represents BSs and users as nodes in a bipartite graph where the same type of nodes shares the same neural networks for generating messages and updating its representations, allowing for distributed implementation. In addition, we utilize a composite reflected channel estimation integrated between layers of the GNN structure to significantly reduce the signaling overhead and complexity required for channel estimation in a multi-RIS network. To avoid BS overload, load balancing is regularized in the training of the GNN and we further develop a collision avoidance algorithm to ensure strict load balancing at every BS. Numerical results show that the proposed GNN architecture is significantly more efficient than existing approaches. The results further demonstrate its strong scalability with network size and achieving a throughput performance approaching that of a centralized traditional optimization algorithm, without requiring individual RIS-reflected channels estimation and without the need for re-training or fine-tuning.

Previous studies have examined the syntactic capabilities of large pre-trained language models, such as BERT, by using stimuli from psycholinguistic studies.Studying well-known processing errors, such as Negative Polarity Item (NPI) illusive effects can reveal whether a model prioritizes linear or hierarchical information when processing language.Recent experiments have found that BERT is mildly susceptible to NPI illusion effects (Shin et al., 2023;Vu and Lee, 2022).We expand on these results by examining the effect of distance on the illusive effect, using and modifying stimuli from Parker and Phillips (2016).We also further tease apart whether the model is more affected by hierarchical distance or linear distance.We find that BERT is highly sensitive to syntactic hierarchical information: added hierarchical layers affected its processing capabilities compared to added linear distance.

As next generation cellular networks become denser, associating users with the optimal base stations at each time while ensuring no base station is overloaded becomes critical for achieving stable and high network performance. We propose multi-agent online Q-learning (QL) algorithms for performing real-time load balancing user association and handover in dense cellular networks. The load balancing constraints at all base stations couple the actions of user agents, and we propose two multi-agent action selection policies, one centralized and one distributed, to satisfy load balancing at every learning step. In the centralized policy, the actions of UEs are determined by a central load balancer (CLB) running an algorithm based on swapping the worst connection to maximize the total learning reward. In the distributed policy, each UE takes an action based on its local information by participating in a distributed matching game with the BSs to maximize the local reward. We then integrate these action selection policies into an online QL algorithm that adapts in real-time to network dynamics including channel variations and user mobility, using a reward function that considers a handover cost to reduce handover frequency. The proposed multi-agent QL algorithm features low-complexity and fast convergence, outperforming 3GPP max-SINR association. Both policies adapt well to network dynamics at various UE speed profiles from walking, running, to biking and suburban driving, illustrating their robustness and real-time adaptability.

The integration of millimeter wave and higher frequencies into 5G and 6G networks raises concerns about potential conflicts with existing satellite services that operate in the same or adjacent frequency bands. This paper analyzes the co-channel interference and out-of-band (OOB) leakage power from terrestrial networks to satellites, and offers design criteria for terrestrial networks to protect existing satellite services. Specifically, we establish the power spectral density of a multicarrier transmitted signal, enabling us to derive the in-band and OOB emission powers for arbitrary pulse shaping and interpolation filters employed in a terrestrial transmitter. We then establish the cumulative distribution function (CDF) of the aggregated interference from terrestrial networks to a satellite receiver using stochastic geometry tools. Based on this CDF, we derive closed-form expressions that impose limits on terrestrial node density and spectrum emission masks, ensuring a near-zero satellite outage probability. By defining an interference threshold based on satellite protection criteria, our analysis enables the identification of an optimal trade-off between terrestrial node density and transmit power, providing robust theoretical guidance for formulating regulations pertaining to terrestrial networks. Extensive simulations validate our analysis results, demonstrating as an example the feasibility of cellular coexistence with LEO satellites in the 47.2-50.2 GHz band. Our approach effectively controls both in-band interference from a terrestrial network to fixed satellite services and OOB interference to passive Earth exploration satellite services in the adjacent band, while satisfying the required satellite interference thresholds.

We consider a novel Hybrid Reconfigurable Intelligent Surface (HRIS) structure with a limited number of RF ports. This structure is different from the commonly considered hybrid surface with a few active elements, where each active element is connected directly to a separate RF port. Instead, in the new structure, the wireless signal impinging on every element propagates along the surface to each RF port, which receives a linear combination of signals from all elements. Using only a few RF ports, we propose a novel compressive sensing (CS) algorithm to jointly estimate all direct channels between multiple multi-antenna devices at the HRIS, without requiring orthogonal pilots among the devices. The proposed algorithm uses a beamspace based CS approach to estimate both the angles and the complex path gains of a sparse number of propagation paths. Simulation results show that our algorithm achieves high estimation accuracy, especially for multi-antenna devices, using only a few RF ports and short pilot sequences. This better channel estimation accuracy translates to higher spectral efficiency when using the HRIS for reflection. Our results demonstrate the merits of adding a few RF ports at the intelligent surface.

We study the joint optimization of beamforming, RIS phase shift, and association for the links of BS-user and RIS-user communications in a multi-cell wireless network aided by multiple RISs. Consider a network setting with many RISs, we can optimize the reflection of each RIS for a single associated user, even though the RIS will reflect the signals of all users. We first design the optimal BS transmit beamforming together with the phase shift of RIS in a closed form to maximize the effective channel gain (ECG). Then, we design two different BS-RIS-user association algorithms satisfying the load balancing constraint. The first algorithm uses worst connection swapping on both the BS-user and RIS-user links, whereas the second algorithm uses a simpler max-ECG rule for the RIS-user link because of no load balancing at the RIS. A joint algorithm alternates between BS-RIS-user association and beamforming/reflection design until convergence. The proposed algorithms not only have substantially lower complexity than existing algorithms, but also outperforms the conventional max SINR association and effectively exploits multiple RISs to boost the network sum rate.

We introduce a novel algorithm for ride-hailing services, called Predictive Leafcutter Ant optimization for Networks (PLAN), that is shown to reduce vehicular fuel costs and customer wait times, especially for clustered requests. PLAN combines traditional ant colony optimization, network flow optimization, and task-partitioning inspired by leafcutter ant foraging patterns. At a high level, PLAN integrates these methods to predictively station vehicles at locations of high activity (hot spots) to minimize both customer wait times and vehicle dispatch costs and to selectively disregard requests if they exceed a dynamic cost-to-benefit ratio. The algorithm is tested on a modeled city with simulated ride requests as well as on real-world New York City commuter data. The performance of PLAN is compared to that of a control algorithm, and the conditions under which PLAN outperforms the control algorithm are identified. Results show that for the New York City data with the presence of quantifiably significant hot spots, PLAN can decrease customer wait times by up to 36% (and proportionally dispatch fuel costs) in a test case with a limited service fleet and produce higher reductions with larger fleets.

Using a multi-armed bandit technique, we propose centralized and semi-distributed online algorithms for load balancing user association and handover in mmWave-enabled networks. Load balancing at all base stations (BSs) imposes explicit constraints that makes the actions of all user equipment (UEs) co-dependent, a challenging twist to reinforcement learning. We propose a central load balancer to guarantee load balancing at all BSs for every learning step. We consider two association vectors: one for leaning update, and one best-to-date for data transmission, allowing UEs to engage in best-result data transmission while effectively participating in a background learning process indefinitely. For dynamic networks, we introduce a measurement model capturing rapid channel variations and user mobility. To minimize handover rate, we also differentiate between the handover cost for transmission and that for learning, and introduce a learning handover cost decreasing with sojourn time. The proposed algorithms can be implemented online as they require no offline training and can effectively adapt to network dynamics. Numerical results show that the proposed algorithms exhibit fast learning convergence and outperform 3GPP handover by achieving an order of magnitude lower handover rate at a significantly higher network sum-rate, reaching within 94-97% of the near-optimal worst connection swapping benchmark algorithm.

The widespread deployment of IoT systems in the real world today has enabled the generation and collection of an enormous amount of sensor times series. One of the important mining techniques to extract patterns from time series is temporal pattern mining (TPM). Unlike the sequential pattern mining, TPM adds an additional temporal dimension, i.e., time intervals, into extracted patterns, making them more informative. However, adding the extra temporal dimension into patterns results in an additional exponential factor to the growth of the search space, and thus, significantly increases the mining complexity. Current TPM approaches work sequentially, therefore, cannot scale to large datasets. In this paper, we propose Distributed Hierarchical Pattern Graph TPM (DHPG-TPM), the first distributed solution that supports large-scale TPM using the leading distributed platform Apache Spark. Moreover, DHPG-TPM employs efficient data structures, distributed bitmap and distributed Hierarchical Pattern Graph that are carefully designed to work efficiently in a distributed environment to enable fast computations of support and confidence. To address the exponential search space of TPM, we design effective distributed pruning techniques based on the Apriori principle and the transitivity property of temporal relations to reduce the search space while minimizing the communication overhead between the cluster nodes. We conduct extensive experiments on real-world and synthetic datasets, showing that DHPG-TPM outperforms the sequential baselines and scales to very large datasets.

