Federated systems enable collaborative training on highly heterogeneous, non-i.i.d. data through model personalization, which can be facilitated by employing multi-task learning. However, multi-task learning algorithms are often implemented using methods like stochastic gradient descent, which may suffer from slow convergence in a multi-task federated setting. To accelerate the training procedure, we design an efficient iterative distributed method based on the alternating direction method of multipliers (ADMM) for support vector machines (SVMs), which tackles federated classification and regression. The proposed method utilizes efficient computations and model exchange in a network of heterogeneous nodes and allows personalization of the learning model in the presence of non-i.i.d. data. To ensure data privacy, we introduce a randomization algorithm that helps avoid data inversion. Finally, we analyze the impact of the proposed privacy mechanisms and participant hardware and data heterogeneity on the system performance. Our experiments confirm the advantages of the proposed ADMM-based personalized federated multi-task learning.

In the current 5G system architecture, the use cases around user plane security enforcement are oriented and limited to security configuration towards the NG-RAN, based on the integrity and/or ciphering protection activation or deactivation in the air interface between user equipment (UE) and gNB. Security features between gNB and User Plane Function (UPF) are optional, configured by the network provider, and there is no end-to-end protection for the user plane data. However, the gNB is more vulnerable to attacks due to its physical location, which leads to data and privacy leakage. Additionally, the mapping from service data flow to QoS flow is mainly based on QoS requirements rather than security, which means the service data flows with similar QoS but different security requirements will be mapped to the same QoS flow and then be processed with the same security protection on the air interface. Moreover, 3GPP only supports coarse-grained Packet Data Unit (PDU) session level integrity protection, i.e., all QoS flows in the same PDU session have to share the same security configuration at the UEgNB interface. This will lead to either high security overhead if only a few QoS flows need protection or inadequate protection if protection is disabled since the majority of the flows do not require it. In this paper, we propose a backward-compatible differentiated (per-QoS flow) end-to-end security mechanism allowing the protection of only those QoS flows that require ciphering and/or integrity protection. The security options can be changed dynamically during the QoS flow lifetime. Our numerical results show that the proposed solution allows us to decrease the computational burden imposed at UE.

Federated Learning (FL) enables collaborative training of machine learning models for edge devices (e.g., mobile phones) over a network without revealing raw data of the participants. The existing FL benchmarks mostly assume static distribution of local data over time failing to capture the behavior of real-world applications with space-time varying data (e.g., autonomous cars). Our framework addresses this limitation by leveraging popular open source physics simulator (CARLA) and FL framework (OpenFL) to allow collection of streaming data from the mobile agents and feeding them to a practically deployable FL engine for online collaborative training. It also provides the FL researchers with the ability to model data heterogeneity, annotate data with practically zero cost, and perform reproducible continual FL experiments. We believe that this is one of the first attempts to demonstrate online FL on realistic streaming datasets from a virtual world. The demo showcases this framework using a popular object detection use case.

In this paper, we propose a new service orchestration approach for in-network fully distributed dynamic compute composition with limited involvement from the consumer and no centralized coordinator. The service is composed fully inside the network including assembling data and software, and compute node selection, leveraging standardized NDN functionalities. The use of the proposed approach will enable smooth support of dynamic compute services over wireless/mobile edge and edge/fog NDN-based systems, where the use of conventional IP approach faces fundamental difficulties related to dynamic allocation of IP addresses. We will also outline extensions of the proposed approach for streaming data, function chaining and re-use of partially computed results. Our numerical results show that the proposed method improves service reliability at the edge by increasing the Interest satisfaction by 5-10 times depending on the considered deployment, network topology, and resources.

