In frequency division duplexing systems, downlink massive multiple-input multiple-output (MIMO) precoding algorithms rely on accurate channel state information (CSI) feedback from users. This paper investigates the tradeoff between the CSI feedback overhead and the resulting user performance in terms of achievable sum rate. Our approach consists of determining the precoding directly from the user feedback. We employ a deep learning-based design for an end-to-end precoding-oriented feedback architecture, including learned pilots, user compressors for finite-rate feedback, and base station processing to determine precoding vectors. We propose a novel loss function that maximizes the sum of achievable rates while minimizing the CSI feedback overhead. We consider both single- and multi-cell multi-user MIMO systems, analyzing the impact of intra- and inter-cell interference on the CSI feedback strategy design, as well as robustness. Simulation results demonstrate that our approach outperforms previous precoding-oriented methods and offers greater efficiency than conventional methods that separate CSI compression and precoding.

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. Based on this formulation, we derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. The analysis shows that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. To overcome this, a shift-of-origin transformation simplifies the optimization, leading to a closed-form expression for the parasitic reactance. The solution generalizes to arrays with multiple active and parasitic elements operating in multipath channels. The proposed hybrid architecture with parasitic elements outperforms conventional architectures in terms of energy efficiency.

This paper proposes a novel mixed panel architecture for channel state information (CSI) acquisition from uplink (UL) channel training in hybrid analog-digital beamforming systems with partially-connected structures in time-division duplex massive multiple-input multiple-output networks. The proposed architecture combines a few fully-digital (FD) panels with a large number of subarray-based panels for UL pilot reception, addressing CSI acquisition challenges while balancing performance and power efficiency. We then develop a unified method that can utilize measurements from both panel types to estimate the required CSI for analog precoder design. In particular, by recognizing that the dominant eigenvector of the panel covariance matrix is crucial for analog precoding, we propose an orthogonal matching pursuit-type algorithm to estimate it by exploiting channel sparsity in the angular domain. Additionally, we introduce a data-driven technique to optimize analog combiners for subarray-based panels during UL pilot reception. Numerical experiments demonstrate that our proposed method approaches the performance of an all-FD-panel architecture for UL pilot training while maintaining the low complexity of all-subarray-based-panel structures

Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel codes separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks.

Impedance-matching networks affect power transfer from the radio frequency (RF) chains to the antennas. Their design impacts the signal to noise ratio (SNR) and the achievable rate. In this paper, we maximize the information-theoretic achievable rate of a multiple-input-single-output (MISO) system with wideband matching constraints. Using a multiport circuit theory approach with frequency-selective scattering parameters, we propose a general framework for optimizing the MISO achievable rate that incorporates Bode-Fano wideband matching theory. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We apply this framework to a single electric Chu’s antenna and an array of dipole antennas. We compare the optimized achievable rate obtained numerically with other benchmarks like the ideal achievable rate computed by disregarding matching constraints and the achievable rate obtained by using sub-optimal matching strategies like conjugate matching and frequency-flat transmission. We also propose a practical methodology to approximate the achievable rate bound by using the optimal transmission coefficient to derive a physically realizable matching network through the ADS software.

Conventional achievable rate analysis using Shannon's theory does not assume practical constraints imposed by Bode-Fano wideband matching theory. This leads to an achievable rate bound that cannot be attained by practical matching networks. In this paper, we generalize the information-theoretic achievable rate of a single-input-single-output (SISO) system by incorporating wideband matching constraints at the transmitter. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We also propose a practical strategy to design a physically realizable matching network through the ADS software which attains the achievable rate bound with near-optimality. In simulations, we apply this framework to a Chu's antenna and compare the achievable rate performance with the conventional conjugate matching strategy.

Downlink massive multiple-input multiple-output (MIMO) precoding algorithms in frequency division duplexing (FDD) systems rely on accurate channel state information (CSI) feedback from users. In this paper, we analyze the tradeoff between the CSI feedback overhead and the performance achieved by the users in systems in terms of achievable rate. The final goal of the proposed system is to determine the beamforming information (i.e., precoding) from channel realizations. We employ a deep learning-based approach to design the end-to-end precoding-oriented feedback architecture, that includes learned pilots, users' compressors, and base station processing. We propose a loss function that maximizes the sum of achievable rates with minimal feedback overhead. Simulation results show that our approach outperforms previous precoding-oriented methods, and provides more efficient solutions with respect to conventional methods that separate the CSI compression blocks from the precoding processing.

Targeting ultra-reliable and scalable connectivity of extremely high data rates, in the 100 Gbps to Tbps range, at almost “zero-latency” in 6G systems would require taking advantage of breakthrough novel technology concepts, including THz wireless links, broadband and spectrally efficient RF-frontends for a variety of different bands, the employment of intelligent materials (e.g., reconfigurable intelligent surfaces) and the design of machine learning-based models, protocols, and management techniques. To materialize the 6G vision, novel system techniques will need to be devised, including channel modeling and estimation, waveforms, beamforming, and multiple-access schemes, all tailored to the particularities of the adopted breakthrough technologies. As challenging Tbit/s usage scenarios are becoming ever more relevant for 6G systems, including non-line of sight connectivity based on intelligent surfaces and ad hoc connectivity in fast-moving network topologies, e.g., based on drones or V2X links, performance targets need to be reassessed. In such scenarios, apart from the high data rates in the order of Tbit/s other critical parameters may arise as more relevant: range, reliability, adaptability, reconfigurability, and agility, to name just a few.

In the conventional sense, a passive intelligent reflecting surface (IRS) is perceived as an ideal phase shifter to the incident signal. It is assumed that the phase of the incident signal can be altered to any desired value without affecting its magnitude. In this paper, we question the veracity of this assumption which forms the basis for the communication model that is widely used in the scientific community. Although there exist rigorous electromagnetic (EM) based models to analyze and design metasurfaces, the same cannot be said about its successor, intelligent reflecting surface. Therefore, we attempt to present an EM-based model that accurately describes intelligent scattering by any arbitrary-shaped IRS. Our objective in this paper is to bridge the gap between the fundamental EM formulation for an IRS and the communication model that accurately captures its functioning. We use Method-of-Moments (MoM), a computational electromagnetic approach to quantify the intelligent scattering by an arbitrary-shaped IRS. The proposed theoretical model is then validated with computational EM simulation in Feko. We then adopt the general MoM-based model for a special case where each IRS element is a center-loaded wire. Closed-form expressions for pathloss and beamwidth are derived considering free space propagation. We show analytically and numerically, that the received power predicted by the conventional model vs. what is observed through computational EM simulations can differ by 6 dB. Furthermore, we demonstrate that the impact of optimizing an IRS using the conventional model, where each element is treated as an ideal passive phase shifter, can result in an additional$6-8$dB of power loss. As a final remark, we propose correction to the communication model that is currently used for IRS-aided networks when each IRS element is a center-loaded wire.

Periodic measurement reporting based beam management is not sufficiently agile for 5G New Radio (NR) and comes with significant overhead that scales with the number of beams and users. Furthermore, such an approach to beam selection is unlikely to be sufficient to avoid signal blocking in real world scenarios. We propose a method to accurately predict in advance the best serving beams and transmission points as users move through the network and thereby eliminate the need for frequent measurement reporting. Our prediction approach applies deep learning techniques similar to that used in Natural Language Processing (NLP) for translation/sentence completion tasks to the problem of predicting the best serving beams. The proposed solution enables the network to proactively switch users to new beams or cells to reduce blockage and handover related interruptions especially in high mobility scenarios. We evaluate our scheme in realistic scenarios using a new modeling technique where computer vision is used to obtain mobility traces of users from videos of live environments. We show significant benefits in terms of measurement report overhead reduction and signal-to-noise ratio enhancement through blockage prevention in several scenarios.