We consider a mobile network consisting of both the wireless access network and the backhaul network. All base stations in the access network and gateways in the backhaul network are equipped with caches, so that routing costs for serving content requests can be reduced by caching the requested content items closer to the users. In this case, user association in the wireless access network must be aware of both the quality of wireless channels and the content caching strategy. In this paper, we propose a framework that jointly optimizes wireless user association and content caching in both access and backhaul networks. The resulting problem is NP-hard. We propose a polynomial-time algorithm based on convex approximation and pipage rounding that produces a solution within a constant factor of 1 − 1/e from the optimal. Simulation results show that the proposed joint algorithm outperforms schemes that combine cache-independent user association methods with traditional caching strategies (e.g. LRU) in terms of minimizing the aggregate routing cost and backhaul traffic while achieving a high data sum rate in the access network.

In this paper, we introduce a new load balancing user association scheme for millimeter wave (mmWave) cellular networks in which intelligent reflecting surface (IRS) is applied in the cellular network to improve the coverage region of each cell and mitigate mmWave vulnerability to non-line of sight (N-LoS) paths. The user association scheme improves network performance significantly by adjusting the interference according to the association. We study the IRS-assisted mmWave cellular network where one IRS is deployed to assist in the communication from the base station (BS) to mobile users (MUs) in each cell. We balance BS loads and maximize a network utility by optimizing the user association with a matching game. Simulation results show that the proposed scheme significantly improves the throughput compared to conventional user association techniques.

Recent development in computing, sensing and crowd-sourced data have resulted in an explosion in the availability of quantitative information. The possibilities of analyzing this so-called Big Data to inform research and the decision-making process are virtually endless. In general, analyses have to be done across multiple data sets in order to bring out the most value of Big Data. A first important step is to identify temporal correlations between data sets. Given the characteristics of Big Data in terms of volume and velocity, techniques that identify correlations not only need to be fast and scalable, but also need to help users in ordering the correlations across temporal scales so that they can focus on important relationships. In this paper, we present AMIC (Adaptive Mutual Information-based Correlation), a method based on mutual information to identify correlations at multiple temporal scales in large time series. Discovered correlations are suggested to users in an order based on the strength of the relationships. Our method supports an adaptive streaming technique that minimizes duplicated computation and is implemented on top of Apache Spark for scalability. We also provide a comprehensive evaluation on the effectiveness and the scalability of AMIC using both synthetic and real-world data sets.

We study distributed user association in 5G and beyond millimeter-wave enabled heterogeneous networks using matching theory. We propose a novel and efficient distributed matching game, calledearly acceptance(EA), which allows users to apply for association with their ranked-preference base station in a distributed fashion and get accepted as soon as they are in the base station’s preference list with available quota. Several variants of the EA matching game with preference list updating and reapplying are compared with the original and stability-optimal deferred acceptance (DA) matching game, which implements a waiting list at each base station and delays user association until the game finishes. We show that matching stability needs not lead to optimal performance in other metrics such as throughput. Analysis and simulations show that compared to DA, the proposed EA matching games achieve higher network throughput while exhibiting a significantly faster association process. Furthermore, the EA games either playing once or multiple times can reach closely the network utility of a centralized user association while having much lower complexity.

This paper introduces a low complexity and optimum decoding algorithm for tail-biting codes. The algorithm, called Selective Initialization Viterbi Algorithm (SIVA), performs the Viterbi algorithm (VA) iteratively and assigns at each iteration the initial costs for a selective set of states to satisfy a necessary condition. The process of selecting the set of states and setting their initial costs is done by forming a directed acyclic graph among the candidate states. We prove the convergence and optimality of SIVA and analyze its complexity in terms of the number of operations for the worst-case scenario with a noise-like decoder input. SIVA achieves optimum decoding at a complexity comparable to the popular, yet sub-optimal, wrap-around Viterbi algorithm (WAVA) and at several orders of magnitude lower complexity compared to other optimal tail-biting decoding algorithms. Application of SIVA to a practical millimeter wave MIMO system with two different tail-biting codes illustrates URLLC-regime frame error performance better than WAVA and confirms the low complexity of the proposed algorithm.

Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user-devices. We consider a multi-access edge computing (MEC) system with collocated MEC server and base-station/access point (AP), each equipped with a massive MIMO antenna array, supporting multiple users requesting data computation and wireless charging. The goal is to minimize the energy consumption for computation offloading and maximize the received energy at the user from wireless charging. The proposed solution is a novel two-stage algorithm employing a nested descent algorithm, primal-dual subgradient and linear programming techniques to perform data partitioning and time allocation for computation offloading and design the optimal energy beamforming for wireless charging, all within MEC-AP transmit power and latency constraints. Algorithm results show that optimal energy beamforming significantly outperforms other schemes such as isotropic or directed charging without beam power allocation. Compared to binary offloading, data partition in partial offloading leads to lower energy consumption and more charging time, resulting in better wireless charging performance. The charged energy over an extended period of multiple time-slots both with and without computation offloading can be substantial. Wireless charging from MEC-AP thus offers a viable untethered approach for supplying energy to user-devices.

Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights and values can be obtained from these time series through performing cross-domain analyses, one of which is analyzing time delay temporal correlations across different datasets. Most existing works in this area are either limited in the type of detected relations, e.g., linear relations alone, only working with a fixed temporal scale, or not considering time delay between time series. This paper presents our Time delaY COrrelation Search (TYCOS) approach which provides a powerful and robust solution with the following features: (1) TYCOS is based on the concept of mutual information (MI) from information theory, giving it a strong theoretical foundation to detect all types of relations including non-linear ones, (2) TYCOS is able to discover time delay correlations at multiple temporal scales, (3) TYCOS works in an efficient, bottom-up fashion, pruning non-interesting time intervals from the search by employing a novel MI-based noise theory, and (4) TYCOS is designed to efficiently minimize computational redundancy. A comprehensive experimental evaluation using synthetic and real-world datasets from the energy and smart city domains shows that TYCOS is able to find significant time delay correlations across different time intervals among big time series. The performance evaluation shows that TYCOS can scale to large datasets, and achieve an average speedup of 2 to 3 orders of magnitude compared to the baselines by using the proposed optimizations.

Using a reinforcement learning multi-armed bandit (MAB) technique, we design a centralized and a semi-distributed online algorithms, for performing load balancing user association in multi-tier heterogeneous cellular networks. The proposed algorithms guarantee user association solutions that satisfy load balancing constraints among the base stations (BSs) by employing a central load balancer (CLB). At each time step, these algorithms provide real-time associations which give the best-to-date network spectral efficiency. In the centralized approach, the CLB performs base station assignments which determine the action for each user equipment (UE) to update its reward. In the semi-distributed approach, each UE proposes an association action based on its local information and communicates with the BS for an associated reward. Numerical results show that the proposed MAB-based algorithms exhibit fast convergence and reach closely a near-optimal benchmark centralized solution.

We introduce a process for designing the optimal cyclic redundancy check (CRC) code for each input of a given κinput convolutional code (κ ≥ 1). Using the free distance on each input and considering that each input sequence can correspond to multiple error events in a κ-input CC, the process efficiently narrows down from the set of polynomials with the same degree the best CRC that provides the minimum frame error rate (FER) for each input. We also extend the efficient and low complexity serial list Viterbi algorithm (SLVA) for single-input CCs in [1] to the κ-input case. We discuss different ways of integrating CRCs in a κ-input CC and derive the truncated union bound on the FER for each input. Numerical examples on a two-input CC illustrate the effectiveness of the proposed CRC design and SLVA decoder for κ-input CCs.

We study a wireless edge-computing system which allows multiple users to simultaneously offload computation-intensive tasks to multiple massive-MIMO access points, each with a collocated multi-access edge computing (MEC) server. Massive-MIMO enables simultaneous uplink transmissions from all users, significantly shortening the data offloading time compared to sequential protocols, and makes the three phases of data offloading, computing, and downloading have comparable durations. Based on this three-phase structure, we formulate a novel problem to minimize a weighted sum of the energy consumption at both the users and the MEC server under a round-trip latency constraint, using a combination of data partitioning, transmit power control and CPU frequency scaling at both the user and server ends. We design a novel nested algorithm consisting of an inner primal-dual algorithm and an outer latency-aware descent algorithm to solve this problem efficiently. Optimized solutions show that for larger requests, more data is offloaded to the MECs to reduce local computation time in order to meet the latency constraint, despite higher energy cost of wireless transmissions. Massive-MIMO channel estimation errors under pilot contamination also causes more data to be offloaded to the MECs. Compared to binary offloading, partial offloading with data partitioning is superior and leads to significant reduction in the overall energy consumption.