With the rapid growth in number of connected devices, edge and fog computing paradigms become promising solutions for handling computation with low latency and reduced bandwidth usage. They allow to execute a computation across IoT devices, e.g., cameras, an edge aggregator/processor, and on the cloud. In this paper, we consider a pervasive computing scenario where end devices such as laptops, smart phones or roadside units are willing to offer compute services along with the traditional infrastructure-based edge computing resources. Our work uses an Information Centric Network (ICN), which is a promising technique for resource-constrained end devices to offload time-sensitive computing tasks to nearby distributed network resources, e.g., to an edge server. We expand the Named Data Networking (NDN), which is an ICN implementation, to operate in a dynamic resource-constrained multi-hop environment and propose i) a fast and efficient capability discovery scheme to assist each node in discovering other nodes’ capabilities in a distributed manner; and ii) efficient schemes for server/path selection, to optimize the end-to-end latency including discovery, data transmission and computation. Our results show that the proposed solutions can improve the total latency by around 50% for most of the tasks.

Today, numerous machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of wireless networks. Distributed real-time ML solutions are highly susceptible to the so-called straggler effect caused by resource heterogeneity, which can be mitigated by various computation offloading mechanisms that severely impact communication efficiency, especially in large-scale scenarios. To reduce the communication overhead, we leverage device-to-device (D2D) connectivity, which enhances spectrum utilization and allows for efficient data exchange between proximate devices. In particular, we design a novel D2D-aided coded distributed learning method named D2D-CDL for efficient load balancing across devices. The proposed solution captures system dynamics, includingdata(time-varying learning model, irregular intensity of data arrivals),device(diverse computational resources and volume of training data), anddeployment(different locations and D2D graph connectivity). To decrease the number of communication rounds, we derive an optimal compression rate, which minimizes the processing time. The resulting optimization problem provides suboptimal compression parameters that improve the total training time. Our proposed method is particularly beneficial for real-time collaborative applications, where users continuously generate training data thus yielding a model drift.

Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. CodedFedL enables coded computing for non-linear federated learning by efficiently exploiting distributed kernel embedding via random Fourier features that transforms the training task into computationally favourable distributed linear regression. Furthermore, clients generate local parity datasets by coding over their local datasets, while the server combines them to obtain the global parity dataset. Gradient from the global parity dataset compensates for straggling gradients during training, and thereby speeds up convergence. For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays. We also characterize the leakage in data privacy when clients share their local parity datasets with the server. Additionally, we analyze the convergence rate and iteration complexity of CodedFedL under simplifying assumptions, by treating CodedFedL as a stochastic gradient descent algorithm. Finally, for demonstrating gains that CodedFedL can achieve in practice, we conduct numerical experiments using practical network parameters and benchmark datasets, in which CodedFedL speeds up the overall training time by up to 15× in comparison to the benchmark schemes.

Spatial correlation has been recognized by 3GPP as one of the key elements in millimeter-wave (mmWave) channel modeling. Correlated channel behavior is induced by macro objects, such as buildings, as well as by micro objects, including humans around the mmWave receivers. The 3GPP's three-dimensional (3D) spatially consistent channel model designed to capture these phenomena assumes a-priori knowledge of the correlation distance between the receivers. In this paper, we propose a novel spatially-consistent human body blockage state generation procedure, which extends the standardized 3D channel model by 3GPP to capture the correlation between the line-of-sight (LoS) links and the reflected cluster states affected by human body blockage. The proposed model is based on analytical expressions for the conditional link state probability, thus permitting the parametrization of the spatial field of receivers. It also does not require any a-priori information on the correlation distance as the latter is identified explicitly based on the environmental parameters. We compare the results for the proposed model with those obtained with the uncorrelated blockage model and conclude that in many special cases correlation manifests itself in quantitatively different propagation conditions experienced at the nearby receivers.

We consider the problem of allocating radio resources over wireless communication links to control a series of independent low-latency wireless control systems common in industrial settings. Supporting wireless control in time sensitive settings requires fast data rates over wireless links, which comes at the cost of reliability. It is challenging to meet both latency and reliability requirements with an equal or arbitrary allocation of resources. We thus propose a novel control-aware approach to the low-latency scheduling problem in which we incorporate control and channel state information in allocating bandwidth and data rates across the wireless links. Control systems that are in desirable states are given modest requirements on error rates, while systems in undesirable states are given more priority. We derive control-aware packet error rate targets for each system to satisfy stability goals and make scheduling decisions to meet such targets while reducing total transmission time. The resulting control-aware based method is tested in simulation experiments that demonstrate its effectiveness in meeting control-based goals under tight latency constraints relative to control-agnostic scheduling.

