We consider the performance and improvement of Multi-User MIMO (MU-MIMO) in 3GPP LTE. In particular, we build on academic guidance, demonstrating the link between channel state information (CSI) accuracy and MU-MIMO performance, by considering MU-MIMO with practical CSI estimation and feedback within an actual system design under practical channels. Link-Level simulations are provided to model the interplay of such factors in the LTE design. We also propose a practical codebook structure to support increased CSI feedback. The results show that for some scenarios, in particular those with sufficient channel coherence and low antenna correlation, that MU-MIMO can be significantly improved by increased CSI feedback. Such conditions may be indicative of “small-cell” scenarios, an important scenario for future network capacity enhancement. The results also point to other issues that merit further consideration including CSI feedback granularity in frequency, CSI estimation, and improved MU-MIMO processing.

Time-Division Duplexing (TDD) allows to estimate the downlink channels for an arbitrarily large number of base station antennas from a finite number of orthogonal uplink pilot signals, by exploiting channel reciprocity. Based on this observation, a recently proposed "Massive MIMO" scheme was shown to achieve unprecedented spectral efficiency in realistic conditions of distance-dependent pathloss and channel coherence time and bandwidth. The main focus and contribution of this paper is an improved Network-MIMO TDD architecture achieving spectral efficiencies comparable with "Massive MIMO", with one order of magnitude fewer antennas per active user per cell (roughly, from 500 to 50 antennas). The proposed architecture is based on a family of Network-MIMO schemes defined by small clusters of cooperating base stations, zero-forcing multiuser MIMO precoding with suitable inter-cluster interference mitigation constraints, uplink pilot signals allocation and frequency reuse across cells. The key idea consists of partitioning the users into equivalence classes, optimizing the Network-MIMO scheme for each equivalence class, and letting a scheduler allocate the channel time-frequency dimensions to the different classes in order to maximize a suitable network utility function that captures a desired notion of fairness. This results in a mixed-mode Network-MIMO architecture, where different schemes, each of which is optimized for the served user equivalence class, are multiplexed in time-frequency. In order to carry out the performance analysis and the optimization of the proposed architecture in a systematic and computationally efficient way, we consider the large-system regime where the number of users, the number of antennas, and the channel coherence block length go to infinity with fixed ratios.

Fundamentally, benefits of Multiple Input Multiple Output (MIMO) in wireless systems rest on increasing the number of Degrees of Freedom (DoFs) that can be exploited for transmission. DoFs can be used to increase spectral efficiency and/or provide increased diversity. However, exploiting DoFs in MIMO often requires knowledge of Channel State Information (CSI) at the transmitter and/or receiver. Obtaining CSI creates overheads due to the required use of wireless resources for CSI-estimation pilots and/or CSI feedback. This can create a fundamental "dimensionality bottleneck" that limits the growth in spectral efficiency for some conventional MIMO systems. Recently a new class of techniques known as "Blind Interference Alignment" (BIA) has demonstrated the possibility to grow DoFs without many of the CSI overheads of conventional systems. BIA therefore has the potential to relieve the "dimensionality bottleneck". However, BIA can have limited applicability to many users in a cellular environment given that it requires a high Signal to Noise Ratio (SNR) to operate efficiently. By examining BIA, we present a method to reduce the SNR requirements and improve the application of BIA through power allocation and cluster-based transmission. We show that BIA can be applied successfully in cellular without the need for joint processing between base-stations, and can improve performance over conventional MIMO for even cell-edge users in some scenarios.

Multi-user MIMO (MU-MIMO) is seen as one of the key technologies for 4G communications systems. A critical factor affecting the throughput gains provided by MU-MIMO is the feedback mechanism that allows the transmitting basestation to periodically receive channel state information (CSI) from mobiles. Sufficient feedback is necessary for efficient MU-MIMO operation. At the same time feedback itself is an overhead using resources that could otherwise be used for data transmission. It is therefore important to carefully consider how feedback is designed and used to support MU-MIMO. This work studies key design parameters in such a feedback process: how often in time and in frequency should mobiles perform feedback (given coherence in the channel), the number of bits of feedback (accuracy of CSI quantization), and the number of mobiles to provide feedback. Within a bound on feedback resources these parameters can determine how best to use such resources to optimize MU-MIMO spectral efficiency. The results show that spectral efficiency is often maximized if mobiles quantize their channels very accurately but perform feedback at moderate intervals in time, such that CSI inaccuracy is primarily determined by the variation of the channel in time and frequency rather than being dominated by quantization errors.