We examine the use of matching theory for user association in millimeter wave (mmWave)-enabled cellular heterogeneous networks. In a mmWave system, the channel variations can be fast and unpredictable, rendering centralized user association potentially inefficient. We propose an efficient distributed matching algorithm, called early acceptance (EA), tailored for user association in 5G HetNets. The effectiveness of the proposed algorithm is assessed by comparing with the well-known deferred acceptance (DA) matching algorithm, in which user association is delayed until the algorithm terminates. Numerical results show that the proposed distributed EA matching algorithm reaches a near-optimal solution compared to a centralized algorithm, and leads to a more power-efficient and much faster user association process compared to the distributed DA algorithm.

This work introduces a priority-based coding scheme for unequal error protection in the AWGN channel using short messages. The scheme simultaneously encodes two messages, one with high and one with low priority, both requiring short blocklengths. The code structure allows the transmission of the higher priority message with higher reliability even at a shorter blocklength. This allows a high- priority, urgent message to be sent at high reliability without interrupting a lower priority message that may have been in transmission. We derive tight analytical upper bounds on the error probabilities of these messages. Numerical results confirm better reliability and delay performance for the high priority message, and also verify the tightness of the analytical upper bound especially as the SNR increases even with very short blocklengths. The scheme is applicable to ultra-reliable and ultra-low latency communication in 5G systems as well as prioritized and delay-sensitive applications such as emergency and vehicular safety communications.

We present a comprehensive Multi-access Edge Computing (MEC) system model with massive Multiple Input Multiple Output (MIMO) access points, each with an integrated edge computing server to allow multiple users to simultaneously offload computationally intensive tasks. We formulate the problem of energy-efficient partial computation offloading under a round trip latency constraint including data offloading time, computation time, and downloading time. A novel and efficient algorithm is designed to minimize a weighted sum of the energy consumed at both the users and the MEC server under a maximum latency constraint, using a combination of power control, partial data offloading, and frequency scaling at both the user and server ends. Numerical results verify how an optimal balance between data offloaded and computed locally is necessary to meet the latency requirement, while minimizing the energy consumption for both wireless transmission and computation. These results also show that with massive MIMO, the times spent for data offloading, computation, and downloading are comparable and grow almost linearly with the amount of data to be computed.

Recent developments in computing and IoT technology have enabled the daily generation of enormous amounts of time series data. These time series have to be analyzed to create value. A fundamental type of analysis is to find temporal correlations between given sets of time series. To provide a robust method for solving this problem, several properties are desirable. First, the method should have a strong theoretical foundation. Second, since temporal correlations can occur at different temporal scales, e.g., sub-second versus weekly, it is important that the method is capable of discovering multitemporal scale correlations. Finally, the method should be efficient and scalable. This paper presents an approach to search for synchronous correlations in big time series that displays all three properties: the proposed method (i) utilizes the metric of mutual information from information theory, providing a strong theoretical foundation, (ii) is able to discover correlations at multiple temporal scales, and (iii) works in an efficient, bottom-up fashion, making it scalable to large datasets. Our experiments verify that the proposed approach can identify various types of correlation relations across multiple temporal scales, while achieving a performance of an order of magnitude faster than the state-of-the-art techniques.

This paper investigates wireless information and power transfer in a full-duplex MIMO relay channel where the self-sustained relay harvests energy from both source transmit signal and self-interference signal to decode and forward source information to a destination. We formulate a new problem to jointly optimize power splitting at the relay and precoding design for both the source and relay transmissions. Using duality theory, we establish closed-form optimal primal solutions in terms of the dual variables, based on which we then design a customized and efficient primal-dual algorithm to maximize the achievable throughput. Numerical results demonstrate the rate gains from using multiple transmit and receive antennas in both information decoding and energy harvesting, and the significant benefit of harvesting energy from self-interference signals. We also extend our analysis to the case when channel state information is only available at receiving nodes and show how our algorithm can optimize the power splitting at the relay for it to remain self-sustained. Through analysis and simulation, we demonstrate that an optimal combination of non-uniform power splitting, variable power allocation, and self-interference power harvesting can effectively exploit a full-duplex MIMO system to achieve significant performance gains over existing uniform power splitting and half-duplex transmissions.

User association is necessary in dense millimeter wave (mmWave) networks to determine which base station a user connects to in order to balance base station loads and maximize a network utility. Given that mmWave connections are highly directional and vulnerable to small channel variations, user association changes these connections and hence significantly affects the network interference and consequently the users' instantaneous rates. In this paper, we introduce a new load balancing user association scheme for mmWave MIMO cellular networks which consider these dependencies. We formulate the user association problem as mixed integer nonlinear programming and design a polynomial-time algorithm, called worst connection swapping (WCS), to find a near-optimal solution. Simulation results confirm that the proposed user association scheme improves network performance significantly by adjusting the interference according to the association, and under the max-min fairness, also enhances cell-edge users' transmission rates. We also show how the proposed algorithm can be applied under mobility. Furthermore, the proposed WCS algorithm outperforms other generic algorithms for combinatorial programming such as the genetic algorithm in both accuracy and speed at several orders of magnitude faster, and for small networks, where exhaustive search is possible, it reaches the optimal solution.

User association determines which base stations a user connects to, hence affecting the amount of network interference and consequently the network throughput. Conventional user association schemes, however, assume that user instantaneous rates are independent of user association. In this paper, we introduce a new load-aware user association scheme for millimeter wave (mmWave) MIMO networks which takes into account the dependency of network interference on user association. This consideration is well suited for mmWave communications, where the links are highly directional and vulnerable to small channel variations. We formulate our user association problem as a mixed integer nonlinear programming (MINLP) and solve it using the genetic algorithm. We show that the proposed method can improve network performance by moving the traffic of congested base stations to lightly-loaded base stations and adjusting the interference accordingly. Our simulations confirm that our scheme results in a higher network throughput compared to conventional user association techniques.

We design user-assisted relaying strategies and analyze their performance in a multiple-input-multiple-output uplink cellular network. In user-assisted relaying, the base station serves an active user equipment (UE) using a transmission scheme adaptive between direct transmission and relaying via another UE according to a cooperation strategy. Modeling the network based on stochastic geometry and Poisson point processes, we propose a practical, geometric-based cooperation strategy determining how to select the relaying UE and when to perform relaying transmission. The proposed relaying transmission employs a simple transmit beamforming and one of two optimized receive combining schemes depending on interference awareness. The extra interference generated by relaying UEs is captured in an interference model that accounts for the adaptive transmission and random locations of these UEs. Integrating the proposed cooperation strategy, transmit and receive beamforming design, and interference model, we analyze the network outage rate performance. Provided sufficient density of potential relay UEs, results demonstrate user-assisted relaying to be most beneficial for cell edge UEs when the relaying UEs are equipped with multiple antenna elements and their signals propagate in relatively high shadowing environments.

We propose a composite decode-forward (DF) scheme for the two-way relay channel in the full-duplex mode by combining coherent, independent, and partial relaying strategies. The relay partially decodes each user's information in each block and forwards this information coherently with the source user to the destination user in the next block as in block Markov coding. In addition, the relay independently broadcasts a binning index of both users' decoded information parts in the next block as in independent network coding. Each technique has a different impact on the relay power usage and the rate region. We further consider the independent and partial DF scheme for its more practical channel state information requirements, and derive in closed-form link regimes when this scheme achieves a strictly larger rate region than just time sharing between its constituent techniques, direct transmission, and independent DF relaying, and when it reduces to a simpler scheme. The analytical approach is based on maximizing the weighted composite DF sum rate and comparing with the outermost time-sharing line connecting corner points of rate regions of the constituent techniques. Numerical results demonstrate significant rate gains by performing link adaptation of the composite scheme based on the identified link regimes.

We propose analytical models for the interference power distribution in a cellular system employing MIMO beamforming in rich and limited scattering environments, which capture non line-of-sight signal propagation in the microwave and mm-wave bands, respectively. Two candidate models are considered: the inverse Gaussian and the inverse Weibull, both are two-parameter heavy tail distributions. We further propose a mixture of these two distributions as a model with three parameters. To estimate the parameters of these distributions, three approaches are used: moment matching, individual distribution maximum likelihood estimation (MLE), and mixture distribution MLE with a designed expectation maximization algorithm. We then introduce simple fitted functions for the mixture model parameters as polynomials of the channel path loss exponent and shadowing variance. To measure the goodness of these models, the information-theoretic metric relative entropy is used to capture the distance from the model distribution to a reference one. The interference models are tested against data obtained by simulating a cellular network based on stochastic geometry. The results show that the three-parameter mixture model offers remarkably good fit to simulated interference power. The mixture model is further used to analyze the capacity of a cellular network employing joint transmit and receive beamforming and confirms a good fit with simulation.