In wireless mobile edge computing platforms, such as those supported by vehicular networks, location specific machine learning models can be trained by distributing computations using resources available at the edge. The computational and communication resources at the wireless edge are heterogeneous and unreliable, which can lead to straggler effects that significantly slow down recursive learning tasks, such as gradient descent. In this paper we propose a coded computation framework, which utilizes statistical knowledge of resource heterogeneity to determine optimal encoding and load balancing of training data using Random Linear codes, while avoiding an explicit step for decoding gradients. Results show that the proposed coding framework speeds up the training time for linear regression models, by up to ten times, when compared with repetition coding schemes.

We consider the problem of allocating radio resources over wireless communication links to control a series of independent wireless control systems. Low-latency transmissions are necessary in enabling time-sensitive control systems with high sampling rates to operate over wireless links. Enabling low-latency through fast data rates comes at the cost of reliability in the form of higher packet error rates due to channel noise. However, the impact of such communication link errors on the control system performance depends dynamically on the control system state. We propose a novel control-aware communication design to the low-latency resource allocation problem. In our proposed method, we incorporate both control and channel state information in scheduling transmissions across time slots, frequency bands, and data rates using the next-generation Wi-Fi scheduling architecture. Control systems that are closer to instability or further from a desired range in a given control cycle are given higher packet delivery rate targets to meet. Rather than a simple priority ranking, we derive precise adaptive packet error rate targets for each system needed to satisfy control-specific performance requirements. We use these adaptive rate targets to make scheduling decisions that reduce total transmission time. The resulting control-aware low-latency scheduling (CALLS) method is tested in numerous simulation experiments that demonstrate its effectiveness in meeting control-based goals under tight latency constraints relative to control-agnostic scheduling.

Millimeter wave (mmWave) communication is a key enabler for realizing the data rates, capacity and latency requirements envisioned for emerging 5G systems. This paper addresses the system performance of 73 GHz mmWave access, which is representative of the systems expected to be standardized in the second phase of 3GPP specifications. In particular, we address the performance scalability of mmWave systems and characterize the tradeoffs and challenges involved in improving their coverage and capacity for different cell density regimes. Our results, based on industry standard 3GPP methodology, show that the directional communication enabled through massive MIMO capability of mmWave systems, is key towards addressing the interference limitation on performance scaling, thus illustrating the promise of mmWave communication to provide a scalable solution capable of handling the needs of future ultra-dense networks.

Dense deployments of millimeter-wave (mmWave) base stations (BSs) are being considered as the most feasible solution to meet the steadily growing data rate demands of mobile users. Accordingly, the achievable performance gains of mmWave-based dense networks in real deployments have to be studied carefully, since mmWave radio technology features specific transceiver, antenna, and propagation properties. In this paper, we contribute an accurate performance evaluation of single- versus dual-polarized MIMO systems operating over the mmWave channel in typical urban scenarios as well as address the impact of device- and network-centric parameters on the performance gains enabled by MIMO in dense to ultra-dense BS deployments. This study relies on our in-house ray-based modeler and takes into account the key mmWave system effects, such as multi-path propagation, utilization of dual-polarized antennas, and characteristic interference models. Our results show that the benefit of using mmWave- MIMO grows with increasing BS density, thus encouraging a further study of this technology especially for (ultra-)dense setups. We also demonstrate that non-coherent non-polarized diffuse scattering component may reduce the capacity gain of dual-polarized vs. single- polarized MIMO.