Network transmission over millimeter-wave (mmW) bands has a big potential to provide orders of higher bandwidth. However, beamforming is generally needed to compensate for the high path loss. As mmW antennas have a potentially large number of candidate beamforming directions, to achieve high network throughput, the finding of a high gain direction between a base station and each mobile in the mmW network may involve a large overhead if training signals are directly sent along all possible directions or according to a large volume of codebook. Taking advantage of the block sparse characteristics of the mmW channel and coexistence of legacy antennas, we propose a comprehensive design for more efficient beam direction finding. Different from existing compressive-sensing-based schemes which just take a random subset of directions to measure, taking advantage of the path clustering feature of the mmW channel, we develop a self-adaptive block sparse algorithm which can benefit from preliminary channel estimation during each iteration of the problem solving to significantly improve the overall channel estimation accuracy thus the beam alignment gain. We also explore two methods to exploit co-located legacy antennas to provide further guidance for transmission direction finding. Simulation results indicate that our proposed beam alignment scheme outperforms the baseline and peer schemes in terms of the beamforming gain and training cost. By taking advantage of the block sparse properties of mmW channel, our proposed design is able to achieve the transmission throughput comparable with the exhaustive direction search at much lower overhead.

This paper introduces a wireless communication protocol for industrial control systems that uses channel quality awareness to dynamically create network-device cooperation and assist the nodes in momentary poor channel conditions. To that point, channel state information is used to identify nodes with strong and weak channel conditions. We show that strong nodes in the network are best to be served in a single-hop transmission with transmission rate adapted to their instantaneous channel conditions. Meanwhile, the remainder of time-frequency resources is used to serve the nodes with weak channel condition using a two-hop transmission with cooperative communication among all the nodes to meet the target reliability in their communication with the controller. We formulate the achievable multi-user and multi-antenna diversity gain in the low-latency regime, and propose a new scheme for exploiting those on-demand, in favor of reliability and efficiency. The proposed transmission scheme is therefore dubbed adaptive network-device cooperation (ANDCoop), since it is able to adaptively allocate cooperation resources while enjoying the multi-user diversity gain of the network. We formulate the optimization problem of associating nodes to each group and dividing resources between the two groups. Numerical solutions show significant improvement in spectral efficiency and system reliability compared to the existing schemes in the literature. System design incorporating the proposed transmission strategy can thus reduce infrastructure cost for future private wireless networks.

We present a non-iterative downlink precoding approach for distributed massive multiple-input multiple output systems (DmMIMO) where users are served by overlapping clusters of transmission/reception points (TRP) and channel estimates for links outside the clusters are available. In contrast to traditional cellular systems, each user is served by its own cluster of transmission points and a DmMIMO TRP could be part of multiple clusters. We also propose a power control algorithm that ensures per site power constraints are satisfied when the proposed precoding approach is used. The algorithm extends straightforwardly to the multiple receive antenna case. Extensive simulation results are presented for various cluster sizes and inter-site distances for a dense urban environment with channels generated using ray tracing. Results show that spectral efficiency comparable to massive MIMO can be achieved for a dense deployment of small cells each with only a small number of antennas with our DmMIMO scheme without the need for extensive coordination between many access points.

Accurate Time of Arrival (TOA) estimation has many use cases, including 5G initial access and localization. However, due to multipath propagation and noise, the correlation-based TOA estimation may not be accurate. In this paper, a deep learning based framework is proposed for preamble detection and TOA estimation without the need of knowing the transmit waveform. Extensive simulations on both synthetic data and real measured data show that the proposed method improves prediction accuracy by about three times while keeping the same computational complexity in comparison to the correlation method. It also provides 1000x computational reduction compared to the template matching method without loss of accuracy.

Wireless motion control in the next generation of industrial control systems aims to provide the sensor/actuator devices on a factory floor with continuous closed-loop control updates from the controller entity, requiring communications with extremely low latency in the order of sub-ms and “cablelike” high reliability. This paper introduces a wireless communication protocol that uses channel state information (CSI) and cooperative communication among the devices to best utilize the radio resources and provide an ultra-reliable radio access. We propose to use CSI to identify devices with strong and weak channel conditions. We show that strong devices in the network are best to be served in a single-hop transmission with transmission rate adapted to their instantaneous channel conditions. Meanwhile, the remainder of time-frequency resources is used to serve the devices with weak channel condition, using a two-hop transmission with cooperative relaying. We formulate the optimization problem of partitioning time budget between the two groups and associating devices to each group. Numerical solution to the optimization problem and simulation results are provided. Thanks to combining multi-user diversity gain together with cooperative relaying, the proposed solution provides orders of magnitude improvement in system reliability, resulting in more than 10 dB signal to noise ratio (SNR) gain at 10-5system outage probability point, with respect to state-of-the-art protocols.

This paper proposes interference mitigation techniques for provisioning ultrareliable low-latency wireless communication in an industrial automation setting, where multiple transmissions from controllers to actuators interfere with each other. Channel fading and interference are key impairments in wireless communication. This paper leverages the recently proposed “Occupy CoW” protocol that efficiently exploits the broadcast opportunity and spatial diversity through a two-hop cooperative communication strategy among distributed receivers to combat deep fading, but points out that because this protocol avoids interference by frequency division orthogonal transmission, it is not scalable in terms of bandwidth required for achieving ultrareliability, when multiple controllers simultaneously communicate with multiple actuators (akin to the downlink of a multicell network). The main observation of this paper is that full frequency reuse in the first phase, together with successive decoding and cancellation of interference, can improve the performance of this strategy notably. We propose two protocols depending on whether interference cancellation or avoidance is implemented in the second phase, and show that both outperform Occupy CoW in terms of the required bandwidth and power for achieving ultrareliability at practical values of the transmit power.

We consider a downlink periodic wireless communications system, where multiple access points cooperatively transmit packets to a number of devices, e.g., actuators in an industrial control system. Each period consists of two phases: an uplink training phase and a downlink data transmission phase. Each actuator must successfully receive its unique packet within a single transmission phase; else, an outage is declared. Such an outage can be caused by two events: a transmission error due to transmission at a rate that the channel cannot actually support or time overflow, where the downlink data phase is too short, given the channel conditions to successfully communicate all the packets. We determine the closed-form expressions for the time overflow probability when there are just two field devices, as well as the transmission error probability for an arbitrary number of devices. In addition, we provide upper and lower bounds on the time overflow probability for an arbitrary number of devices. We propose a novel variable-rate transmission method that eliminates time overflow. Detailed system-level simulations are used to identify system design guidelines, such as the optimal amount of training time, as well as for benchmarking the proposed system design versus non-cooperative cellular, cooperative fixed-rate, and cooperative relaying.

Future wireless networks envision ultra-reliable, ultra-low latency communication with efficient use of the limited wireless channel resources. Closed-loop retransmission protocols, such as automatic repeat request (ARQ), where retransmission of a packet is enabled using a feedback channel has been adopted since early days of wireless telecommunication to achieve the required reliability. The performance of such protocols is strongly dependent on the feedback channel reliability. Focusing on the problem of feedback errors, a new method of acknowledging packet delivery to overcome unreliability of feedback channel is proposed in this paper. The proposed method is based on backwards composite acknowledgment of multiple packets and provides the transmitter with additional design parameters to configure ultra-reliable communication for a user depending on channel quality. Numerical analysis are presented showing orders of magnitude increase in reliability for the proposed method compared to traditional ARQ at the cost of a small increase in average experienced delay.

We investigate the impact of human body blocking on the performance of millimeter wave wireless systems in an urban hotspot scenario. The effects of human body blocking at higher frequency bands are expected to be more severe than at traditional cellular bands because of worse signal propagation through the human body. To study the temporal behaviour of signals with blocking in a realistic setting, we employ a video clip of human activity in a public square and model the spatio-temporal distribution of individuals by extracting the locations of the people in each frame of the clip and embedding that information within various environment models in a ray tracing tool. This approach enables us to study the human body blocking effects in the context of realistic pedestrian trajectories taking into account the social interactions among people. We present extensive results from the ray tracing tool on the signal to noise ratio statistics and their temporal variation from multiple transmission points in various environments involving human body blocking. Our results show that maintaining good signal to noise ratio requires frequent switching between transmission points, and joint transmission may be preferred over switching to eliminate handover interruption. Our study also shows that multipath propagation through reflections from structures in the surrounding environment can mitigate to some extent but not eliminate the losses because of human body blocking.