By relying on a wireless backhaul link, relay stations enhance the performance of cellular networks by achieving the required reliability at a savings of infrastructure cost and energy, but at the same time, they can aggravate the interference issue. In this paper, we analyze the maximum energy gain provided by relays for several coding schemes, including energy-optimized partial decode-forward relaying, accounting for the additional relay-generated interference to neighboring cells. First, we define new energy-efficient service areas for relaying in log-normal shadowing environments and propose easily computable and tractable models to predict: 1) the probability of energy-efficient relaying; 2) the spatial distribution of energy consumption within a cell; and 3) the average interference generated by relays. These models allow finding the optimal location and the number of relays with significantly lower complexity and execution time, as compared with system simulations. Finally, we analyze the gains provided by more advanced relaying coding schemes and propose a map showing how to use them across a cell, as a function of their respective circuitry consumption. The inclusion of more advanced relaying schemes not only alleviates the interference issue, but also leads to a reduction in the number of relays required for the same rate and outage performance.

In the past two decades, new developments in computing, sensing and crowdsourced data have resulted in an explosion in the availability of quantitative information. The possibilities of analyzing this so-called “big data” to inform research and the decision-making process are virtually endless. In general analyses have to be done across multiple data sets in order to bring out the most value of big data. A first important step is to identify temporal correlations between data sets. Given the characteristics of big data in term of volume and velocity, techniques that identify correlations not only need to be scalable, but also need to help users in ordering the correlation across temporal resolutions so that they can focus on important relationships. There is a large body of work in this area, however, most of them either only deal with small data sets, using a fixed temporal resolution, or does not provide a quantifiable measure of a correlation significance. In this paper, we present a method based on mutual information to identify correlations in large data sets. Discovered correlations are suggested to users in an order based on their significance. Our method supports an adaptive streaming technique that minimizes duplicated computation and is implemented on top of Apache Spark for scalability using big data platforms. We also provide a comprehensive evaluation using real-world data sets from NYC Open Data, and compare our findings against a recent study.

We propose a novel model for the NLOS interference power distribution in a cellular system employing MIMO beamforming transmission in mmWave spectrum. The proposed model is a mixture of the Inverse Gaussian and the Inverse Weibull distributions, both are two-parameter medium-to-heavy tail distributions. To estimate the parameters of the mixture model, we design an expectation maximization algorithm using both analytical moment matching and maximum likelihood estimation. Further, the information-theoretic metric relative entropy is used to measure the goodness of the model by capturing the distance from the modeled distribution to a reference one. The model is tested against simulation data obtained from a stochastic geometry based cellular network and demonstrates very good fit for a wide range of practical mmWave path loss and shadowing values. The mixture model is then used to analyze the capacity of a cellular network employing joint dominant mode beamforming and again confirms a good fit with simulation.

In this paper, we examine message prioritization in terms of both rate and reliability in the two-way relay channel using decode-forward relaying. Each source sends both a low priority message and a high priority message. We design a scheme that routes the high priority messages through the relay and direct link but the low priority message is only decoded by the destination. For fixed message priorities, the rate-optimal power allocation is analytically determined based on the link state. The high priority message is guaranteed to have greater reliability than that of the low priority message. We analytically formulate this outage probability from both the perspective of a single user and that of the overall system, and numerically examine the increased reliability for the high priority message.

In this paper, we re-examine the relay channel under the decode-forward (DF) strategy. Contrary to the established belief that block Markov coding is always the rate-optimal DF strategy, under certain channel conditions (a link regime), independent signaling between the source and relay achieves the same transmission rate without requiring coherent channel phase information. Furthermore, this independent signaling regime allows the relay to conserve power. As such, we design a composite DF relaying strategy that achieves the same rate as block Markov DF but with less required relay power. The finding is attractive from the link adaptation perspective to adapt relay coding and relay power according to the link state. We examine this link adaptation in fading under both perfect channel state information (CSI) and practical CSI in which nodes have perfect receive and long-term transmit CSI, and derive the corresponding relay power savings in both cases. We also derive the outage probability of the composite relaying scheme, which adapts the signaling to the link regime. Through simulation, we expose a tradeoff for relay placement showing that the relay conserves the most power when closer to the destination but achieves the most rate gain when closer to the source.

We establish a novel link-state regime result for a composite decode-forward (DF) two-way relaying scheme with a direct link. During transmission, our scheme combines block Markov coding and an independent coding scheme that resembles network coding at the relay. A developed novel approach optimizes the composite technique by analyzing the dual variable space to identify link-state regimes in which a particular combination of transmission techniques is optimal. Our results expose an interesting trend: when the user-to-relay link is marginally stronger than the direct link, independent coding is optimal and the relay can conserve power. However, for larger user-to-relay link gains, the relay must use full power and supplement independent coding with block Markov coding to achieve the largest rate region. For Rayleigh fading links, we demonstrate that relay power savings are achievable in most node configurations. The link-state regimes are further applied to perform link adaptation in fading to illustrate significant data rate gains over direct transmission even under a more practical, long-term link state information. These link-state regime results are useful for the application of two-way DF relaying in practice.

We use stochastic geometry to analyze the performance of a user-assisted decode-and-forward (DF) relaying scheme where an active user relays data through another idle user in uplink cellular communication. We propose a new geometric policy based on the random selection of an idle user within a certain area mid-way between the active user and base station, and compare this policy to the common nearest neighbor geometric policy. These probabilities are further used in the analytical derivation of the moments of inter-cell interference power caused by system-wide deployment of this user-assisted DF relaying. We then numerically evaluate the outage probability performance and show that user-assisted relaying can significantly improve reliability for active users near the cell edge. We also show that the proposed mid-way policy significantly improves outage performance over the nearest neighbor policy even when the nearest neighbor cooperation probability is higher. This result suggests that mid-way relay selection is a highly effective cooperation policy.

In this paper, we consider a composite independent and coherent decode-forward (DF) relaying scheme and analytically evaluate its outage performance over Rayleigh fading channels. We examine the link-state regimes in which the optimal strategy is either direct transmission, independent DF (IDF), or coherent DF (CDF), depending on the relation among the links. Outage probabilities at both the relay and destination are determined in order to analytically derive the overall outage performance. With full channel state information (CSI), the relay can save power without affecting the overall outage performance. Specifically, by using only a portion of its maximum power for transmission, the relay conserves power in the IDF regime while still achieving the maximum transmission rate. Even with just statistical CSI, the relay can employ these link regime results to reduce transmit power significantly while maintaining the same outage performance as that of using full power.

We present a link-state based composite decode-forward (DF) scheme for the relay channel comprised of independent coding and coherent block Markov coding. Although it is believed that block Markov coding is the optimal DF technique whenever the source- to-relay link is stronger than the direct link, we show that under some link-state conditions, independent coding achieves the same rate but also results in power savings at the relay. We then apply this composite scheme in fading under a practical channel state information (CSI) assumption in which nodes have perfect receive CSI and long-term transmit CSI. Through simulation, we demonstrate that the throughput with practical CSI achieves a rate comparable to that of block Markov coding, but with less required relay power. Further, we expose a novel trade-off between consumed relay power and rate gain for relay placement. Specifically, the relay conserves the most power when closer to the destination but achieves the most rate gain when closer to the source.

In this paper, we analyze a full-duplex composite decode-and-forward scheme for the two-way relay channel in a Rayleigh fading environment. Designed from a link-state perspective, this scheme combines both block Markov and independent coding, and yields relay power savings in some link-state regimes. For a given distance configuration of nodes, we analytically determine the probability of each link-state regime in Rayleigh fading, which always spans the relay power saving regimes. As such, relay power savings are realizable for almost any distance configuration. Further, we evaluate the throughput when only long-term channel state information (CSI) is available instead of perfect CSI, which still shows substantial gain over not using the relay. Numerical results illustrate significant relay power savings for most distance configurations and highlight an inherent tradeoff between achievable rate and power savings at the relay.

There is a growing need to position wireless devices in the real world for applications such as navigation, emergency location services, and contextual advertisements. Though GPS and the cellular network provide viable outdoor accuracy, these approaches are unsuited for indoor positioning. We propose a high-accuracy Wi-Fi Fingerprint-based indoor positioning system ideal for consumer applications. This system can be implemented in any Wi-Fi-enabled environment without modifying the site. We propose several optimized distance metrics as well as two novel environment-based methods, a density penalty factor and a floor preprocessing technique, to improve the accuracy of the Weighted K-Nearest Neighbor algorithm. Our implementation requires only thirteen minutes of site-survey time per 100 square meters and has an average positioning accuracy of 2.66 meters, which is sufficient for most practical applications.