To satisfy the stringent capacity and scalability requirements in the fifth generation (5G) mobile networks, both wireless access and backhaul links are envisioned to exploit millimeter wave (mmWave) spectrum. Here, similar to the design of access links, mmWave backhaul connections must also address many challenges such as multipath propagation and dynamic link blockage, which calls for advanced solutions to improve their reliability. To address these challenges, 3GPP New Radio technology is considering a flexible and reconfigurable backhaul architecture, which includes dynamic link rerouting to alternative paths. In this paper, we investigate the use of aerial relay nodes carried by e.g., unmanned aerial vehicles (UAVs) to allow for such dynamic routing, while mitigating the impact of occlusions on the terrestrial links. This novel concept requires an understanding of mmWave backhaul dynamics that accounts for: 1) realistic 3-D multipath mmWave propagation; 2) dynamic blockage of mmWave backhaul links; and 3) heterogeneous mobility of blockers and UAV-based assisting relays. We contribute the required mathematical framework that captures these phenomena to analyze the mmWave backhaul operation in characteristic urban environments. We also utilize this framework for a new assessment of mmWave backhaul performance by studying its spatial and temporal characteristics. We finally quantify the benefits of utilizing UAV assistance for more reliable mmWave backhaul. The numerical results are confirmed with 3GPP-calibrated simulations, while the framework itself can aid in the design of robust UAV-assisted backhaul infrastructures in future 5G mmWave cellular.

The use of extremely high frequency (EHF) or millimeter-wave (mmWave) band has attracted significant attention for the next generation wireless access networks. As demonstrated by recent measurements, mmWave frequencies render themselves quite sensitive to “blocking” caused by obstacles like foliage, humans, vehicles, etc. However, there is a dearth of analytical models for characterizing such blocking and the consequent effect on the signal reliability. In this paper, we propose a novel, general, and tractable model for characterizing the blocking caused by humans (assuming them to be randomly located in the environment) to mmWave propagation as a function of system parameters like transmitter-receiver locations and dimensions, as well as density and dimensions of humans. Moreover, the proposed model is validated using a ray-launcher tool. Utilizing the proposed model, the blockage probability is shown to increase with human density and separation between the transmitter-receiver pair. Furthermore, the developed analysis is shown to demonstrate the existence of a transmitter antenna height that maximizes the received signal strength, which in turn is a function of the transmitter-receiver distance and their dimensions.

Given the importance of multi-radio heterogeneous networks (HetNets) in delivering more throughput and better connectivity experience to today's wireless users, we investigate the prioritized centrally-controlled resource allocation mechanisms in such systems. First, we theoretically formulate the problem of assisted rate allocation across multiple radio access technologies (RATs) as a special case of relative max-min fairness problem with bifurcated (splittable) traffic flows, which can then be solved by employing the standard linear optimization techniques. Our proposed solution delivers certain minimum guarantees to all the network users, while the rest of system resources are divided proportionally to the preset priority of the users. The priorities in our system may correspond to different subscription/pricing plans of a network operator. Finally, we demonstrate the practical benefits of the proposed resource allocation scheme with system-level simulations, as well as discuss its implementation within a testbed prototype based on the OpenFlow architecture.

As fourth generation communications technology is already being deployed, research efforts are now being shifted to what comes beyond state-of-the-art wireless systems. Driven by the anticipated acceleration in mobile traffic demand, the wireless industry is specifically focused on improving capacity and coverage of current networks through aggressive reuse of the cellular spectrum. Together with deploying an increasingly dense overlay tier of smaller cells, mobile network operators are beginning to rely on unlicensed-band WLAN technologies to leverage additional spectrum and relieve congestion on their networks. Consequently, the emerging vision of heterogeneous networks exploits the potential of a diverse range of devices requiring connectivity at different scales to augment available system capacity and improve the user connectivity experience. In this paper, we seek to meet this important trend with our novel integrated methodology for assisted (managed) radio network selection capturing spatial randomness of converged cellular/WLAN deployments together with dynamic uplink traffic from their users. To this end, we employ tools coming from stochastic geometry to characterize performance of macro and pico cellular networks, as well as WLAN, mindful of user experience and targeting intelligent network selection/assignment. We complement our analysis with system-level simulations providing deeper insights into the behavior of future heterogeneous deployments.