Emerging 5G networks will not only offer higher link rates, but also integrate a variety of Radio Access Technologies (RATs) in order to provide ultra-reliable broadband access to a wide range of applications with high throughput and low latency requirements. SDN-enabled dynamic path selection is of critical importance in exploiting the collective transmission resources in such heterogeneous multi-RAT environments and delivering excellent user performance. In the present paper we propose the `best-rate' path selection algorithm for multi-RAT networks with various types of traffic flows. The best-rate algorithm accounts for the radio conditions and performance requirements of individual flows as well as the load conditions at the various access points. We analytically establish that the rates received by the various flows under the best-rate path selection, in conjunction with local fair resource sharing at the individual access points, are close to globally Proportional Fair. Detailed simulation experiments demonstrate that the best-rate algorithm achieves significant gains in terms of user-perceived throughput performance over various baseline policies.

The paper describes the principles of a novel mechanism that is aiming at providing unprecedented Quality of Experience (QoE) in next generation mobile networks. The main benefits include: (i) enabling dynamic IP tunnel management (establishing and tearing down) with terminating point in the mobile device instead of the traditional base station; (ii) enabling multiple parallel tunnels (e.g., Generic Routing Encapsulation (GRE)) between an anchor node and a mobile device, where a specific tunnel may support differentiated Quality of Service (QoS) requirements for a targeted service class; (iii) enabling advanced network protocol support for service flows split across multiple Radio Access Technologies (M-RATs), whenever it may be deemed necessary to support and maintain a targeted QoS level per service flow; (iv) allowing to aggregate the necessary bandwidth to meet the QoS requirements per service flow if no single RAT can fulfill them alone; (v) supporting massive M-RAT capacity aggregation for enhanced scalability and very large throughput.

This paper addresses the design of a novel ultra-broadband wireless access system, as an alternative to fiber to the home (FTTH) deployments. Hence, the system must be capable of delivering very high data rates to indoor users without the need of deploying fiber to homes. We show that a hybrid wireless access system with outdoor deployed cell sites operating jointly across high and low spectrum bands enables ultra-broadband wireless connectivity to indoor users with indoor antennas close to the windows. On the other hand, a system operating in the 28 GHz band alone does not achieve the target performance. We have created a representative suburban neighborhood via the Bell Labs WiSE (Wireless System Engineering) 3D ray tracing tool and explored multiple system design options to provide such high data rates to homes. Via 3D ray tracing environment simulations, we demonstrate that sustained throughputs on the order of 100Mbps at the cell edge may be delivered to multiple homes within the representative suburban neighborhood. Further, we studied the sensitivity to parameters such as density of sites, number of active beams, Effective Isotropic Radiated Power (EIRP) and interference characteristics.

We employ a ray tracing framework to extract the three dimensional (3D) channel parameters characterizing outdoor small cell deployments providing services to indoor users close to the exterior wall and windows at 28 GHz and 3.5 GHz. The wireless channel is highly dependent on 3D antenna patterns, the environment specific characteristics such as 3D geometry of the buildings, materials used for building construction and their specific propagation properties. These dependencies are particularly important at higher frequencies, where the range of radio signals may be significantly limited due to the path loss and shadowing by obstacles. A good understanding of the channel propagation characteristics at these frequencies, and their correlation to propagation at lower bands, is thus critical for designing and deploying reliable radio systems. The ray tracing approach has substantial merits in absence of relevant field measurements, facilitating extraction of the 3D site-specific channel parameters pertaining to 28 GHz small cell deployments and giving useful insights to foster innovation of new wireless technologies at 28 GHz. Furthermore, we derive channel statistics at both 28 GHz and 3.5 GHz for the same environment drawing conclusions on path loss, delay spread, and angle spread in azimuth and vertical directions. Our results indicate that for the environment considered the angle spread of azimuth angle of arrival exceeds 20° in more than 45% of the terminal locations even at 28 GHz, suggesting that very high beamforming gain at the terminal is not feasible. Furthermore, with a 15° grid of beams based transmitted signal, the best beam at 28 GHz is different from that 3.5 GHz for almost 60% of the locations.

Transmission in millimeter wave (mmW) band has a big potential to provide orders of higher wireless bandwidth. To combat the high channel loss in high frequency band, beamforming is generally taken to transmit along the direction that provides the maximum transmission gain. This requires the MAC protocol to facilitate the finding of the optimal beamforming direction. Exiting protocol suggests the rotational channel measurement which may introduce high measurement cost, and compromise the transmission capacity. This paper presents a comprehensive design for more efficient directional beam alignment in mmW cellular networks. Instead of exhaustively searching all possible beamforming directions at the transmitter (TX) and the receiver (RX), our proposed scheme selects only a fairly small number of TX and RX beam pairs to facilitate effective beam alignment. To avoid long and resource-consuming exhaustive search, our scheme not only takes advantage of the low rank characteristics of the channel to estimate the full channel information with a small number of measurements, but also further exploits the channel estimation from initial measurements to guide the selection of future beam pairs for more effective measurements later. These strategies help to speed up the process of finding satisfactory beam pairs. We perform extensive simulations to evaluate the performance of our proposed schemes, and our results demonstrate our scheme can significantly outperform other schemes in terms of measurement effectiveness and cost efficiency.

The concepts of Big Data have became intertwined with those of the Internet of Things, creating mental pictures of a fully connected, all-encompassing, cyber-physical world, where each and every object will contribute with information to a "fully aware" society. Academic works are presenting this as the natural evolution for our current technologies. The panel looks at these promises from the hard perspective of reality: what is being done, how much it cost, what needs to be developed, and what can be expected in the near and mid-term.

Offering good Quality of Experience (QoE) in stadiums poses unprecedented challenges to Wireless Operators, due to extreme traffic conditions. During popular sporting events, there could be tens of thousands of active users present in a relatively small area, downloading/uploading pictures and video clips through smart phone applications. This results in a large traffic density and drives requirements for high capacity, and yet economically feasible solutions for stadiums. Ensuring high capacity required in such open and heavily interfered environments is a daunting task. Small cells offer promising advantages to increase the spectral efficiency of wireless systems, thanks to their small frequency reuse factor. We show through environment simulations that it is possible to serve thousands of users in a stadium using small cell technologies. In particular, Wi-Fi and LTE small cells can be used in stadiums to complement each other to cope with the increasing capacity demand.

Communication can consume a significant fraction of the energy for many simple sensor devices for which battery life is an important consideration in deployment. Battery power is consumed not only by transmit power amplifier but also in the radio frequency circuits and digital processors during transmission and reception. When communication requirements are bursty, many devices incorporate a `sleep' state where the circuit power consumption is also reduced by turning circuits off. Delaying transmission can allow devices to sleep more and conserve energy. We consider the optimal tradeoff between receiver energy consumption and average throughput, and derive insights on multi-user downlink communication. We reformulate the problem with generalized power constraints on the transmitter's and the receiver's power consumption: depending on their states, either transmit/receive or sleep, they consume different amounts of power. We show how these changes of power constraints affect average spectral efficiency. In Gaussian broadcast channels, taking into account the receivers' power constraints, we show that multi-user transmission schemes, previously proven to be optimal for maximizing spectral efficiency, such as superposition coding and dirty paper coding (DPC) are not always optimal. We characterize the condition, under which these schemes remain optimal, in terms of receivers' power constraints. These models are suited for machine-to-machine (M2M) communications and wireless sensor networks where 1) transmitters and/or receivers are battery-powered devices, 2) their locations are static once deployed, and 3) their data characteristic is not delay-sensitive.

