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The rise of generative artificial intelligence (GenAI) is transforming the telecom industry. GenAI models, particularly large language models (LLMs), have emerged as powerful tools capable of driving innovation, improving efficiency, and delivering superior customer services in telecom. This paper provides an overview of GenAI for telecom from theory to practice. We review GenAI models and discuss their practical applications in telecom. Furthermore, we describe the key technology enablers and best practices for applying GenAI to telecom effectively. We highlight the importance of retrieval augmented generation (RAG) in connecting LLMs to telecom domain specific data sources to enhance the accuracy of the LLMs' responses. We present a real-world use case on RAG-based chatbot that can answer open radio access network (O-RAN) specific questions. The demonstration of the chatbot to the O-RAN Alliance has triggered immense interest in the industry. We have made the O-RAN RAG chatbot publicly accessible on GitHub., Comment: 7 pages, 6 figures, submitted for possible publication

As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab (ARC), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) near-real-time RIC. We discuss software integration, the network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, measuring a cell rate higher than 500 Mbps in downlink and 45 Mbps in uplink., Comment: 15 pages, 15 figures, 3 tables. arXiv admin note: text overlap with arXiv:2310.17062

The transition of fifth generation (5G) cellular systems to softwarized, programmable, and intelligent networks depends on successfully enabling public and private 5G deployments that are (i) fully software-driven and (ii) with a performance at par with that of traditional monolithic systems. This requires hardware acceleration to scale the Physical (PHY) layer performance, end-to-end integration and testing, and careful planning of the Radio Frequency (RF) environment. In this paper, we describe how the X5G testbed at Northeastern University has addressed these challenges through the first 8-node network deployment of the NVIDIA Aerial RAN CoLab (ARC), with the Aerial Software Development Kit (SDK) for the PHY layer, accelerated on Graphics Processing Unit (GPU), and through its integration with higher layers from the OpenAirInterface (OAI) open-source project through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). We discuss software integration, the network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, measuring a cell rate higher than 500 Mbps in downlink and 45 Mbps in uplink., Comment: Cite as: D. Villa, I. Khan, F. Kaltenberger, N. Hedberg, R. Soares da Silva, A. Kelkar, C. Dick, S. Basagni, J. M. Jornet, T. Melodia, M. Polese, and D. Koutsonikolas, "An Open, Programmable, Multi-vendor 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface," Proc. of the 2nd IEEE Workshop on Next-generation Open and Programmable Radio Access Networks (NG-OPERA), Vancouver, BC, Canada, May 2024

As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by solely relying on instantaneous uplink channel estimates. Our proposed method can effectively operate in multi-user scenarios where user feedback imposes excessive delay and bandwidth overhead. To learn the mapping between the user channel matrices and the optimal MCS level of each user, we develop a Convolutional Neural Network (CNN)-Long Short-Term Memory Network (LSTM)-based model and compare the performance with the state-of-the-art methods. Finally, we validate the effectiveness of our algorithm by evaluating it experimentally using real-world datasets collected from the RENEW massive MIMO platform.

The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose \name{}, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks., Comment: IEEE Transactions on Machine Learning in Communications and Networking (TMLCN) 2023

Iterative detection and decoding (IDD) is known to achieve near-capacity performance in multi-antenna wireless systems. We propose deep-unfolded interleaved detection and decoding (DUIDD), a new paradigm that reduces the complexity of IDD while achieving even lower error rates. DUIDD interleaves the inner stages of the data detector and channel decoder, which expedites convergence and reduces complexity. Furthermore, DUIDD applies deep unfolding to automatically optimize algorithmic hyperparameters, soft-information exchange, message damping, and state forwarding. We demonstrate the efficacy of DUIDD using NVIDIA's Sionna link-level simulator in a 5G-near multi-user MIMO-OFDM wireless system with a novel low-complexity soft-input soft-output data detector, an optimized low-density parity-check decoder, and channel vectors from a commercial ray-tracer. Our results show that DUIDD outperforms classical IDD both in terms of block error rate and computational complexity., Comment: This work has been presented at the Asilomar Conference on Signals, Systems, and Computers 2022