Multi-radio heterogeneous networks (Het-Nets) are an important focus area for next generation cellular standards, with the 3GPP community actively developing WiFi/LTE interworking solutions for small-cell deployments. This paper explores coordinated usage of multiple radios (e.g., LTE and WiFi) to improve quality of service (QoS) in multi- radio heterogeneous networks. We focus on heterogeneous network deployments based on co- located WiFi-LTE small cells that allow for tighter multi-radio coordination. To evaluate QoS enhancements, we consider on-time throughput, a metric that captures the deliverable data rate for traffic with delay deadlines. A QoS-aware scheduling algorithm is designed to optimize on-time throughput. Cross-RAT coordination schemes are also explored to further enhance QoS. Through combining the QoS-aware scheduling algorithm and intelligent radio link assignment, we observe significant improvement in per user on-time throughput. Our results indicate that tightly coupled integrated LTE-WiFi small cell architectures can significantly increase the number of user achieving their targeted QoS.

We develop cognitive resource allocation scheme to mitigate co-tier and cross-tier interference in overlay femtocell networks. By exploiting statistical models for characterizing multitier interference, our scheme avoids prohibitive exchange of realtime interference statistics in the network. The proposed scheme is independently implemented at each femtocell and allocates resources distributedly in response to the probabilistic interference conditions in the network. This “self-organizing” framework can be useful to address interference management in dense, ad-hoc, and consumer-deployed femtocell networks. Simulation results show that the proposed scheme can improve the throughput of femtocell links while simultaneously reducing cross-tier interference.

Energy-efficient wireless communication is very important for battery-constrained mobile devices. For mobile devices in a cellular system, uplink power consumption dominates the wireless power budget because of RF power requirements for reliable transmission over long distances. Our previous work in this area focused on optimizing energy efficiency by maximizing the instantaneous bits-per-Joule metric through iterative approaches, which resulted in significant energy savings for uplink cellular OFDMA transmissions. In this paper, we develop energy efficient schemes with significantly lower complexity when compared to iterative approaches, by considering time-averaged bits-per-Joule metrics. We consider an uplink OFDMA system where multiple users communicate to a central scheduler over frequency-selective channels with high energy efficiency. The scheduler allocates the system bandwidth among all users to optimize energy efficiency across the whole network. Using time-averaged metrics, we derive energy optimal techniques in "closed forms" for per-user link adaptation and resource scheduling across users. Simulation results show that the proposed schemes not only have low complexity but also perform close to the globally optimum solutions obtained through exhaustive search.

Hierarchical femtocell architectures have become popular recently because of their potential to provide increased coverage and capacity in cellular systems. However, introduction of femtocells might reduce the overlay macrocell system performance due to increased interference caused to macrocel-lular users. With MIMO precoding, the Least Interference (LI) technique can be employed at the femtocellular base stations (FBSs) to minimize the interference to the macrocellular user. This approach maximizes the macrocellular throughput, but also results in significant reduction in femtocellular throughput. As is well-known, a Precoding Matrix Index (PMI) approach to beamforming can maximize the signal power at a desired receiver, with minimal feedback. In this paper, we apply mode adaptation between LI and PMI at the FBSs in order to increase both the macrocellular and femtocellular throughputs. We show that allowing for mode adaptation at each FBS improves the system performance when compared with using the same mode across the system, and a simple binary choice at each FBS can nearly achieve the optimum mode-adaptation performance. Analysis and simulation results in a multicell environment are presented to illustrate the improvement in system performance with the proposed techniques.

Power optimization techniques are becoming increasingly important in wireless system design since battery technology has not kept up with the demand of mobile devices. They are also critical to interference management in wireless systems because interference usually results from both aggressive spectral reuse and high power transmission and severely limits system performance. In this paper, we develop an energy-efficient power optimization scheme for interference-limited wireless communications. We consider both circuit and transmission powers and focus on energy efficiency over throughput. We first investigate a non-cooperative game for energy-efficient power optimization in frequency-selective channels and reveal the conditions of the existence and uniqueness of the equilibrium for this game. Most importantly, we discover a sufficient condition for generic multi-channel power control to have a unique equilibrium in frequency-selective channels. Then we study the tradeoff between energy efficiency and spectral efficiency and show by simulation results that the proposed scheme improves both energy efficiency and spectral efficiency in an interference-limited multi-cell cellular network.