We consider the performance of Hybrid ARQ (HARQ) in LTE in the presence of inter-cell interference (ICI) due to MIMO transmissions. Such ICI is a function of the MIMO transmission parameters used in neighboring cells, including the beamforming vectors used and the powers applied to these vectors. Since these are often adapted for different transmissions and generally not known to neighboring cells, the prevailing ICI and channel quality experienced by users in such scenarios varies and may often be unknown to the transmitter. Systems that can operate efficiently in the presence of variable/unknown ICI levels are therefore of particular interest for such scenarios. We show that MIMO-ICI can be efficiently handled by use of HARQ. However, the 4-Redundancy Version HARQ design of LTE can have a significant loss in throughput with respect to what can be achieved. Handling MIMO-ICI requires a more aggressive use of Incremental Redundancy (IR) that does not target successful decoding in the first few (e.g. 1 to 4) transmissions (TXs). Rather, the system is less reliant on fine-grained control of modulation/code-rate settings, and net TX rates are adapted implicitly with prevailing ICI conditions via the number of TXs (often exceeding 4) required for successful decoding. This points to a fundamentally different, simplified, and higher-throughput system design that is well suited to delay tolerant transmission.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes of the next-generation of cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users according to their channels vector directions and SINR levels. However, when scheduling is applied independently in each cell, the inter-cell interference (ICI) power at each user receiver is not known in advance since it changes at each new scheduling slot depending on the scheduling decisions of all interfering base stations. In order to cope with this uncertainty, we consider the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We develop a game-theoretic framework for this problem and build on stochastic optimization techniques in order to find optimal scheduling and ARQ schemes. Particularizing our framework to the case of "outage service rates", we obtain a scheme based on adaptive variable-rate coding at the physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then, we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ) that is able to achieve a throughput performance arbitrarily close to the "genie-aided service rates", with no need for a genie that provides non-causally the ICI power levels. The novel HARQ scheme is both easier to implement and superior in performance with respect to the conventional combination of adaptive variable-rate coding and ARQ-LLC.

In next-generation cellular systems, Multi-User MIMO (MU-MIMO) schemes are expected to achieve very high downlink throughput, provided that issues such as Inter-Cell Interference (ICI), user scheduling, etc. are properly handled. In order to accomplish this goal, we consider a joint scheduling and cell clustering scheme where groups of adjacent cells (clusters) are jointly controlled and effectively act as a distributed multi-antenna transmitter. An ``overlapped'' clustering scheme is used whereby different clustering patterns operate on different frequency subchannels. The arrangement is such that each user in the system is located away from the cluster-edge and sees a favorable ICI level on one or more subchannels. This leads to significant per-user and per-cell performance improvements over single-arrangement clustering schemes that force a user to use only one cluster arrangement. Critical to realizing such benefits in an overlapped cluster arrangement is the joint operation of downlink opportunistic scheduling across all cluster patterns, i.e., across the frequency bands. Indeed, joint scheduling enables users to optimally share and balance the use multiple subchannels (i.e, multiple clustering patterns). Such a balance does not trivially schedule users close to cluster centers. We demonstrate, under the popular Proportionally Fair Scheduling (PFS) criterion, that the joint system operation of scheduling and overlapped arrangements yields significant improvements to ``cell-edge'' user rates while also increasing overall per-cell throughput.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes foreseen in the next-generation cellular systems (e.g., LTE-Advanced). Scheduling exploits the knowledge of the users' instantaneous achievable coding rates. However, when it is applied independently in each cell, the level of inter-cell interference (ICI) experienced by each user is not known a priori at the cell scheduler as it depends on the local scheduling decisions at all other base stations. As a result, the instantaneous achievable rates are random variables, even under the optimistic assumption of perfect transmitter channel state information. In order to cope with this problem, we consider the joint design and operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We present a systematic method for joint scheduler/ARQ design and show that properly designed incremental-redundancy hybrid ARQ schemes can achieve (with finite average decoding delay) any desired fraction of the "optimistic" throughputs, i.e., of the throughputs that would be achieved if the transmitter knew the instantaneous user ICI and transmitted to each scheduled user at the maximum possible instantaneous rate.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes of the next-generation of cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users according to their channels vector directions and channel quality indicator (CQI). However, when scheduling is applied independently in each cell, the inter-cell interference (ICI) power at each user receiver is random and not known in advance since it changes from slot to slot depending on the scheduling decisions of all interfering base stations. In order to cope with this uncertainty, we consider the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We develop a game-theoretic framework for this multi-cell decentralized scheduling problem for which we prove existence of Nash equilibria, and we build on stochastic optimization techniques in order to devise good joint scheduling and ARQ schemes. We also find “extremal” ICI power statistics that provide uniform lower and upper bounds to the values of the network utility function at all Nash equilibria of the decentralized scheduling game.

Cooperative base-station (BS) signaling using MU-MIMO (Network MIMO) has received a great deal of attention given its ability to reduce inter-cell interference (ICI) and improve the system spectral efficiency. Indeed for a given number of antennas per BS, cooperative systems can have significant benefits over conventional cellular architectures. However cooperative signaling requires and uses more channel state information (CSI). This increases CSI signaling overhead, and can have a non-negligible effect on the system throughput. In fact once systems are compared while taking into account CSI overhead, the question of what system architecture is best becomes interesting and non-trivial. We provide one such alternative look at cooperative architectures. We show, despite prevailing views, that cellular architectures, using coordinated colocated antennas, can be quite attractive compared to Network MIMO.