Digital twin networks (DTNs) are real-time replicas of physical networks. They are emerging as a powerful technology for design, diagnosis, simulation, what-if-analysis, and artificial intelligence (AI)/machine learning (ML) driven real-time optimization and control of the sixth-generation (6G) wireless networks. Despite the great potential of what digital twins can offer for 6G, realizing the desired capabilities of 6G DTNs requires tackling many design aspects including data, models, and interfaces. In this article, we provide an overview of 6G DTNs by presenting prominent use cases and their service requirements, describing a reference architecture, and discussing fundamental design aspects. We also present a real-world example to illustrate how DTNs can be built upon and operated in a real-time reference development platform - Omniverse., Comment: 7 pages, 5 figures, submitted for publication

We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our detector achieves state-of-the-art performance in terms of symbol error rate (SER) while keeping the computational complexity in check. Indeed, our method can be easily tuned to strike the right balance between computational complexity and performance as required by the application at hand. This balance is achieved by tuning hyperparameters that control the length of the simulated Langevin dynamic. Through numerical experiments, we demonstrate that our detector yields lower SER than competing approaches (including learning-based ones) with a lower running time compared to a previously proposed \emph{overdamped} Langevin-based MIMO detector., Comment: arXiv admin note: substantial text overlap with arXiv:2202.12199, arXiv:2205.05776

Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention. In this paper, we show that the commonly used binary cross-entropy (BCE) loss is a sensible choice in uncoded systems, e.g., for training ML-assisted data detectors, but may not be optimal in coded systems. We propose new loss functions targeted at minimizing the block error rate and SNR deweighting, a novel method that trains communication systems for optimal performance over a range of signal-to-noise ratios. The utility of the proposed loss functions as well as of SNR deweighting is shown through simulations in NVIDIA Sionna., Comment: A shorter version of this paper will be presented at the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

Artificial intelligence (AI) has emerged as a powerful technology that improves system performance and enables new features in 5G and beyond. Standardization, defining functionality and interfaces, is essential for driving the industry alignment required to deliver the mass adoption of AI in 5G-Advanced and 6G. However, fragmented efforts in different standards bodies, such as the third generation partnership project (3GPP) and the open radio access network (O-RAN) Alliance, can lead to confusion and uncertainty about which standards to follow and which aspects of the standards to embrace. This article provides a joint 3GPP and O-RAN perspective on the state of the art in AI adoption in mobile communication systems, including the fundamentals of 5G architecture and its evolution towards openness and intelligence, AI for 5G-Advanced evolution, and a case study on AI-enabled traffic steering. We also identify several areas for future exploration to accelerate AI adoption on the path towards 6G., Comment: 8 pages, 4 figures, submitted for publication

We consider an integrated access and backhaul (IAB) node operating in full-duplex (FD) mode. We analyze simultaneous transmission from the New Radio gNB to the IAB node on the backhaul uplink, IAB node to a user equipment (UE) on the access downlink, and IAB transmitter to the IAB receiver on the self-interference (SI) channel. Our contributions include (1) a low complexity algorithm to jointly design the hybrid analog/digital beamformers for all three nodes to maximize the sum spectral efficiency of the access and backhaul links by canceling SI and maximizing received power; (2) derivation of all-digital beamforming and spectral efficiency upper bound for use in benchmarking; and (3) simulations to compare full vs. half duplex modes, hybrid vs. all-digital beamforming algorithms, proposed hybrid vs. conventional beamforming algorithms, and spectral efficiency upper bound. In simulations, the proposed algorithm shows significant reduction in SI power and increase in sum spectral efficiency.

Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term -- the score of the likelihood -- by a neural network. Through numerical experiments in both synthetic and real-world data, we show that our proposed detector yields state-of-the-art symbol error rate performance and the robust version becomes noise-variance agnostic., Comment: arXiv admin note: substantial text overlap with arXiv:2202.12199

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radio frequency (RF) signals. Future standards and next-generation (nextG) networks beyond 5G will have two significant evolutions over the state-of-the-art 5G implementations: (i) massive number of antenna elements, scaling up to hundreds-to-thousands in number, and (ii) inclusion of AI/ML in the critical path of the network reconfiguration process that can access sensor feeds from a variety of RF and non-RF sources. While the former allows unprecedented flexibility in 'beamforming', where signals combine constructively at a target receiver, the latter enables the network with enhanced situation awareness not captured by a single and isolated data modality. This survey presents a thorough analysis of the different approaches used for beamforming today, focusing on mmWave bands, and then proceeds to make a compelling case for considering non-RF sensor data from multiple modalities, such as LiDAR, Radar, GPS for increasing beamforming directional accuracy and reducing processing time. This so called idea of multimodal beamforming will require deep learning based fusion techniques, which will serve to augment the current RF-only and classical signal processing methods that do not scale well for massive antenna arrays. The survey describes relevant deep learning architectures for multimodal beamforming, identifies computational challenges and the role of edge computing in this process, dataset generation tools, and finally, lists open challenges that the community should tackle to realize this transformative vision of the future of beamforming.

Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Through numerical experiments, we show that our proposed detector yields state-of-the-art symbol error rate performance.

Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Recently, there has been a growing interest to get reasonably close to the optimal solution using neural networks while keeping the computational complexity in check. However, existing work based on deep learning shows that it is difficult to design a generic network that works well for a variety of channels. In this work, we propose a method that tries to strike a balance between symbol error rate (SER) performance and generality of channels. Our method is based on hypernetworks that generate the parameters of a neural network-based detector that works well on a specific channel. We propose a general framework by regularizing the training of the hypernetwork with some pre-trained instances of the channel-specific method. Through numerical experiments, we show that our proposed method yields high performance for a set of prespecified channel realizations while generalizing well to all channels drawn from a specific distribution.

Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep learning can solve complex problems, digital signal processing (DSP) is interpretable and can be more computationally efficient. To combine both, we propose the dual path network (DPN). It consists of a signal path of DSP operations that recover the signal, and a feature path of neural networks that estimate the unknown transmit parameters. By interconnecting the paths over several recovery stages, later stages benefit from the recovered signals and reuse all the previously extracted features. The proposed design is demonstrated to provide 5% improvement in modulation classification compared to alternative designs lacking either feature sharing or access to recovered signals. The estimation results of DPN along with its blind decoding performance are shown to outperform a blind signal processing algorithm for BPSK and QPSK on a simulated dataset. An over-the-air software-defined-radio capture was used to verify DPN results at high SNRs. DPN design can process variable length inputs and is shown to outperform relying on fixed length inputs with prediction averaging on longer signals by up to 15% in modulation classification.

Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding. Many of the existing end-to-end learning approaches demonstrated robustness to signal distortions like frequency and timing errors, and outperformed classical signal processing techniques with sufficient training. However, deep learning approaches typically require hundreds of thousands of floating points operations for inference, which is orders of magnitude higher than classical signal processing approaches and thus do not scale well for long sequences. Additionally, they typically operate as a black box and without insight on how their final output was obtained, they can't be integrated with existing approaches. In this paper, we propose a novel neural network architecture that combines deep learning with linear signal processing typically done at the receiver to realize joint modulation classification and symbol recovery. The proposed method estimates signal parameters by learning and corrects signal distortions like carrier frequency offset and multipath fading by linear processing. Using this hybrid approach, we leverage the power of deep learning while retaining the efficiency of conventional receiver processing techniques for long sequences. The proposed hybrid approach provides good accuracy in signal distortion estimation leading to promising results in terms of symbol error rate. For modulation classification accuracy, it outperforms many state of the art deep learning networks.