Mobile operators are leveraging WiFi to relieve the pressure posed on their networks by the surging bandwidth demand of applications. However, operators often lack intelligent mechanisms to control the way users access their WiFi networks. This lack of sophisticated control creates poor network utilization, which in turn degrades the quality of experience (QoE). To meet user traffic demands, it is evident that operators need solutions that optimally balance user traffic across cellular and WiFi networks. Motivated by the lack of practical solutions in this space, we design and implement ATOM - an end-to-end system for adaptive traffic offloading for WiFi-LTE deployments. ATOM has two novel components: (i) A network interface selection algorithm that maps user traffic across WiFi and LTE to optimize user QoE and (ii) an interface switching service that seamlessly re-directs ongoing user sessions in a cost-effective and standards-compatible manner. Our evaluations on a real LTE-WiFi testbed using YouTube traffic reveals that ATOM reduces video stalls by 3-4 times compared to naive solutions.

For wireless systems in which randomly arriving devices attempt to transmit a fixed payload to a central receiver, we develop a framework to characterize the system throughput as a function of arrival rate and per-device data rate. The framework considers both coordinated transmission (where devices are scheduled) and uncoordinated transmission (where devices communicate on a random access channel and a provision is made for retransmissions). Our main contribution is a novel characterization of the optimal throughput for the case of uncoordinated transmission and a strategy for achieving this throughput that relies on overlapping transmissions and joint decoding. Simulations for a noise-limited cellular network show that the optimal strategy provides a factor of four improvement in throughput compared with slotted ALOHA. We apply our framework to evaluate more general system-level designs that account for overhead signaling. We demonstrate that, for small payload sizes relevant for machine-to-machine (M2M) communications (200 bits or less), a one-stage strategy, where identity and data are transmitted optimally over the random access channel, can support at least twice the number of devices compared with a conventional strategy, where identity is established over an initial random-access stage and data transmission is scheduled.

We consider a wireless time-slotted random access channel where user arrivals are characterized by a Poisson process. Each user comes with a fixed payload, which has to be transmitted in the slot in which it arrives. If the transmission is successful, the user leaves the system, else it is dropped. The receiver and users are assumed to have knowledge of the arrival rate λ, but they are not aware of the actual number of users simultaneously attempting to communicate during a given time slot. In contrast to a conventional slotted ALOHA-based strategy where the channel is partitioned into orthogonal subchannels and each user communicates on a randomly chosen subchannel, we propose a novel strategy whereby users transmit simultaneously over the entire channel resource and the receiver jointly decodes the transmissions. Under the proposed strategy, neither users nor the receiver have prior knowledge of the active user set. Our analysis concretely demonstrates that the proposed strategy is optimal in terms of maximizing the average throughput among all uncoordinated strategies. Numerical results show that the proposal provides an order of magnitude throughput improvement compared to slotted ALOHA in a single-cell environment under a 10% maximum outage constraint.

The explosive growth in data traffic is resulting in a spectrum crunch forcing many wireless network operators to look towards refarming their 2G spectrum and deploy more spectrally efficient Long Term Evolution (LTE) technology. However, mobile network operators face a challenge when it comes to spectrum refarming because 2G technologies such as Global System for Mobile (GSM) is still widely used for low bandwidth machine-to-machine (M2M) devices. In this paper we propose a novel solution to provide GSM connectivity within an LTE carrier through an efficient overlay by reserving a few physical resource blocks for GSM. With this approach, operators can refarm their 2G spectrum to LTE efficiently while still providing some GSM connectivity to their low data rate M2M customers. Furthermore, spectrum can be dynamically shared between LTE and GSM. An approach similar to that proposed in this paper can also be applied for other narrow band technology overlays over LTE.

Video streaming, in particular, hypertext transfer protocol based (HTTP) adaptive streaming (HAS) of video, is expected to be a dominant application over mobile networks in the near future. The observation that the base station can alter the video quality requested by a HAS client to its server by controlling the over-the-air throughput from the base station to the client implies that the base station can jointly maximize aggregate video quality of all the HAS flows and throughput of data flows that it serves. We formulate a utility maximization problem that separately takes into account different utility functions for video and data flows and show that the utility maximization can be achieved through an algorithm, we term adaptive guaranteed bit rate (AGBR), wherein target bit rates are calculated for each HAS flow and passed on to an underlying minimum rate proportional fair scheduler that schedules resources across all the flows. This approach has the advantage that it retains the existing scheduling function in the base station with a separate function to compute the target bit rates for the video flows allowing them to only change slowly over time in order to avoid frequent video quality changes. Through analytical modeling and simulations we show that the proposed algorithm can achieve required fairness among the video flows as well as automatically and fairly adapt video quality with increasing congestion thereby preventing data flow throughput starvation.

The growing popularity of Machine-to-Machine (M2M) communications in cellular networks is driving the need to optimize networks based on the characteristics of M2M, which are significantly different from the requirements that current networks are designed to meet. First, M2M requires large number of short sessions as opposed to small number of long lived sessions required by the human generated traffic. Second, M2M constitutes a number of battery operated devices that are static in locations such as basements and tunnels, and need to transmit at elevated powers compared to the traditional devices. Third, replacing or recharging batteries of such devices may not be feasible. All these differences highlight the importance of a systematic framework to study the power and energy optimal system design in the regime of interest for M2M, which is the main focus of this paper. For a variety of coordinated and uncoordinated transmission strategies, we derive results for the optimal transmit power, energy per bit, and the maximum load supported by the base station, leading to the following design guidelines: (i) frequency division multiple access (FDMA), including equal bandwidth allocation, is sum-power optimal in the asymptotically low spectral efficiency regime, (ii) while FDMA is the best practical strategy overall, uncoordinated code division multiple access (CDMA) is almost as good when the base station is lightly loaded, (iii) the value of optimization within FDMA is not significant in the regime of interest for M2M.

The explosive growth in data traffic is resulting in a spectrum crunch forcing many wireless network operators to look towards refarming their 2G spectrum and deploy more spectrally efficient Long Term Evolution (LTE) technology. However, mobile network operators face a challenge when it comes to spectrum refarming because 2G technologies such as Global System for Mobile (GSM) is still widely used for low bandwidth machine-to-machine (M2M) devices. M2M devices typically have long life cycles, e.g. smart meters, and it is expensive to migrate these devices to newer technology since a truck roll will typically be required to the site where a device is deployed. Furthermore, with cost of 2G modules several times less than that of LTE, even newly deployed M2M devices tend to adopt 2G technology. Nevertheless, operators are keen to either force their 2G M2M customers to migrate so that they can refarm the spectrum or set aside a portion of the 2G spectrum for continuing operating 2G and only refarm the rest for LTE. In this paper we propose a novel solution to provide GSM connectivity within an LTE carrier through an efficient overlay by reserving a few physical resource blocks for GSM. With this approach, operators can refarm their 2G spectrum to LTE efficiently while still providing some GSM connectivity to their low data rate M2M customers. Furthermore, spectrum can be dynamically shared between LTE and GSM. An approach similar to that proposed in this paper can also be applied for other narrow band technology overlays over LTE.

As it becomes common for wireless service providers (WSP) to employ multiple heterogeneous radio access technologies (RAT), the management of the combined resources across multiple RATs arises as an important issue. The WSP's objective is to assign different users to the different RATs so as to maximize network capacity (or total utility) while ensuring that individual users' quality of service (QoS) requirements are met. In this paper, we consider this resource allocation problem for two scenarios: voice communication and video communication. For voice communication, we propose a stable and optimal assignment scheme based on the deferred acceptance algorithm for both static and online cases. For video communication, identifying the NP-hardness of the problem, we propose and compare a set of heuristic algorithms including a low-complexity, high-performance scheme.