We analyze the outage performance of coherent partial decode-forward (pDF) relaying over Rayleigh fading channels in both half- and full-duplex transmissions. In coherent DF relaying, the relay either partially or fully decodes the source message, then coherently forwards the decoded message with the source to the destination. This coherent transmission creates a beamforming gain from the source and relay to the destination which improves the transmission rate. We analyze the impact of this beamforming gain on the outage performance, considering outage events at both the relay and the destination. We derive analytical expressions for the overall outage probability, assuming full CSI at receivers and only limited CSI at transmitters. We further show that at high SNR, coherent pDF relaying converges to full DF relaying and achieves the full diversity order of 2. These analyses provide a fundamental understanding of the reliability of coherent pDF relaying and form the basis for analyzing performance in larger network settings. Numerical results show that partial decoding at the relay outperforms full decoding for low SNR and high target rates in the half-duplex mode. Comparison with existing results further shows that source-relay coherent transmission and joint decoding at the destination both improve the outage performance.

We use stochastic geometry to analyze the performance of a partial decode-and-forward (PDF) relaying scheme applied in a user-assisted relaying setting, where an active user relays data through another idle user in uplink cellular communication. We present the geometric model of a network deploying user-assisted relaying and propose two geometric cooperation policies for fast and slow fading channels. We analytically derive the cooperation probability for both policies. This cooperation probability is further used in the analytical derivation of the moments of intercell interference power caused by system-wide deployment of this user-assisted PDF relaying. We then model the intercell interference power statistics using the Gamma distribution by matching the first two moments analytically derived. This cooperation and interference analysis provides the theoretical basis for quantitatively evaluating the performance impact of user-assisted relaying in cellular networks. We then numerically evaluate the average transmission rate performance and show that user-assisted relaying can significantly improve per-user transmission rate despite of increased intercell interference. This transmission rate gain is significant for active users near the cell edge and further increases with higher idle user density, supporting user-assisted relaying as a viable solution to crowded population areas.

We propose a time-division uplink transmission scheme that is applicable to future-generation cellular systems by introducing hybrid device-to-device (D2D) and infrastructure cooperation. Compared with existing frequency-division schemes, the proposed time-division scheme achieves the same or better spectral efficiency and outage performance with simpler signals and shorter decoding delay. These advantages come from sending a different message in each transmission block without block Markovity as in existing schemes. Using time-division, the proposed scheme divides each transmission frame into three phases with variable duration. The two user equipment units (UEs) partially exchange their information in the first two phases and then cooperatively transmit to the base station (BS) in the third phase. We further formulate the end-to-end outage probabilities, considering outages at both UEs and the BS. We analyze this outage performance in Rayleigh fading environment assuming full channel state information (CSI) at receivers and limited CSI at transmitters. Results show that user cooperation improves the achievable rate region even under half-duplex transmission. Moreover, as the received SNR increases, this uplink cooperation significantly reduces outage probabilities and achieves the full diversity order despite additional outages at the UEs. These characteristics make the proposed scheme appealing for deployment in future cellular networks.

We analytically evaluate the outage performance of the half-duplex partial decode-forward (PDF) relaying over Rayleigh fading channels. In PDF relaying, the source splits its information into cooperative and private parts, and the relay decodes only the cooperative information then forwards it to the destination coherently with the source. Assuming full CSI at the receivers and limited CSI at the transmitters, we analyze the outage performance by jointly considering outages of the cooperative and private parts at both the relay and the destination. In spite of additional outage at the relay and limited CSI at the transmitters, we show that PDF relaying achieves the full diversity order of 2 at high SNR. Numerical results confirm the analysis and show the advantage of the considered PDF scheme, with coherent transmission and joint decoding at the destination, over the existing DF and PDF schemes.

Relay-aided cooperative communication is a critical component of next generation cellular networks as it improves coverage and boosts system capacity. In this paper, we use stochastic geometry to study the performance of partial decode-and-forward (PDF) relaying through another idle user in uplink cellular communication. We analytically derive the average inter-cell interference power caused by system-wide deployment of this PDF relaying. This interference analysis provides the basis for quantitatively evaluating performance impacts of user-assisted relaying in cellular networks. We show that user-assisted relaying can significantly improve per-user transmission rate despite of increased inter-cell interference. This throughput gain further increases with higher idle-user density.

Relay-based cellular networks are likely to play an important role in the race for energy efficiency. However, potential gains greatly depend on how relay stations are deployed within the cell. Using a geometrical model for energy-efficient analysis, we investigate the impact of the number and location of relays on energy consumption, and its dependence on the relay coding scheme and the propagation environment, i.e. the pathloss and the line-of-sight conditions. In addition to the transmit energy, we account for the economic cost of relay deployment, as well as the overhead energy dissipated at each relay stations due to data processing and network maintenance. We then bring out four key trade-offs which balance the cost and flexibility of relay deployment, the energy efficiency and the coverage extension.

The performance of a relay-based cellular network is greatly affected by the relay location within a cell. Existing results for optimal relay placement do not reflect how the radio propagation environment and choice of the coding scheme can impact system performance. In this paper, we analyze the impact on relaying performance of node distances, relay height and line-of-sight conditions for both uplink and downlink transmissions, using several relay coding schemes. Our first objective is to propose a geometrical model for energy-efficient relay placement that requires only a small number of characteristic distances. Our second objective is to estimate the maximum cell coverage of a relay-aided cell given power constraints, and conversely, the averaged energy consumption given a cell radius. We show that the practical full decode-forward scheme performs close to the energy-optimized partial decode-forward scheme when the relay is ideally located. However, away from this optimum relay location, performance rapidly degrades and more advanced coding scheme, such as partial decode-forward, is needed to maintain good performance and allow more freedom in the relay placement. Finally, we define a trade-off between cell coverage and energy efficiency, and show that there exists a relay location for which increasing the cell coverage has a minimal impact on the average energy consumed per unit area.

We consider the impact of destination cooperation on improving the achievable rate region in a multiple access channel with joint source-destination cooperation (MAC-SDC). Such cooperation may be appealing in systems with a more powerful destination than the sources, such as the uplink of cellular networks. We propose a coding scheme in which each source employs superposition block Markov encoding and partial decode-forward relaying, while the destination employs quantize-forward relaying and backward decoding. The sources partially exchange their messages using not only the direct links between them as in source cooperation, but also the feedback links from the destination, hence they are able to exchange more information at the same power by utilizing the destination as a relay. We analyze the condition on channel and power parameters such that involving destination cooperation is strictly better than just having source cooperation. Results for Gaussian channels show improvement especially when the inter-source link qualities are close to source-destination link qualities. These results provide guideline for practical systems such as cellular networks in determining when it is beneficial for the base station to cooperate with mobile users in the uplink communication.

The Causal Cognitive Interference Channel (CCIC) models realistic causal cognitive communication between two sender-and-receiver pairs, in which the cognitive sender causally obtains a message from the primary sender and helps forward it to the primary receiver while also sending its own message to the cognitive receiver. We propose a new coding scheme combining the Han-Kobayashi scheme, partial decode-forward relaying and modified dirty-paper coding (DPC) for the Gaussian CCIC in the half-duplex mode. The proposed scheme induces correlation between the transmit signal and the state to allow traditional DPC as well as state forwarding. An achievable rate region with joint decoding is derived. Numerical results show that the rate region for the proposed scheme is better than the Han-Kobayashi scheme and several other existing schemes. We also analyze the maximum rate for the cognitive user while keeping the primary user's rate as interference-free. Results show that, by decode-and-forward relaying, the cognitive user can achieve significant rates while not affecting the primary user's rate even in the half-duplex causal case.

This paper studies a new model for cooperative communication, the interfering relay channels. We show that the hash-forward scheme introduced by Kim for the primitive relay channel is capacity achieving for a class of semideterministic interfering relay channels. The obtained capacity result generalizes and unifies earlier capacity results for a class of primitive relay channels and a class of deterministic interference channels.

We derive the optimal resource allocation of a practical half-duplex scheme for the Gaussian multiple access channel with transmitter cooperation (MAC-TC). Based on rate splitting and superposition coding, two users transmit information to a destination over 3 phases, such that the users partially exchange their information during the first 2 phases and cooperatively transmit to the destination during the last one. This scheme is near capacity-achieving when the inter-user links are stronger than each user-destination link; it also includes partial decode-forward relaying as a special case. We propose efficient algorithms to find the optimal resource allocation for maximizing either the individual or the sum rate and identify the corresponding optimal scheme for each channel configuration. For fixed phase durations, the power allocation problem is convex and can be solved analytically based on the KKT conditions. The optimal phase durations can then be obtained numerically using simple search methods. Results show that as the inter-user link qualities increase, the optimal scheme moves from no cooperation to partial then to full cooperation, in which the users fully exchange their information and cooperatively send it to the destination. Therefore, in practical systems with strong inter-user links, simple decode-forward relaying at both users is rate-optimal.