We propose a method of training quantization thresholds (TQT) for uniform symmetric quantizers using standard backpropagation and gradient descent. Contrary to prior work, we show that a careful analysis of the straight-through estimator for threshold gradients allows for a natural range-precision trade-off leading to better optima. Our quantizers are constrained to use power-of-2 scale-factors and per-tensor scaling of weights and activations to make it amenable for hardware implementations. We present analytical support for the general robustness of our methods and empirically validate them on various CNNs for ImageNet classification. We are able to achieve near-floating-point accuracy on traditionally difficult networks such as MobileNets with less than 5 epochs of quantized (8-bit) retraining. Finally, we present Graffitist, a framework that enables automatic quantization of TensorFlow graphs for TQT (available at https://github.com/Xilinx/graffitist )., Comment: Link to Conference (Oral & Poster) Schedule - https://mlsys.org/Conferences/2020/ScheduleMultitrack?event=1431

Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8 user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than 2.6x in terms of throughput per FPGA look-up tables., Comment: IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS I), Vol. 63, No. 12, Dec. 2016

Fifth generation (5G) wireless systems are expected to combine emerging transmission technologies, such as large-scale multiple-input multiple-output (MIMO) and non-orthogonal multi-carrier waveforms, to improve the spectral efficiency and to reduce out-of-band (OOB) emissions. This paper investigates the efficacy of two promising multi-carrier waveforms that reduce OOB emissions in combination with large-scale MIMO, namely filter bank multi-carrier (FBMC) and generalized frequency division multiplexing (GFDM). We develop novel, low-complexity data detection algorithms for both of these waveforms. We investigate the associated performance/complexity trade-offs in the context of large-scale MIMO, and we study the peak-to-average power ratio (PAPR). Our results show that reducing the OOB emissions with FBMC and GFDM leads to higher computational complexity and PAPR compared to that of orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency division multiple access (SC-FDMA)., Comment: Presented at the Asilomar Conference on Signals, Systems, and Computers

In this paper, full-duplex radios are used to continuously update the channel state information at the transmitter, which is required to compute the downlink precoding matrix in MIMO broadcast channels. The full-duplex operation allows leveraging channel reciprocity for open-loop uplink training to estimate the downlink channels. However, the uplink transmission of training creates interference at the downlink receiving mobile nodes. We characterize the optimal training resource allocation and its associated spectral efficiency, in the proposed open-loop training based full-duplex system. We also evaluate the performance of the half-duplex counterpart to derive the relative gains of full-duplex training. Despite the existence of inter-node interference due to full-duplex, significant spectral efficiency improvement is attained over half-duplex operation., Comment: The material in this paper was presented (without proof) in 16th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2015

Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems, based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose - to the best of our knowledge - the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection. We analyze the associated error, and we compare its performance and complexity to those of an exact linear detector. We present corresponding VLSI architectures, which perform exact and approximate soft-output detection for large-scale MIMO systems with various antenna/user configurations. Reference implementation results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale MIMO system. We finally provide a performance/complexity trade-off comparison using the presented FPGA designs, which reveals that the detector circuit of choice is determined by the ratio between BS antennas and users, as well as the desired error-rate performance., Comment: To appear in the IEEE Journal of Selected Topics in Signal Processing

In a multi-input multi-output (MIMO) full-duplex network, where an in-band full-duplex infrastruc- ture node communicates with two half-duplex mobiles supporting simultaneous up- and downlink flows, the inter-mobile interference between the up- and downlink mobiles limits the system performance. We study the impact of leveraging an out-of-band side-channel between mobiles in such network under different channel models. For time-invariant channels, we aim to characterize the generalized degrees- of-freedom (GDoF) of the side-channel assisted MIMO full-duplex network. For slow-fading channels, we focus on the diversity-multiplexing tradeoff (DMT) of the system with various assumptions as to the availability of channel state information at the transmitter (CSIT). The key to the optimal performance is a vector bin-and-cancel strategy leveraging Han-Kobayashi message splitting, which is shown to achieve the system capacity region to within a constant bit. We quantify how the side-channel improve the GDoF and DMT compared to a system without the extra orthogonal spectrum. The insights gained from our analysis reveal: i) the tradeoff between spatial resources from multiple antennas at different nodes and spectral resources of the side-channel, and ii) the interplay between the channel uncertainty at the transmitter and use of the side-channel., Comment: 60 pages, Submitted to IEEE Transactions on Information Theory (under revision), Jan 2014