In this paper, the impact of co-channel MIMO interference on MIMO Beamforming (MBF) in the desired link is investigated. The exact closed-form expression of the probability density function (pdf ) of the signal-to-interference ratio when the desired user has one antenna is derived. Due to the unavailability of the closed-form pdf expression for arbitrary (N,M) (where N and M are the number of transmit and receive antennas, respectively), a simple approximation for an arbitrary number of antennas is proposed and used to analyze the impact of various MIMO modes used by the co-channel base stations. Simulation results verify the validity of the analysis and the impact of cochannel MIMO modes on the desired MBF receiver is discussed.

Energy efficiency is becoming increasingly important for small form factor mobile devices, as battery technology has not kept up with the growing requirements stemming from ubiquitous multimedia applications. This paper addresses link adaptive transmission for maximizing energy efficiency, as measured by the "throughput per Joule" metric. In contrast to the existing water-filling power allocation schemes that maximize throughput subject to a fixed overall transmit power constraint, our scheme maximizes energy efficiency by adapting both overall transmit power and its allocation, according to the channel states and the circuit power consumed. We demonstrate the existence of a unique globally optimal link adaptation solution and develop iterative algorithms to obtain it. We further consider the special case of flat-fading channels to develop an upper bound on energy efficiency and to characterize its variation with bandwidth, channel gain and circuit power. Our results for OFDM systems demonstrate improved energy savings with energy optimal link adaptation as well as illustrate the fundamental tradeoff between energy-efficient and spectrum-efficient transmission.

In a cellular system, when multiple-input multiple-output (MIMO) techniques are employed, the interference scenario is quite complicated. In this paper, considering realistic propagation conditions including both path loss and Rayleigh fading, and considering the existence of unequal-power interferers, we analyze the statistical distributions of the post-processing SIRs for downlink space-time block coding (STBC) transmission with different co-channel MIMO interferers. Simulation results verify the validity of the theoretical analyses.

In order to meet the constantly increasing demand for ubiquitous, mobile access to the internet, next-generation mobile broadband communications systems based on OFDMA, such as IEEE 802.16 m, require a significant performance increase over previous generation systems, such as IEEE 802.16e-2005, particularly in cell-edge and average spectral efficiency. In this paper, we address the downlink adaptive frequency reuse (AFR) and multi-user MIMO (MU-MIMO) techniques which are considered to be the most promising candidates for meeting the requirements on cell-edge and average spectral efficiency of next-generation mobile broadband systems.

While the demand for battery capacity on mobile devices has grown with the increase in high-bandwidth multimedia rich applications, battery technology has not kept up with this demand. Therefore power optimization techniques are becoming increasingly important in wireless system design. Power optimization schemes are also important for interference management in wireless systems as interference resulting from aggressive spectral reuse and high power transmission severely limits system performance. Although power optimization plays a pivotal role in both interference management and energy utilization, little research addresses their joint interaction. In this paper, we develop energy-efficient power optimization schemes for interference-limited communications. Both circuit and transmit powers are considered and energy efficiency is emphasized over throughput. We note that the general power optimization problem in the presence of interference is intractable even when ideal user cooperation is assumed. We first study this problem for a simple two-user network with ideal user cooperation and then develop a practical non-cooperative power optimization scheme. Simulation results show that the proposed scheme improves not only energy efficiency but also spectral efficiency in an interference-limited cellular network.

Energy efficient communications in wireless communications is very important as mobile devices are battery- constrained. For mobile devices in a cellular system, uplink power consumption dominates the wireless power budget, due to the RF power requirements for reliable communications over long distances. Our previous work in this area demonstrated significant energy savings in uplink cellular OFDMA transmissions, with iterative approaches maximizing the instantaneous bits-per- Joule energy efficiency. In this paper, we use a time-averaged bits-per-Joule metric to develop low-complexity schemes. Specifically, we obtain closed-form solutions for energy-efficient link adaptation in frequency-selective channels. We also derive closed- form approaches for the maximum arithmetic and geometric mean energy-efficient schedulers. Simulation results show that the proposed schemes not only have low complexity but also perform close to the globally optimum solutions.