We analyze coding for energy efficiency in relay channels at a fixed source rate. We first propose a half-duplex decode-forward coding scheme for the Gaussian relay channel. We then derive three optimal sets of power allocation, which respectively minimize the network, the relay and the source energy consumption. These optimal power allocations are given in closed-form, which have so far remained implicit for maximum-rate schemes. Moreover, analysis shows that minimizing the network energy consumption at a given rate is not equivalent to maximizing the rate given energy, since it only covers part of all rates achievable by decode-forward. We thus combine the optimized schemes for network and relay energy consumptions into a generalized one, which then covers all achievable rates. This generalized scheme is not only energy-optimal for the desired source rate but also rate-optimal for the consumed energy. The results also give a detailed understanding of the power consumption regimes and allow a comprehensive description of the optimal message coding and resource allocation for each desired source rate and channel realization. Finally, we simulate the proposed schemes in a realistic environment, considering path-loss and shadowing as modelled in the 3GPP standard. Significant energy gain can be obtained over both direct and two-hop transmissions, particularly when the source is far from relay and destination.

We propose three energy-optimal distributed schemes for the half-duplex relay channel with block fading to maintain a desired source rate. We consider both network energy consumption and consumption of the relay alone, assuming only local channel knowledge. Then, we combine both into a generalized distributed energy-efficient scheme. In these schemes, the source uses message splitting and allocates resources dynamically, such that direct transmission, decode-forward or partial decode-forward is performed at each block, depending on the channel quality. The optimal distributed power allocation is computed from the corresponding centralized scheme by using an estimate of the relay consumption. Compared to decode-forward with no message splitting as often used in distributed designs, the proposed schemes significantly decrease the peak power and provide up to 15% average energy gain for the network consumption and a minimum of 3.3dB gain for the relay consumption.

We design novel partial decode-forward (PDF) schemes for the Gaussian two-way relay channel with direct link. Different from pure decode-forward, each user divides its message into two parts and the relay decodes only one part of each. The relay then generates its codeword as a function of the two decoded parts and forwards to the two users. We propose PDF schemes for both the full- and half-duplex modes. In full duplex, the scheme is based on block Markov encoding and forward joint decoding over 2 consecutive blocks. In half duplex, the transmission is divided into 4 phases, in which one user transmits during the first phase, the other during the second phase, both users transmit during the third phase and the relay transmits during the last phase. The relay decodes a part of the messages from both users at the end of phase 3 and each user decodes only at the end of phase 4. Analysis and simulation show that if for one user, the direct link is stronger than the user-to-relay link, while for the other, the direct link is weaker, then PDF can achieve a rate region strictly larger than the time-shared region of pure decode-forward and direct transmission for both full- and half-duplex modes.

The feasibility of having chaos-based communication in a MIMO system is presented. A promising chaotic symbolic dynamics modulation is chosen, and an Alamouti space-time code scheme is used with 2 transmit and 2 receiver antennas. The diversity technique is combined with chaotic modulation to improve the performance of the system. The performance of the proposed system is evaluated, and then the analytical BER expression is derived.

A relay channel is one in which a Source and Destination use an intermediate Relay station in order to improve communication rates. We propose the study of relay channels with classical inputs and quantum outputs and prove that a “partial decode and forward” strategy is achievable. We divide the channel uses into many blocks and build codes in a randomized, block-Markov manner within each block. The Relay performs a standard Holevo-Schumacher-Westmoreland quantum measurement on each block in order to decode part of the Source's message and then forwards this partial message in the next block. The Destination performs a novel “sliding-window” quantum measurement on two adjacent blocks in order to decode the Source's message. This strategy achieves non-trivial rates for classical communication over a quantum relay channel.

The causal cognitive interference channel (CCIC) is a four-node channel, in which the second sender obtains information from the first sender causally and assists in the transmission of both. We propose a new coding scheme called Han-Kobayashi partial decode-forward binning (HK-PDF-binning), which combines the ideas of Han-Kobayashi coding, partial decode-forward relaying, conditional Gelfand-Pinsker binning and relaxed joint decoding. The second sender decodes a part of the message from the first sender, then uses Gelfand-Pinsker binning to bin against the decoded codeword. When applied to the Gaussian channel, this HK-PDF-binning essentializes to a correlation between the transmit signal and the state, which encompasses the traditional dirty-paper-coding binning as a special case when this correlation factor is zero. The proposed scheme encompasses the Han-Kobayashi rate region and achieves both partial decode-forward relaying rate for the first user and interference-free rate for the second user.

We propose two coding schemes combining decode-forward (DF) and noisy network coding (NNC) with different flavors. The first is a combined DF-NNC scheme for the oneway relay channel which includes both DF and NNC as special cases by performing rate splitting, partial block Markov encoding and NNC. The second combines two different DF strategies and layered NNC for the two-way relay channel. One DF strategy performs coherent block Markov encoding at the source at the cost of power splitting at the relay, the other performs independent source and relay encoding but with full relay power, and layered NNC allows a different compression rate for each destination. Analysis and simulation show that both proposed schemes supersede each individual scheme and take full advantage of both DF and NNC.

We propose an iterative mode-dropping algorithm that optimizes input signals to achieve the sum capacity of the MIMO-MAC with per-antenna power constraint. The algorithm successively optimizes each user's input covariance matrix by applying mode-dropping to the equivalent single-user MIMO rate maximization problem. Both analysis and simulation show fast convergence. We then use the algorithm to briefly highlight the difference in MIMO-MAC capacities under sum and per-antenna power constraints.

In this paper, we consider the single-user MIMO channel with per-antenna power constraint. We formulate the capacity optimization problem with per-antenna constraint in the SDP framework and analyze its optimality conditions. We establish in closed-form the optimal input covariance matrix as a function of the dual variable. We then propose a simple algorithm to find this optimal input covariance and the capacity. Results show that the capacity with per-antenna power can be significantly different from those with sum power or with independent multiple access constraint.

We study a cooperative communication system consisting of two users in half duplex mode communicating with one destination over additive white Gaussian noise (AWGN). Cooperation is performed between the two users by partial decode-forward relaying over 3 time slots with variable duration. During the first two slots, each user alternatively transmits and partially decodes while during the last time slot, both users cooperate to forward information to the destination. We establish the achievable rate region of this scheme. Then using the Lagrangian method, we analyze the optimal power allocations and the optimal time duration that maximize its individual and sum rates for the symmetric channel. Results show a significant improvement of the rates compared to the classical multiple access channel (MAC) when the inter-user channel quality is better than that between each user and the destination.

This paper analyzes the performance of chaotic communications in a MIMO system. The robustness of chaos-based communications systems makes Differential Chaos Shift Keying (DCSK) the preferred modulation choice. In order to improve the performance of such a system, the Alamouti space-time code is used for 2 transmit and 2 receive antennas. A new approach for computing the bit-error-rate (BER) performance is provided, and an analytical BER expression is derived. The approach used explores the dynamic properties of chaotic sequences and takes into account the fact that the bit energy varies from one transmitted bit to the next. Simulation results confirm the accuracy of this approach.

In this paper, we present a new cooperative communication scheme consisting of two users in half-duplex mode communicating with one destination over a discrete memoryless channel. The users encode messages in independent blocks and divide the transmission of each block into 3 time slots with variable durations. Cooperation is performed by partial decode-forward relaying over these 3 time slots. During the first two time slots, each user alternatively transmits and decodes, while during the last time slot, both users cooperate to send information to the destination. An achievable rate region for this scheme is derived using superposition encoding and joint maximum likelihood (ML) decoding across the 3 time slots. An example of the Gaussian channel is treated in detail and its achievable rate region is given explicitly. Results show that the proposed half-duplex scheme achieves significantly larger rate region than the classical multiple access channel and approaches the performance of a full-duplex cooperative scheme as the inter-user channel quality increases.

We explore the issue of the network energy efficiency in relay channels. We first propose a half-duplex decode-forward coding scheme. We then optimize the power allocation to minimize the total power consumption while maintaining a desired source rate. We show that this scheme significantly outperforms direct and two-hop transmissions. Moreover, it reduces the relay energy consumption by up to 7dB, which is beneficial for shared relay stations, and smooths out transmit power peaks, which simplifies interference management.

We consider the full-duplex two-way relay channel with direct link between two users and propose two coding schemes: a partial decode-forward scheme, and a combined decode-forward and compute-forward scheme. Both schemes use rate-splitting and superposition coding at each user and generate codewords for each node independently. When applied to the Gaussian channel, partial decode-forward can strictly increase the rate region over decode-forward, which is opposite to the one-way relay channel. The combined scheme uses superposition coding of both Gaussian and lattice codes to allow the relay to decode the Gaussian parts and compute the lattice parts. This scheme can also achieve new rates and outperform both decode-forward and compute-forward separately. These schemes are steps towards understanding the optimal coding.