Recent experimental results have shown that full-duplex communication is possible for short-range communications. However, extending full-duplex to long-range communication remains a challenge, primarily due to residual self-interference even with a combination of passive suppression and active cancellation methods. In this paper, we investigate the root cause of performance bottlenecks in current full-duplex systems. We first classify all known full-duplex architectures based on how they compute their cancelling signal and where the cancelling signal is injected to cancel self-interference. Based on the classification, we analytically explain several published experimental results. The key bottleneck in current systems turns out to be the phase noise in the local oscillators in the transmit and receive chain of the full-duplex node. As a key by-product of our analysis, we propose signal models for wideband and MIMO full-duplex systems, capturing all the salient design parameters, and thus allowing future analytical development of advanced coding and signal design for full-duplex systems., Comment: 35 pages, Submitted to IEEE Transactions on Vehicular Technology, Dec 2012

This paper discusses transmission strategies for dealing with the problem of self-interference in multi-hop wireless networks in which the nodes communicate in a full- duplex mode. An information theoretic study of the simplest such multi-hop network: the two-hop source-relay-destination network, leads to a novel transmission strategy called structured self-interference cancellation (or just "structured cancellation" for short). In the structured cancellation strategy the source restrains from transmitting on certain signal levels, and the relay structures its transmit signal such that it can learn the residual self-interference channel, and undo the self-interference, by observing the portion of its own transmit signal that appears at the signal levels left empty by the source. It is shown that in certain nontrivial regimes, the structured cancellation strategy outperforms not only half-duplex but also full-duplex schemes in which time-orthogonal training is used for estimating the residual self-interference channel., Comment: Draft of the paper to be presented at the Allerton Conference on Sept 29, 2011; Proc. 49th Annual Allerton Conference on Communication, Control, and Computing, September 2011

We present an experiment-based characterization of passive suppression and active self-interference cancellation mechanisms in full-duplex wireless communication systems. In particular, we consider passive suppression due to antenna separation at the same node, and active cancellation in analog and/or digital domain. First, we show that the average amount of cancellation increases for active cancellation techniques as the received self-interference power increases. Our characterization of the average cancellation as a function of the self-interference power allows us to show that for a constant signal-to-interference ratio at the receiver antenna (before any active cancellation is applied), the rate of a full-duplex link increases as the self-interference power increases. Second, we show that applying digital cancellation after analog cancellation can sometimes increase the self-interference, and thus digital cancellation is more effective when applied selectively based on measured suppression values. Third, we complete our study of the impact of self-interference cancellation mechanisms by characterizing the probability distribution of the self-interference channel before and after cancellation., Comment: Revised the submission to IEEE Transactions on Wireless Communications, May 2012. Submitted to IEEE Transactions on Wireless Communications, July 2011

We present an analytical, simulation, and experimental-based study of beamforming Multiple Input Single Output (MISO) systems. We analyze the performance of beamforming MISO systems taking into account implementation complexity and effects of imperfect channel estimate, delayed feedback, real Radio Frequency (RF) hardware, and imperfect timing synchronization. Our results show that efficient implementation of codebook-based beamforming MISO systems with good performance is feasible in the presence of channel and implementation-induced imperfections. As part of our study we develop a framework for Average Error Vector Magnitude Squared (AEVMS)-based analysis of beamforming MISO systems which facilitates comparison of analytical, simulation, and experimental results on the same scale. In addition, AEVMS allows fair comparison of experimental results obtained from different wireless testbeds. We derive novel expressions for the AEVMS of beamforming MISO systems and show how the AEVMS relates to important system characteristics like the diversity gain, coding gain, and error floor., Comment: Submitted to IEEE Transactions on Wireless Communications, November 2009