Physical layer techniques have come a long way and can achieve very close to Shannon capacity for point-to-pint links. It is apparent that, to further improve network capacity significantly, we have to resort to concurrent transmissions. Many concurrent transmission techniques (e.g., zero forcing, interference alignment and distributed MIMO) are proposed in which multiple senders jointly encode signals to multiple receivers so that interference is aligned and each receiver is able to decode its desired information. In this paper, we investigate the constraints and challenges of using interference alignment. Our main contribution is conducting the first systematic investigation on the key issue of identifying opportunities for interference alignment. We identify diverse, novel scenarios for using interference alignment. We show that identifying opportunities for interference alignment in the general case is computational challenging. However, we also present a promising, distributed algorithm for identifying a wide range of opportunities for interference alignment using a unifying framework based on the degree of freedom. Our second contribution is evaluating key practical implementation issues.

Collisions in overlapping channels can be a major problem in the deployment of high-speed OFDM networks. In this paper, we present Remap, a simple, novel paradigm for handling collisions in overlapping OFDM channels. Remap introduces a novel concept of retransmission permutation that permutes the bit-to-subcarrier assignment after each transmission, departing from the traditional, simply-repeat paradigm. Remap is simple to implement and able to exploit collision-free subcarriers to decode frames despite successive collisions in overlapping channels. We apply Remap to 802.11g to demonstrate that the diversity created by remapped frames can substantially improve decoding efficiency and improve wireless throughput. We implement our technique in software radio and demonstrate that it has potential to be deployed with simple software and firmware updates.

We present muNet, a wireless mesh network design and implementation to harness the multiuser capacity of wireless channels. Traditionally, media access control is designed to schedule one transmission between one sender and one receiver without interference at any given time. However, this design is suboptimal in terms of achieving the multiuser capacity of multi-access wireless channels. In muNet, we implement effective physical layer techniques called superposition coding and successive interference cancellation to enable simultaneous unicast transmissions from a single transmitter to multiple receivers as well as from multiple transmitters to a single receiver. We design the first practical MAC protocol that leverages such a physical layer and exposes the multiuser capacity to upper layers. We also present a simple, effective routing protocol that increases simultaneous transmission opportunities for the MAC layer. A proof-of-concept muNet is implemented on the GNU radio platform. Measurements on the implementation shows that the throughput gains of muNet are significant (up to 93%).

Self-optimization of the network, for the purposes of improving overall capacity and/or cell edge data rates, is an important objective for next generation cellular systems. We propose algorithms that automatically create efficient, soft fractional frequency reuse (FFR) patterns for enhancing performance of orthogonal frequency division multiple access (OFDMA) based cellular systems for forward link best effort traffic. The Multi- sector Gradient (MGR) algorithm adjusts the transmit powers of the different sub-bands by systematically pursuing maximization of the overall network utility. We show that the maximization can be done by sectors operating in a semi-autonomous way, with only some gradient information exchanged periodically by neighboring sectors. The Sector Autonomous (SA) algorithm adjusts its transmit powers in each sub-band independently in each sector using a non-trivial heuristic to achieve out- of-cell interference mitigation. This algorithm is completely autonomous and requires no exchange of information between sectors. Through extensive simulations, we demonstrate that both algorithms provide substantial performance improvements. In particular, they can improve the cell edge data throughputs significantly, by up to 66% in some cases for the MGR, while maintaining the overall sector throughput at the same level as that achieved by the traditional approach. The simulations also show that both algorithms lead the system to "self-organize" into efficient, soft FFR patterns with no a priori frequency planning.

The problem of multiple multicast sessions with intra-session network coding in time-varying networks is considered. The network-layer capacity region of input rates that can be stably supported is established. Dynamic algorithms for multicast routing, network coding, power allocation, session scheduling, and rate allocation across correlated sources, which achieve stability for rates within the capacity region, are presented. This work builds on the back-pressure approach introduced by Tassiulas, extending it to network coding and correlated sources. In the proposed algorithms, decisions on routing, network coding, and scheduling between different sessions at a node are made locally at each node based on virtual queues for different sinks. For correlated sources, the sinks locally determine and control transmission rates across the sources. The proposed approach yields a completely distributed algorithm for wired networks. In the wireless case, power control among different transmitters is centralized while routing, network coding, and scheduling between different sessions at a given node are distributed.

We study uncoordinated multiple access using the orthogonal frequency division multiple access (OFDMA) technique where multiple users independently and randomly pick a sub-carrier from a set of sub-carriers for transmission in each slot. Signals transmitted on distinct sub-carriers are orthogonal to each other while signals transmitted by different users on the same sub-carrier interfere with each other. We study average throughput and outage probability performance of such a system in the asymptotic limit of large numbers of sub-carriers and users while keeping the ratio of number of users to sub-carriers fixed, for non-fading, flat fading, and frequency-selective fading additive white Gaussian noise channels. The performance is compared to that of a system in which all users spread their signal over all the sub-carriers as in a code division multiple access (CDMA) system. Our results show that at high signal-to-noise ratio (SNR), transmission without spreading results in higher throughput but requires more retransmissions on the average. At low SNRs, the throughput performance is comparable but the system with spreading requires smaller average number of retransmissions.

A major barrier for the adoption of wireless mesh networks is severe limits on throughput. Many in-network packet mixing techniques at the network layer [1], [2], [3] as well as the physical layer [4], [5], [6] have been shown to substantially improve throughput. However, the optimal mixing algorithm that maximizes throughput is still unknown. We propose iPack, an algorithm for in-network generation of composite packets that integrates coding at two different layers of the protocol stack: XOR-based network coding and physical layer superposition coding. Using extensive simulations, we find that the throughput gain of the joint coding iPack algorithm is 30% more than the better performer of network coding and superposition coding in a wide range of scenarios, and automatically takes advantage of the best available coding opportunities. In a typical wireless mesh network when more traffic is between the clients and access points, the average throughput improvement of iPack, our joint optimization scheduler, can be 324%, while there can be little gain (less than 10%) if network coding alone is used. We also validate our results by implementing iPack on a small-scale testbed based on GNU Radio.

We describe an algorithm for sub-carrier and power allocation that achieves out-of-cell interference avoidance through dynamic fractional frequency reuse (FFR) in downlink of cellular systems based on orthogonal frequency division multiple access (OFDMA). The focus in on the constant-bit-rate (CBR) traffic type flows (e.g., VoIP). Our approach is based on the continuous "selfish" optimization of resource allocation by each sector. No a priori frequency planning and/or inter-cell coordination is required. We show, both analytically (on a simple illustrative example) and by simulations (of a more realistic system), that the algorithm leads the system to "self-organize" into efficient frequency reuse patterns.

This paper studies the fundamental operational limits of a class of Gaussian multicast channels with an interference setting. In particular, the paper considers two base stations multicasting separate messages to distinct sets of users. In the presence of channel state information at the transmitters and at the respective receivers, the capacity region of the Gaussian multicast channel with interference is characterized to within one bit. At the crux of this result is an extension to the multicast channel with interference of the Han-Kobayashi or the Chong-Motani-Garg achievable region for the interference channel.

A major barrier for the adoption of wireless mesh networks is severe limits on throughput. In this paper, we apply superposition coding to substantially improve network capacity of large, dense wireless mesh networks. Superposition coding is a physical layer technique that allows a transmitter to simultaneously send independent packets to multiple receivers. While superposition coding has been studied extensively by the physical layer community, we present the first design of practical and effective MAC protocols to take advantage of superposition coding in wireless mesh networks. Extensive evaluations show that superposition coding can be a practical method to increase the throughput of large, dense wireless mesh networks. Specifically, in a mesh network with 2 to 64 active receivers and one gateway, we show that our system can increase throughput up to 154%, with average gain ranging from 10% to 21%. When there are multiple gateways forming a mesh network, our system gains up to 98%, with average gain ranging from 24% to 46%. These results clearly demonstrate the potential benefits of our system. We also present results from an implementation of superposition coding using GNU Radio.