This paper carries out the first-ever study of the feasibility of using chaos shift keying (CSK) in a Multiple-Input, Multiple-Output (MIMO) channel. To that end, an Alamouti space time code scheme is combined with the CSK system for 2 transmit and 2 receiver antennas. Once the design of CSK-MIMO system is presented, the performance of the proposed system is analyzed in a mono-user case. An exact computation approach of the BER expression based on the probability density function of the bit energy of the chaotic sequence is presented. Simulation results prove the accuracy of our BER computation methodology.

We investigate spectrum sensing by energy detection based on two different objective functions: a Bayesian sensing cost or the network weighted sum capacity. The Bayesian cost is a traditional detection measure which aims at minimizing a combination of the miss-detection and false-alarm probabilities, while the capacity objective is a communication measure which aims at maximizing the network throughput. Fading-dependent optimal sensing thresholds for each objective are derived in closed-form for different cases of location side information. To make sensing more robust to channel fading, we also propose fading-independent sub-optimal thresholds. Results show that location side information helps improve performance when using the threshold designed for that performance measure. However, the Bayesian-based threshold does not utilize the side information well in improving the network sum capacity. On the other hand, the capacity-based threshold captures the benefit of side information in both the capacity and Bayesian cost measures. Furthermore, it helps to significantly improve the network throughput. The proposed sensing schemes with location side information can also be generalized to a network with multiple cognitive users in a simple and distributed manner.

We establish in closed-form the capacity and the optimal signaling scheme for a MISO channel with per-antenna power constraint. Two cases of channel state information are considered: constant channel known at both the transmitter and receiver, and Rayleigh fading channel known only at the receiver. For the first case, the optimal signaling scheme is beamforming with the phases of the beam weights matched to the phases of the channel coefficients, but the amplitudes independent of the channel coefficients and dependent only on the constrained powers. For the second case, the optimal scheme is to send independent signals from the antennas with the constrained powers. In both cases, the capacity with per-antenna power constraint is usually less than that with sum power constraint.

Two distinct, but overlapping, networks that operate at the same time, space, and frequency is considered. The first network consists ofnrandomly distributed primary users, which form an ad hoc network. The second network again consists ofmrandomly distributed ad hoc secondary users or cognitive users. The primary users have priority access to the spectrum and do not need to change their communication protocol in the presence of the secondary users. The secondary users, however, need to adjust their protocol based on knowledge about the locations of the primary users to bring little loss to the primary network's throughput. By introducing preservation regions around primary receivers, a modified multihop routing protocol is proposed for the cognitive users. Assumingm=nβwith β >; 1, it is shown that the secondary network achieves almost the same throughput scaling law as a stand-alone network while the primary network throughput is subject to only a vanishingly small fractional loss. Specifically, the primary network achieves the sum throughput of ordern1/2and, for any δ >; 0, the secondary network achieves the sum throughput of orderm1/2-δwith an arbitrarily small fraction of outage. Thus, almost all secondary source-destination pairs can communicate at a rate of orderm-1/2-δ.

This paper analyzes the impact and benefits of infrastructure support in improving the throughput scaling in networks ofnrandomly located wireless nodes. The infrastructure uses multiantenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbitrary rates relative ton. Under the model, capacity scaling laws are analyzed for both dense and extended networks. Two BS-based routing schemes are first introduced in this study: an infrastructure-supported single-hop (ISH) routing protocol with multiple-access uplink and broadcast downlink and an infrastructure-supported multihop (IMH) routing protocol. Then, their achievable throughput scalings are analyzed. These schemes are compared against two conventional schemes without BSs: the multihop (MH) transmission and hierarchical cooperation (HC) schemes. It is shown that a linear throughput scaling is achieved in dense networks, as in the case without help of BSs. In contrast, the proposed BS-based routing schemes can, under realistic network conditions, improve the throughput scaling significantly in extended networks. The gain comes from the following advantages of these BS-based protocols. First, more nodes can transmit simultaneously in the proposed scheme than in the MH scheme if the number of BSs and the number of antennas are large enough. Second, by improving the long-distance signal-to-noise ratio (SNR), the received signal power can be larger than that of the HC, enabling a better throughput scaling under extended networks. Furthermore, by deriving the corresponding information-theoretic cut-set upper bounds, it is shown under extended networks that a combination of four schemes IMH, ISH, MH, and HC is order-optimal in all operating regimes.

We study Bayesian detection based cognitive sensing and analyze its impact on the capacity in various cases of location information. In a network of one primary and one cognitive users, the cognitive transmitter relies on information about the locations of the primary transmitter and the two receivers to design its optimal sensing threshold. Results show that this location-aware threshold can significantly improve the cognitive user's capacity, while imposing almost no detrimental effect on the primary user's capacity. Combined with a priori knowledge of the primary transmission probability, the location information is shown to be beneficial to the cognitive user's capacity when the primary user is likely to be active. Without the knowledge of the primary transmission probability, location information is beneficial for all range of primary activity.

We study two distinct, but overlapping, networks which operate at the same time, space and frequency. The first network consists of n randomly distributed primary users, which form either an ad hoc network, or an infrastructure supported ad hoc network in which l additional base stations support the primary users. The second network consists of m randomly distributed secondary or cognitive users. The primary users have priority access to the spectrum and do not change their communication protocol in the presence of secondary users. The secondary users, however, need to adjust their protocol based on knowledge about the locations of the primary users so as not to harm the primary network's scaling law. Base on percolation theory, we show that surprisingly, when the secondary network is denser than the primary network, both networks can simultaneously achieve the same throughput scaling as a standalone ad hoc network.

We consider beamforming in a cognitive network with multiple primary users and a secondary user sharing the same spectrum. Each primary and secondary user consists of a transmitter and a receiver. In particular, we assume that the secondary transmitter hasNtantennas and transmits data to its single-antenna receiver using beamforming. The beamformer is designed to maximize the cognitive signal-to-interference ratio (CSIR). Using mathematical tools from random matrix theory, we derive both lower and upper bounds on the average interference created by the cognitive transmitter at the primary receivers and the average CSIR of the cognitive user. We further analyze and prove the convergence of these two performance measures asymptotically as the number of antennasNtor primary usersNpincreases. Specifically, we show that the average interference per primary receiver converges toE[d-α], the expected value of the path loss in the network, whereas the average CSIR decays as1/cwhenc = Np/Nt→∞. In the special case ofNt≥ Npthe lower bound of the average total interference approaches 0 and the upper bound of the average CSIR approachesNtE[d->-α]/σC2whereσC2is the noise variance at the cognitive receiver.

We study a cognitive network consisting of a single primary transmitter and multiple secondary, or cognitive, users. The primary transmitter, located at the center of the network, communicates with primary receivers within a disc called the primary exclusive region (PER). Inside the PER, no cognitive users may transmit, in order to guarantee an outage probability for the primary receivers within. Outside the PER, uniformly distributed cognitive users may transmit, provided they are at a certain protected radius from a primary receiver. We analyze the aggregated interference from the cognitive transmitters to a primary receiver within the PER. Based on this interference and the outage guarantee, we derive bounds on the radius of the PER, showing its interdependence on the receiver protected distance and other system parameters. We also extend the analysis to allowing the cognitive users to scale their power according to the distance from the primary transmitter. These studies provide a closed-form, theoretical analysis of such a network geometry with PER, which may be relevant in the upcoming spectrum sharing actions.

We consider a cognitive network consisting of n cognitive users uniformly distributed with constant density among primary users. Each user has a single transmitter and a single receiver, and the primary and cognitive users transmit concurrently. The cognitive users use single-hop transmission in two scenarios: (i) with constant transmit power, and (ii) with transmit power scaled according to the distance to a designated primary transmitter. We show that, in both cases, the cognitive users can achieve a throughput scaled linearly with the number of users n. The first scenario requires the cognitive users to have the transmitter-receiver (Tx-Rx) distance bounded, but it can be arbitrarily large. Then with high probability, any network realization has the throughput scaling linearly with n. The second scenario allows the cognitive Tx-Rx distance to grow with the network at a feasible exponent as a function of the path loss and the power scaling factors. In this case, the average network throughput grows at least linearly with n and at most as n log(n). These results suggest that single-hop transmission may be a suitable choice for cognitive transmission.

In this paper, we consider a cognitive network in which a single primary transmitter communicates with primary receivers within an area of radius RO, called the primary exclusive region (PER). Inside this region, no cognitive users may transmit. Outside the PER, provided that the cognitive transmitters are at a minimal distance isinpfrom a primary receiver, they may transmit concurrently with the primary user. We determine bounds on the primary exclusive radius ROand the guard band isinpto guarantee an outage performance for the primary user. Specifically, for a desired rate COand an outage probability beta, the probability that the primary user's rate falls below COis less than beta. This performance guarantee holds even with an arbitrarily large number of cognitive users uniformly distributed with constant density outside the primary exclusive region.