Severe cochannel interference in wireless cellular networks significantly affects users at cell edges. We propose a cost-effective cochannel interference avoidance (CIA) medium access control (MAC) scheme to improve network performance. For CIA-MAC, base stations judged as severe interferers transmit randomly and the transmission is controlled by wireless channel states to optimize the overall network performance while maintaining proportional fairness among users. Conditions for triggering CIA-MAC are derived and two simple trigger mechanisms are obtained. The CIA-MAC scheme requires low signaling overhead and only minor changes to the existing mobile systems. Simulation results show that the proposed scheme, CIAMAC, significantly outperforms traditional approaches through the avoidance of severe cochannel interference as well as the exploitation of multiuser diversity through cross-layer design.

Abstract Since battery technology has not progressed as rapidly as semiconductor technology, power efficiency has become increasingly important in wireless networking, in addition to the traditional quality and performance measures, such as bandwidth, throughput, and fairness. Energy‐efficient design requires a cross layer approach as power consumption is affected by all aspects of system design, ranging from silicon to applications. This article presents a comprehensive overview of recent advances in cross‐layer design for energy‐efficient wireless communications. We particularly focus on a system‐based approaches toward energy optimal transmission and resource management across time, frequency, and spatial domains. Details related to energy‐efficient hardware implementations are also covered. Copyright © 2008 John Wiley & Sons, Ltd.

When MIMO techniques are used in a cellular system, the interference environment can become quite complicated. In this paper, we evaluate the impact of co-channel MIMO interference on the performance of space-time block coding in the downlink of a cellular-like system. In particular, we derive closed-form expressions for the probability density functions of the resulting signal-to-interference ratio with and various MIMO modes used by the co-channel base stations. Simulation results verify the validity of the analysis.

Energy efficiency is becoming increasingly important for small form factor mobile devices, as battery technology has not kept up with the growing requirements stemming from ubiquitous multimedia applications. This paper addresses link adaptive transmission for maximization of energy efficiency rather than throughput. We extend our previous results for flat fading OFDMA to frequency-selective channels. Different from existing water-filling power allocation schemes that maximize throughput subject to overall transmit power constraints, our scheme adapts both overall transmit power and its allocation according to the states of all subchannels and circuit power consumption to maximize energy efficiency. We demonstrate the existence of a unique globally optimal link adaptation solution and provide iterative algorithms to obtain this optimum. Simulation results show at least a 15% improvement in energy utilization when frequency selectivity is exploited.

Energy-efficient transmission is an important aspect of wireless system design due to limited battery power in mobile devices. We consider uplink energy-efficient transmission in OFDMA systems since mobile stations are battery powered. We account for both circuit and transmit power when designing energy-efficient communication mechanisms and emphasize energy efficiency over peak rates or throughput. Both link adaptation and resource allocation schemes are developed to optimize the overall bits transmitted per Joule of energy, which allows for maximum energy savings in a network. Our simulation results show that the proposed schemes significantly improve energy efficiency.

A utility based resource allocation scheme is investigated for improving system performance of 802.16 OFDMA systems supporting a mix of multi-media traffic types. We show that a utility based approach can improve system capacity over conventional proportional-fair resource allocation schemes through more efficient allocation of only the required resources demanded by the QoS profile of each traffic class. It is also shown that using average channel- aware metrics for resource allocation allows for local linearization of the utility function, which results in linear complexity OFDMA resource allocation schemes. Further, performance with the reduced complexity scheme is similar to a previously known near-optimal algorithm with cubic complexity.