Network coding has been shown to be useful for throughput and reliability in various network topologies, under a fixed-rate, point-to-multipoint wireless network model. We study the effect of introducing a wireless network model where link capacity depends on the network geometry and the signal to interference and noise ratio. In particular, we compare strategies with and without network coding on a multicast network with and without fading, and on single-user multiple path networks with fading. For the multicast network without fading, we find that the network geometry affects which scheme attains higher throughput. For the case with fading, we compare the throughput-outage probability curves achieved by network coding and repetition schemes. For the multiple path networks, we further consider the case where multiple simultaneous transmissions of identical information signals can be combined at a receiver. We find that the relative performance of the schemes we consider depends on the network geometry, the ratio of signal to noise power, whether multiple simultaneous transmissions can be combined, and the operating point on the throughput-outage probability curve

The growth and evolution in wireless networks continues to increase the importance of operational expenditures by service providers, which in turn creates demand for network solutions that will reduce operator intervention such as autoconfiguration. In this paper we study pseudorandom noise (PN) offset assignment as an example of the autoconfiguration of a shared resource in a wireless network. We explore various types of data that might be available for autoconfiguration, and propose several online planning algorithms. We study how these data influence the choice of algorithm and impact the quality of the PN offset plan obtained. We also explore how these algorithms and the PN offset plans generated can be changed if we relax the online requirement and allow in-service base stations to change their PN offsets

Multicasting is emerging as an important application in both cellular networks and wireless sensor networks. We consider several schemes for enhancing throughput and coverage for streaming multicast sessions by allowing intermediate nodes to relay information from the source. The schemes exploit the broadcast nature of the wireless medium, and techniques such as received signal combining and network coding to enhance performance while guaranteeing that a constant streaming delay is experienced by every user. The performance metric used for comparison is the throughput achieved at a given area coverage probability. Specifically, we study techniques such as decode-and-forward, amplify-and-forward, and pipelining and compare their performances through simulations in the context of a cellular network with relays and show that significant performance benefits are obtained through such schemes

We consider different transmission options on the reverse link of cellular systems for packet data. The different transmission options are classified based on the nature of in-cell and out-of-cell interference power statistics. The categories are: (a) no in-cell interference, averaged out-of cell interference; (b) no in-cell interference, bursty out-of-cell interference; and (c) averaged in-cell interference, averaged out-of-cell interference. Depending on whether the reverse link transmission is time multiplexed one user at a time transmission, or simultaneous transmission by multiple users with or without in-cell orthogonality, the interference structure falls into one of the above three categories. We analyze the throughput performance of the system in each of these cases when incremental redundancy is employed to combat uncertainty in the interference power. We compare the different options under an in-cell rise-over-thermal (IROT) constraint and provide some insights for reverse link design for next-generation cellular systems. Our results show that transmission option (a) with an optimal choice of the number of simultaneous transmissions within the cell has the best performance over several different scenarios. Time-multiplexed transmissions, despite the bursty out-of-cell interference power structure, has throughput comparable to that of a multiple-user orthogonal transmission system for small cells where mobiles have sufficient transmit power to meet the target IROT.

Wireless backhaul communication is expected to play a significant role in providing the necessary backhaul resources for future high-rate wireless networks. Mesh networking, in which information is routed from source to destination over multiple wireless links, has potential advantages over traditional single-hop networking, especially for backhaul communication. We develop a linear programming framework for determining optimum routing and scheduling of flows that maximizes throughput in a wireless mesh network and accounts for the effect of interference and variable-rate transmission. We then apply this framework to examine the throughput and range capabilities for providing wireless backhaul to a hexagonal grid of base stations, for both single-hop and multihop transmissions for various network scenarios. We then discuss the application of mesh networking for load balancing of wired backhaul traffic under unequal access traffic conditions. Numerical results show a significant benefit for mesh networking under unbalanced loading.

This paper investigates joint power control and routing policies for general multihop wireless networks when all the transmitting nodes are subject to a long-term average power constraint. The main contribution of this paper is to propose online power and rate control algorithms and prove that these policies stabilize the entire queuing network whenever the packet arrival rates at each node are in the corresponding region of achievable rates. The online policies are time varying and based on the queue size at each node and the instantaneous channel conditions. The theoretical results are supported by simulations for the illustrative cases of both a multiple-access channel and a relay channel

Multihop wireless networking has traditionally been viewed in the context of ad-hoc and peer-to-peer networks. We consider the application of multihop networking in wide area cellular systems and evaluate potential capacity enhancements. A cellular system with relays that store and forward information from the base station to the terminals on the downlink is considered. With geographical routing for uniform placement of relays within a cell, upper bounds to throughput gains are derived for a transmission scheme that guarantees equal long term throughput to all users. Our results show that, for a large number of hops, the gain is limited by the rate on the links between the base and the closest set of relays

Guard channels have been proposed to minimize handoff call dropping when mobile hosts move from one cell to another. Code-division multiple-access (CDMA) systems are power- and interference-limited. Therefore, guard capacity in CDMA networks is soft, that is, a given capacity corresponds to variable number of connections. Thus, it is essential to adjust the guard capacity in response to changes in traffic conditions and user mobility. We propose two schemes for managing downlink CDMA radio resources: guard capacity adaptation based on dropping (GAD), and guard capacity adaptation based on prediction and dropping (GAPD). In both schemes, the guard capacity of a cell is dynamically adjusted so as to maintain the handoff dropping rate at a target level. In the second scheme, there is an additional, frequent adjustment component where guard capacity is adjusted based on soft handoff prediction. We show through extensive simulations that GAD and GAPD control the handoff dropping rate effectively under varying traffic conditions and system parameters. We also find that GAPD is more robust than GAD to temporal traffic variations and changes in control parameters.

We consider scheduling strategies for multiantenna and multibeam cellular wireless systems for high-speed packet data services on the downlink. We establish a fundamental connection between the stability region of the queuing system and the set of feasible transmission rates, which provides the basis for the scheduling algorithm proposed in this paper. Transmission using adaptive steerable beams and fixed sector beams are considered and average delay versus throughput results are obtained through simulations for the proposed scheduling scheme in each case. While in single antenna systems multiuser diversity gains are achieved by the scheduling algorithms that transmit to a single user in each scheduling interval, our results show that with multiple antennas, transmitting to a carefully chosen subset of users has superior performance. The multiantenna scheduling problem is closely related to the problem of coordinated scheduling for transmission through multiple base stations, where a user can receive signals from several base stations simultaneously. We consider the special case when three single-antenna base stations are allowed to cooperate and transmit to the users in the triangular region between the base stations and propose scheduling strategies that demonstrate significant gains.

We formulate and analyze the problem of optimal downlink scheduling with instantaneous channel and queue size information when both power and bandwidth may be adaptively split among multiple users. We derive optimal solutions of low computational complexity, as well as faster and simpler approximations, to various versions of this problem when the power, rate, and bandwidth allocations to the users can all take continuous values. For this case, we show that the optimal scheme requires transmission to no more than two users during each time slot when users can receive at arbitrary rates, even when the user rate per unit of bandwidth is upper bounded by the best available modulation scheme. Our methods also extend easily to incorporate other intuitive constraints such as upper limits on user rates to improve frame fill efficiency. Simulation results suggest that the simple approximations work nearly as well as the throughput optimal schemes when continuous bandwidth and power partitions are allowed. In practice, the rate and bandwidth assignments to users take discrete values, and we present heuristic methods motivated by the continuous optimum to this discrete case.

Future cellular wireless networks could include multihop transmission through relays. We propose a centralized downlink scheduling scheme in a cellular network with a small number of relays. The scheduling scheme has the property that it guarantees stability of the user queues for the largest set of arrival rates. We obtain throughput results by simulation for various scenarios and study the effect of number of relays, relay transmit power relative to the base station (BS) power, and the effect of distributing a given total power between the BS and different numbers of relays. We also present results for the case without channel fading to determine what fraction of the throughput gain is achieved from diversity reception. We find that, with four relays deployed in each sector, it is possible to achieve significant throughput gain including the signaling overhead.