We consider beamforming in a cognitive network with multiple primary users and secondary users sharing the same spectrum. In particular, we assume that each secondary transmitter has Nt antennas and transmits data to its single- antenna receiver using beamforming. The beamformer is designed to maximize the cognitive user's signal-to-interference ratio (SIR), defined as the ratio of the received signal power at the desired cognitive receiver to the total interference created at all the primary receivers. Using mathematical tools from random matrix theory, we derive both lower and upper bounds on the average interference at the primary receivers and the average SIR of the cognitive user. We further analyze and prove the convergence of these two performance measures asymptotically as the number of antennas Ntor primary users Ntincreases. Specifically, the average interference per primary receiver converges to the expected value of the path loss in the network whereas the average SIR of the secondary user decays as 1/c when c = Np/Ntrarr infin. In the special case of Nt= Np, the average total interference approaches 0 and the average SIR approaches infin.

We analyze the benefits of infrastructure support in improving the throughput scaling in networks of n randomly located wireless nodes. The infrastructure uses multi-antenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbtrary rates relative to n. We introduce two multi-antenna BS-based routing protocols and analyze their throughput scaling laws. Two conventional schemes not using BSs are also shown for comparison. In dense networks, we show that the BS-based routing schemes do not improve the throughput scaling. In contrast, in extended networks, we show what our BS-based routing schemes can, under certain network conditions, improve the throughput scaling significantly.

We study a cognitive network consisting of multiple cognitive users communicating in the presence of a single primary user. The primary user is located at the center of the network, and the cognitive users are uniformly distributed within a circle around the primary user. Assuming a constant cognitive user density, the radius of this circle will increase with the number of users. We consider a scheme in which the primary transmitter sends a beacon signaling its own transmission. The cognitive users, upon receiving this beacon, stay silent. Because of channel fading, however, there is a non-zero probability that a cognitive user misses the beacon and hence, with a certain activity factor, transmits concurrently with the primary user. Given the location of the primary receiver, we are interested in the total interference caused by the cognitive users to this receiver. In particular, we provide closed-form bounds on the mean and variance of the interference, and relate them to the outage probability on the primary user. These analytical results can help in the design of a cognitive network with beacon.

In this paper, we study a wireless multiple-input multiple-output system in a Rayleigh flat-fading environment with correlation among the transmit antennas. We assume that the receiver has perfect CSI and the transmitter only knows the correlation matrix. The transmitter employs a quasi-orthogonal space-time block code in combination with a linear precoder; the receiver uses a linear MMSE detector. We analyze the optimal transmit precoding strategy that minimizes the average sum MSE at the receiver. We show that, as expected, the optimal precoding directions coincide with the eigenvectors of the transmit correlation matrix. The optimal power allocation, however, only supports at most 2 directions at all SNRs independent of the number of transmit antennas, which correspond to the 2 largest eigenvalues of the transmit correlation matrix. We characterize this optimal power allocation by the necessary and sufficient optimality conditions. At high SNRs, the optimal allocation approaches equal power on the two supported modes. At low SNRs, the weaker mode is dropped and the precoding matrix becomes single-mode beamforming. We provide a closed-form expression characterizing this low-SNR range. Numerical simulations confirm our theoretical analysis.

Adaptive transmission techniques including transmit beamforming, preceding, and opportunistic scheduling offer high spectral efficiency when channel state information at the transmitter (CSIT) is accurate, but suffer performance loss when the CSIT quality is poor. Diversity transmission techniques such as space-time coding and frequency interleaving, in contrast, are capable of capturing spatial and spectral diversity without CSIT, hence can provide good performance when CSIT degradation is severe. Between a pair of adaptive and diversity techniques, there exists a switching point in terms of the CSIT-quality; a CSIT- quality higher than this point favors the adaptive technique, but a lower CSIT-quality prefers the diversity one. This paper analyzes several adaptive and diversity schemes for a multiuser multiple-input single-output (MISO) system. Comparative performance in terms of the outage capacity is studied and the CSIT-quality switching points between adaptive and diversity schemes are analyzed. Results are supportive of adaptive transmissions for mobile applications, such as in the downlink of an orthogonal frequency division multiple access (OFDMA) system.

Transmit channel side information (CSIT) can significantly increase MIMO wireless capacity. Due to delay in acquiring this information, however, the time-selective fading wireless channel often induces incomplete, or partial, CSIT. In this paper, we first construct a dynamic CSIT model that takes into account channel temporal variation. It does so by using a potentially outdated channel measurement and the channel statistics, including the mean, covariance, and temporal correlation. The dynamic CSIT model consists of an effective channel mean and an effective channel covariance, derived as a channel estimate and its error covariance. Both parameters are functions of the temporal correlation factor, which indicates the CSIT quality. Depending on this quality, the model covers smoothly from perfect to statistical CSIT. We then summarize and further analyze the capacity gains and the optimal input with dynamic CSIT, asymptotically at low and high SNRs. At low SNRs, dynamic CSIT often multiplicatively increases the capacity for all multi-input systems. The optimal input is typically simple single-mode beamforming. At high SNRs, for systems with equal or fewer transmit than receive antennas, it is well-known that the capacity gain diminishes to zero because of equi-power optimal input. With more transmit than receive antennas, however, the capacity gain is additive. The optimal input then is highly dependent on the CSIT. In contrast to equi-power, it can drop modes for channels with a strong mean or strongly correlated transmit antennas. For such mode-dropping at high SNRs in special cases, simple conditions on the channel K factor or the transmit covariance condition number are subsequently quantified. Next, using a convex optimization program, we study the MIMO capacity with dynamic CSIT non-asymptotically. Particularly, we numerically analyze effects on the capacity of the CSIT quality, the relative number of transmit and receive antennas, and the channel K factor. For example, the capacity gain based on dynamic CSIT is more sensitive to the CSIT quality at higher qualities. The program also helps to evaluate a simple, analytical capacity lower-bound based on the Jensen optimal input. The bound is tight at all SNRs for systems with equal or fewer transmit than receive antennas, and at low SNRs for others.

A linear precoder for MIMO channels exploiting both the channel mean and transmit correlation has been shown to improve performance of an orthogonal space-time coded system (Vu, M. and Paulraj, A., Proc. IEEE Vehicular Tech. Conf., 2004). We extend the precoder design to systems with non-orthogonal space-time code, and provide asymptotic analysis at high K factor. The precoder is designed by minimizing the Chernoff bound on the pairwise error probability. While a linear precoder can be viewed as a multi-mode beamformer, it converges to a single beam as the K factor increases. A design criterion based on the minimum codeword distance and a new criterion based on the average codeword distance are considered. Numerical simulations using quasi-orthogonal STBC give examples of the performance gain that can be achieved with these designs.

We study a space-time precoding technique for MISO wireless systems by employing a linear prefilter at each transmit antenna. The channel is Rician fading, where the mean and variance of the propagation paths are known to the transmitter. This model includes the Rayleigh fading channels as special cases. We use channel capacity as the optimizing criterion for the prefilter design. This criterion provides a unified design of the prefilters for both Rician and Rayleigh fading channels. The optimum prefilters are functions of the channel mean and variance. The solution ranges from beamforming for Rician channels with high K-factor, to unitary diversity for Rayleigh fading cases, where delay diversity is an example. The MMSE equalizer is then used to detect the signal at the receiver. Analysis of bounds on error rate performance and numerical simulations for 4QAM input signals show significant diversity gains and array gains. The result also illustrates that having partial channel knowledge at the transmitter can strongly enhance the system performance.

The optimum transmission scheme that maximizes ergodic capacity in a K/spl rarr//spl infin/ regime for 2/spl times/1 MISO systems is studied when the channel knowledge at the transmitter is characterized by a known gain imbalance and a known PDF of the phase shift between antennas. Such a channel scenario can arise in a forward link at the base station when there is a single direct path propagation. We show that the optimum transmit solution is beam-forming on the mean value of the phase shift with unequal power input to the antennas. When the phase is completely unknown, the solution reduces to a single antenna transmission.

Abstract We study the optimum transmission scheme that maximizes ergodic capacity of a 2 × 1 multiple‐input single‐output (MISO) system, when the channel knowledge at the transmitter is characterized by a known gain ratio and a known probability density function (PDF) of the phase shift between antennas. Such a channel scenario can arise in a forward link at the base station when there is a single direct path propagation. We show that the optimum transmit solution is beamforming on the mean value of the phase shift with unequal power input to the antennas. When the phase is completely unknown, the solution reduces to a single antenna transmission. Copyright © 2004 John Wiley & Sons, Ltd.