This paper compares the system performance of transmit diversity schemes for use in interference-limited cellular systems. We focus on evaluating the performance of Alamouti space time block codes (STBC) and cyclic delay diversity (CDD) codes, which are being actively considered for adoption within the 3 GPP-long-term-evolution (LTE) and 802.16e WiMAX standards. While the performance of transmit diversity schemes has been extensively evaluated in noise-limited scenarios, their performance in co-channel interference (CCI) limited cellular systems has received limited attention so far. As cellular systems adopt frequency reuse of one, co-channel interference from other cells becomes a major limitation to system performance. We present system simulation results comparing the performance of transmit diversity schemes using matched filter reception for a multi-carrier OFDM-based cellular system. In particular, the performance of STBC and CDD schemes is compared with respect to the evolving third generation 3 GPP-LTE standard. Our comparison highlights the spatial as well as the multi-user diversity aspects of these schemes and considers a variety of scenarios that will require their use. Our results show that CDD can significantly improve the system spectral efficiency and cell-edge performance over STBC, when used in combination with frequency-dependent scheduling. For open loop transmission without scheduling, the relative diversity gains of STBC and CDD at link layer depend on the interference scenario considered, while the system performance of these schemes is very similar.

This paper investigates the effects of co-channel interference on spatial diversity techniques. By analyzing distributions of post-processing signal to noise plus interference ratio (SINR) after matched filtering, we capture the performance characteristics of spatial diversity techniques in an interference limited environment. Using intuition from the theoretical analysis of a single carrier system, the performance of spatial diversity techniques in an OFDMA system with co-channel interference is also characterized. Our analytical and simulation results show that space time block code (STBC) schemes are more sensitive to co-channel interference than other spatial diversity techniques so their performance gain in a noise-limited environment can be lost in an interference-limited environment.

This paper investigates the interaction between multiuser diversity and spatial diversity in an interference-limited environment based on post-receiver-processing signal-to-interference-plus-noise ratio (SINR) distributions. If opportunistic scheduling is employed, spatial diversity effects limit the achievable multiuser diversity gain. This paper quantifies the interaction by using order statistic theory and shows a spatial diversity technique with a larger SINR variance can be more effective under opportunistic scheduling. Through analysis and simulations, the authors show that the cyclic delay diversity technique gets the most benefit from the opportunistic scheduling among likely spatial diversity techniques and outperforms space time block coding (STBC), even though STBC is generally considered the most effective transmit diversity technique in a noise-limited environment.

The modified trimmed mean (MTM) filters are known to possess desirable robustness and detail preservation properties. They combine the averaging operation with the median operation which implies that the filters also attenuate noise efficiently. We investigate multivariate extensions of the MTM filters. In the multivariate case, there is no unique way to define the MTM filters and hence there are several design options. A few of the most promising definitions for these filters and various design options are studied. The robustness of multivariate MTM filters is analyzed using the influence function approach. Filtering examples are also given using RGB color images.

This paper addresses the problems of image enhancement and restoration in the case of multivariate image data. Multivariate generalizations of median filtering are studied. The robustness properties of two such techniques are investigated using the influence function approach. Robust polynomial filters are introduced for applications where the original image has to be restored with high fidelity. Filtering examples are given using multivariate noise processes with equal component variances, unequal component variances, correlated noise components, and in the presence of outliers. Both simulated data and multivariate data from a range imaging sensor are used.>

In applications such as smoothing and enhancement of images, adaptive filtering techniques offer the flexibility needed for good performance with non-stationary observations. Many adaptive schemes can be based on the idea of determining the local statistics of the signal through appropriate tests on the data, to aid in the selection of a filtering procedure that is suited to the data. In the paper, the authors consider decision-directed or data-dependent adaptive filtering schemes that are based on order statistics. A general formulation for such a class of adaptive order statistics filters is presented. Approximate statistical performance analysis, especially in the presence of edges, may be carried out for this entire class of filters. The authors give examples of some existing filters that fit into this framework. The formulation also accommodates filters that employ multiple windows in their operation. To illustrate the potential of this class of multiple window (MW) filters, they construct and analyze simple filters, like the triple window median (TW-MED) and the triple window median of means (TW-MOM) filters, that are shown to yield useful performance. The class of mean-median hybrid (MMH) filters is also presented as a simple example which may be extended to give interesting performance.