Third generation wireless systems typically employ adaptive coding and modulation, scheduling, and Hybrid Automatic Repeat reQuest (HARQ) techniques to provide high-speed packet data service on the downlink. Two main considerations in designing such a system are algorithms for the selection of coding and modulation schemes based on the channel quality of the link and algorithms for the selection of the user to whom a particular slot is assigned. We propose a systematic approach to optimize the mapping between signal-to-interference-and-noise ratio (SINR) and modulation and coding scheme (MCS) to maximize the throughput by taking into account the type of HARQ scheme employed. We also propose to incorporate frame error rate (FER) and retransmission information as a part of the scheduling decision. The proposed scheduler ranking methods based on using an effective rate rather than the instantaneous rate provide natural priority to retransmissions over new transmissions, and priority to users with better channel quality. Extensive simulation results comparing performance of the proposed methods to conventional methods are presented.

Abstract Coexistence of different access technologies, hierarchical cellular deployment, a wide variety of data services, requirements for transparent operation across different technologies, adaptivity to varying network conditions and mobility and quality of service (QoS) constraints introduce a number of challenges in the design of future generation systems and the specification of new air interfaces, such as efficiency and flexibility in the utilization of spectrum, dynamic resource allocation and exploitation of the multiuser diversity and reconfigurable interference management and inter‐cell coordination. In this paper, three critical issues for the design of next generation systems are addressed: (i) duplexing, (ii) scheduling and resource allocation and (iii) interference and inter‐cell coordination. A number of research directions are presented, which constitute promising potential candidates for next generation systems specification. Copyright © 2005 John Wiley & Sons, Ltd.

We consider different transmission options for packet data which are classified based on the nature of in-cell interference (ICI) and out-of-cell interference (OCI) power statistics. The categories are: (a) no ICI, averaged OCI; (b) no ICI, bursty OCI; and (c) averaged ICI, averaged OCI. Depending on whether the reverse link transmission is time-multiplexed, one user at a time transmission or simultaneous transmission by multiple users, with or without in-cell orthogonality, the interference structure falls into one of the above three categories. We analyze the throughput performance of the system in each of these cases when incremental redundancy is employed to combat uncertainty in the interference power. We compare the different options, under an in-cell rise-over-thermal (IROT) constraint, and provide some insights for reverse link design for next-generation cellular systems. Our results show that time multiplexed transmissions, despite the bursty OCI power structure, has throughput comparable to an orthogonal multiple user transmission system for small cells.

Guard channels have been proposed to minimize handoff call dropping when mobile hosts move from one cell to another. CDMA systems are power- and interference-limited. Therefore, guard capacity in CDMA networks is soft, that is, a given capacity corresponds to variable number of connections. Thus, it is essential to adjust the guard capacity in response to changes in traffic conditions and user mobility. We propose two schemes for managing downlink CDMA radio resources: guard capacity adaptation based on dropping (GAD), and guard capacity adaptation based on prediction and dropping (GAPD). In both schemes, the guard capacity of a cell is dynamically adjusted so as to maintain the handoff dropping rate at a target level. In the second scheme, there is an additional, frequent adjustment component where guard capacity is adjusted based on soft handoff prediction. We show through extensive simulations that GAD and GAPD control the handoff dropping rate effectively under varying traffic conditions and system parameters. We also find that GAPD is more robust than GAD to temporal traffic variations and changes in control parameters.

We present the optimal solution to the problem of allocating bandwidth and power across users for downlink transmission in wireless systems when multiple users can be scheduled for transmission simultaneously. We include maximum and minimum rate per user constraints and a maximum rate per unit bandwidth constraint in the formulation. When only the constraint of a maximum rate per unit bandwidth is imposed we show that scheduling at most two users simultaneously is sufficient for optimality.

It has been observed through simulations of some specific scheduling algorithms that multiuser diversity gains in packet data systems with channel-aware scheduling can be reduced in the presence of any form of link diversity, such as transmit antenna diversity or wideband multipath diversity. We establish that asymptotically, in the limit of large number of transmit antennas and users, the maximum throughput achieved by any optimal scheduling algorithm in the presence of transmit diversity under signal-to-noise-ratio-only feedback can be infinitely worse than that of a system with no diversity. Our results are general and are independent of any particular scheduling algorithm.

We analyze the performance of different handoff algorithms for the forward link of a CDMA cellular system. Unlike the reverse link, soft handoff on the forward link requires additional resources such as CDMA codes and transmit power and also causes additional interference. If handoff requests can be processed instantaneously, transmission from the base station with the best link to the user would achieve a significant fraction of the macro-diversity gain without utilizing additional resources. However, in practical systems there is a non zero execution delay and soft-handoff provides the required robustness to delays although it comes at the expense of additional network resources. There is thus a tradeoff between the extent of soft handoff required and the handoff execution delay. We compare the performance of the hard and soft handoff schemes and study their sensitivity to the execution delay. Outage and the average transmit power required are used as performance metrics. We present an analytical framework to study this tradeoff.

Third generation code-division multiple access (CDMA) systems propose to provide packet data service through a high speed shared channel with intelligent and fast scheduling at the base-stations. In the current approach base-stations schedule independently of other base-stations. We consider scheduling schemes in which scheduling decisions are made jointly for a cluster of cells thereby enhancing performance through interference avoidance and dynamic load balancing. We consider algorithms that assume complete knowledge of the channel quality information from each of the base-stations to the terminals at the centralized scheduler as well as a two-tier scheduling strategy that assumes only the knowledge of the long term channel conditions at the centralized scheduler. We demonstrate that in the case of asymmetric traffic distribution, where load imbalance is most pronounced, significant throughput gains can be obtained while the gains in the symmetric case are modest. Since the load balancing is achieved through centralized scheduling, our scheme can adapt to time-varying traffic patterns dynamically.

Third generation wireless systems typically employ adaptive coding and modulation, scheduling, and hybrid automatic repeat request (HARQ) techniques to provide high speed packet data service on the downlink. One important consideration in designing such a system is algorithm for the selection of coding and modulation schemes based on the channel quality of the link. We propose a systematic approach to optimize the mapping between signal-to-interference-and-noise ratio (SIlSR) and modulation and coding scheme (MCS) to maximize the throughput that takes into account the type of HARQ scheme employed. Extensive simulation results comparing performance of the proposed methods to conventional methods are presented.

The use of multiple antennas at the transmitter and the receiver is considered for the uplink of cellular communication systems. The achievable spectral efficiency in bits/s/Hz is used as the criterion for comparing various design choices. The focus is on wideband code-division multiple-access (CDMA) systems when the receiver uses the matched-filter or the minimum mean-squared error detector, followed by single-user decoders. The spreading sequences of the CDMA system are assumed to be random across the users, but could be dependent across the transmit antennas of each user. Using analytical results in the large system asymptote, guidelines are provided for the sequence design across the transmit antennas and for choosing the number of antennas. In addition, comparisons are made between (random) CDMA and orthogonal multiaccess with multiple antennas. It is shown that CDMA, even with single-user decoding, can outperform orthogonal multiaccess when the number of receive antennas is sufficiently large.

Recently, the capacity region of a multiple-input multiple-output (MIMO) Gaussian broadcast channel, with Gaussian codebooks and known-interference cancellation through dirty paper coding, was shown to equal the union of the capacity regions of a collection of MIMO multiple-access channels. We use this duality result to evaluate the system capacity achievable in a cellular wireless network with multiple antennas at the base station and multiple antennas at each terminal. Some fundamental properties of the rate region are exhibited and algorithms for determining the optimal weighted rate sum and the optimal covariance matrices for achieving a given rate vector on the boundary of the rate region are presented. These algorithms are then used in a simulation study to determine potential capacity enhancements to a cellular system through known-interference cancellation. We study both the circuit data scenario in which each user requires a constant data rate in every frame and the packet data scenario in which users can be assigned a variable rate in each frame so as to maximize the long-term average throughput. In the case of circuit data, the outage probability as a function of the number of active users served at a given rate is determined through simulations. For the packet data case, long-term average throughputs that can be achieved using the proportionally fair scheduling algorithm are determined. We generalize the zero-forcing beamforming technique to the multiple receive antennas case and use this as the baseline for the packet data throughput evaluation.

We study optimal time-allocation strategies for a cellular architecture with relays that can store and forward information from the base station to the terminals on the downlink and from the terminals to the base station on the uplink and determine the consequent capacity gains. We focus on the case when the cellular structure is one-dimensional and evaluate the sensitivity of the capacity gains to key system parameters such as relay power, self-noise, relay location and terminal locations. We show that the optimal time allocation problem on both uplink and downlink with the objective of maximizing the maximum common throughput of all the users in the cell, can be formulated as a linear program. We derive several analytical conditions that any optimal allocation must satisfy yielding simpler linear programs. We also present a sub-optimal allocation algorithm that does not require the solution of a linear program.

The problem of placing training symbols optimally for orthogonal frequency-division multiplexing (OFDM) and single-carrier systems is considered. The channel is assumed to be quasi-static with a finite impulse response of length (L + 1) samples. Under the assumptions that neither the transmitter nor the receiver knows the channel, and that the receiver forms a minimum mean square error (MMSE) channel estimate based on training symbols only, training is optimized by maximizing a tight lower bound on the ergodic training-based independent and identically distributed (i.i.d.) capacity. For OFDM systems, it is shown that the lower bound is maximized by placing the known symbols periodically in frequency. For single-carrier systems, under the assumption that the training symbols are placed in clusters of length /spl alpha/ /spl ges/ (2L + 1), it is shown that the lower bound is maximized by a family of placement schemes called QPP-/spl alpha/, where QPP stands for quasi-periodic placement. These placement schemes are formed by grouping the known symbols into as many clusters as possible and then placing these clusters periodically in the packet. For both OFDM and single-carrier systems, the optimum energy tradeoff between training and data is also obtained.

Providing wireless high-speed packet data services for Web browsing and streaming multimedia applications will be a key feature in future code-division multiple-access (CDMA) systems. We study down-link CDMA schemes for providing such services using multiple antennas at the transmitter and receiver. We propose a generalization of the point-to-point narrowband Bell Labs layered space-time (BLAST) system to a wideband multiple access system which simultaneously supports multiple users through code spreading. We discuss transmission options for achieving transmit diversity and spatial separation and introduce a generalization of the vertical BLAST detector for CDMA signals. Using link level simulations, we determine the bit-error rates versus signal-to-interference ratio of the various transmitter options. We then describe a novel technique for determining the system spectral efficiency (measured in bits per second per Hertz per cell sector) by incorporating the link level results with system level outage simulations. Using four antennas at the transmitter and eight antennas at each receiver, the system can support multiple receivers at 16 times the voice rate, resulting in a system spectral efficiency an order magnitude higher than a conventional single-antenna voice system.

The problem of placing known symbols optimally for OFDM is considered. The channel is assumed to be quasi-static with a finite impulse response. Under the assumption that neither the transmitter and receiver know the channel, we optimize the training by maximizing a lower bound of the mutual information. It is shown that the lower bound is maximized by placing the known symbols periodically. Optimum energy trade-off between the training and the data is also obtained and illustrated through simulation.

A common belief in quantization theory says that the quantization noise process resulting from uniform scalar quantization of a correlated discrete-time process tends to be white in the limit of small distortion ("high resolution"). A rule of thumb for this property to hold is that the source samples have a "smooth" joint distribution. We give a precise statement of this property, and generalize it to nonuniform quantization and to vector quantization. We show that the quantization errors resulting from independent quantizations of dependent real random variables become asymptotically uncorrelated (although not necessarily statistically independent) if the joint Fisher information (FI) under translation of the two variables is finite and the quantization cells shrink uniformly as the distortion tends to zero.

We study a generalization of the successive refinement coding problem called the sequential coding of correlated sources. In successive refinement source coding one first describes the given source using a few bits of information, and then subsequently improves the description of the same source when more information is supplied. Sequential coding differs from successive refinement in that the second-stage encoding involves describing a correlated source as opposed to improving the description of the same source. We introduce the notion of a coupled fidelity criterion to quantify perceived distortion in certain applications of sequential coding. We characterize the achievable rate region for this source coding problem and show that the rate region reduces to the successive refinement rate region when the two sources are the same. Then we consider the specific case of a pair of correlated Gaussian sources as an example. We give an explicit characterization that reveals an interesting generalization of a property of successive refinement of a single Gaussian source.

We investigate the use of multiple antennas at the transmitter and multiple antennas and multi-user decorrelating detectors at the receivers for providing high-speed data on the downlink of cellular CDMA systems. We analyze the system's spectral efficiency by combining link level bit error rate simulations with system level signal-to-interference ratio simulations. We show that using random spreading codes, a spectral efficiency of 3.8 bits/chip/sector can be achieved using 8 transmit and 12 receive antennas, compared to a spectral efficiency of 0.16 bits/chip/sector for a voice system with single transmit and receive antennas.

While feedback does not increase the capacity of memoryless channels, the capacity of time-varying channels with feedback can be increased by exploiting the structure in the channel variations. Feedback information from the receiver is usually available at the transmitter only after some time delay. The capacity increase due to feedback depends on the feedback delay relative to the channel decorrelation time. We model time-varying channels as finite-state Markov channels and determine their capacity as a function of the feedback delay assuming perfect channel state information at the receiver. We apply the result to derive power control strategies to maximize the capacity for finite-state additive Gaussian noise channels and log-normal shadow fading channels.

A firm's CEO employs a team of L agents who observe independently corrupted versions of a data sequence {X(t)}/sub t=1//sup /spl infin//. Let R be the total data rate at which the agents may communicate information about their observations to the CEO. The agents are not allowed to convene. Berger, Zhang and Viswanathan (see ibid., vol.42, no.5, p.887-902, 1996) determined the asymptotic behavior of the minimal error frequency in the limit as L and R tend to infinity for the case in which the source and observations are discrete and memoryless. We consider the same multiterminal source coding problem when {X(t)}/sub t=1//sup /spl infin// is independent and identically distributed (i.i.d.) Gaussian random variable corrupted by independent Gaussian noise. We study, under quadratic distortion, the rate-distortion tradeoff in the limit as L and R tend to infinity. As in the discrete case, there is a significant loss between the cases when the agents are allowed to convene and when they are not. As L/spl rarr//spl infin/, if the agents may pool their data before communicating with the CEO, the distortion decays exponentially with the total rate R; this corresponds to the distortion-rate function for an i.i.d. Gaussian source. However, for the case in which they are not permitted to convene, we establish that the distortion decays asymptotically only as R-l.

We consider a new problem in multiterminal source coding motivated by the following decentralized communication/estimation task. A firm's Chief Executive Officer (CEO) is interested in the data sequence {X(t)}/sub t=1//sup /spl infin// which cannot be observed directly, perhaps because it represents tactical decisions by a competing firm. The CEO deploys a team of L agents who observe independently corrupted versions of {X(t)}/sub t=1//sup /spl infin//. Because {X(t)} is only one among many pressing matters to which the CEO must attend, the combined data rate at which the agents may communicate information about their observations to the CEO is limited to, say, R bits per second. If the agents were permitted to confer and pool their data, then in the limit as L/spl rarr//spl infin/ they usually would be able to smooth out their independent observation noises entirely. Then they could use their R bits per second to provide the CEO with a representation of {X(t)} with fidelity D(R), where D(/spl middot/) is the distortion-rate function of {X(t)}. In particular, with such data pooling D can be made arbitrarily small if R exceeds the entropy rate H of {X(t)}. Suppose, however, that the agents are not permitted to convene, Agent i having to send data based solely on his own noisy observations {Y/sub i/(t)}. We show that then there does not exist a finite value of R for which even infinitely many agents can make D arbitrarily small. Furthermore, in this isolated-agents case we determine the asymptotic behavior of the minimal error frequency in the limit as L and then R tend to infinity.