In this paper we present reduced-complexity high-performance iterative receivers for single-user MIMO systems that employ OFDM with bit-interleaved coded modulation. The proposed inner-outer decoder structures exploit the Soft-Output M algorithm (SOMA) to calculate bit-likelihood estimates via a limited search on a tree. A key element of the proposed designs is the way the SOMA front-end adapts at each iteration to new extrinsic information. This front-end defines the tree, both in terms of the order in which symbols are considered, as well as in terms of the subspaces used in the likelihood calculations at each depth in the tree. The proposed SOMA front-ends are different from the conventional QR-decomposition front-ends and provide a systematic way to adapt both the order and the subspaces at each iteration in an iterative decoding structure. Simulations for 4 × 4 16 QAM systems show that the proposed iterative receivers can yield significant performance gains over receivers using non-adaptive conventional front-ends.

We consider random beamforming and user scheduling strategies in a multi-cell environment for downlink wireless communications. Our focus is on using combined scheduling and beamforming to mitigate performance losses due to inter-cell interference. Of interest are strategies requiring minimal intra-cell and inter-cell system information exchange. Indeed the acquisition and inter-cell exchange of such information can be significant overheads in a practical deployment. Random beamforming requires substantially less channel state information than linear zero forcing strategies to schedule transmissions. It is therefore of practical interest despite some limitations in controlling intra-cell interference. We consider how one can use a minimal exchange of information, in a multi-cell multistep pattern of exchanges, to improve the overall performance of random beamforming in a multi-cell environment.

The paper presents an approach to the design of layered BICM MIMO systems using multi-stage iterative decoding and Soft Output M-Algorithm (SOMA) based component decoders. The systems consider transmission based on multilevel coding linked to hierarchical QAM constellations. The receivers are iterative designs with a view to supporting high-rates. These receiver designs can also be used to obtain reduced complexity. The performance of the SOMA schemes are compared to achievable rate bounds for multi-level D-BLAST based schemes. The D-BLAST receivers illustrate general limitations of non-iterative approaches. The multi-layer transmission approach provides a natural support for unequal error protection (UEP), and can be used to balance tradeoffs between robust coverage vs quality in media broadcast applications. Rough predictions on such tradeoffs are given using layered MPEG2 video as a media example.

Due to the low cost and ease of deployment, singlehop and multi-hop 802.11 networks have become attractive solutions for providing last-mile broadband (wireless) access in urban environments. However, a critical issue in using such networks to support applications such as Voice over IP is the widely known overheads in the Medium Access Control (MAC) layer and Physical (PHY) layer for each transmission. The effect of these overheads can be more severe in multi-hop deployments, and can limit the number of VoIP calls per Gateway Access Point (GAP) in a multi-hop system to be no greater than that of a single-hop single-GAP system. In this paper, we build upon our previous work that showed that a multi-hop network, with one GAP using a single channel, can in fact support more users than a single-hop network. To do so functions such as aggregation have to be intelligently used at relays in the network. We build on this prior work giving a theoretical framework that considers the joint tradeoff of aggregation, bursting, and PHY rate adaptation given a set of admission and routing decisions. We compare call-capacity estimates from the theory to those derived from simulations, and illustrate through examples the advantages of considering routing/ admission decisions jointly with bursting and aggregation.

We present a method for network coding over time-varying networks with attractive tradeoffs between throughput and decoding-delay. We model the time-varying networks as a sequence of topology graphs. The method relies on designing a deterministic network code for a fixed virtual graph derived from the actual sequence of topologies, and on exploiting proper buffering in order to implement the code over the actual network. We show that, under certain conditions, this approach can achieve the maximum achievable throughput over the given time- varying network. We also present simulation results that validate the theoretical findings.

We present a study of voice over IP capacity in 802.11 wireless local area networks (WLANs) using actual conversational speech recordings. Our intention is to give a more realistic view on how voice capacity varies in a WLAN environment. To this goal we conducted several parallel studies comparing results based on real speech to those based on the ITU-T Rec. P.59 artificial conversational model. The studies include the use (or not) of silence suppression, distributed and centralized access mechanisms, and a variety of speech conditions. The results show, importantly, that indeed capacity based on real speech differs from that obtained using the artificial model. The reasons for this difference goes beyond observed differences in activity levels. It depends on secondary statistical characteristics of traffic and on the (random) sampling of conversations. With the study we are also able to compare the relative benefits of different approaches to increase capacity.

In this paper, we are interested in the design and performance of unequal error protection (UEP) rateless codes. These codes have interesting properties in the deployment of mobile peer to peer (MP2P) and mobile broadcast systems. As our main contribution, we propose a new UEP rateless code structure that is composed of a bank of precoders and a common rateless code. Through the bank of precoders, information layers are first expanded into encoded layers with a diminishing coding rate as the priority of the layer increases. The output of precoders are then concatenated and fed into a rateless encoder. The output of the rateless encoder naturally constitutes UEP encoding blocks carrying disproportionately more information on the higher priority layers. The decoder at the receiver end performs the inverse operations to recover each layer separately. We evaluate our proposed scheme comparing against the alternative solutions. Results show that over a broad class of rateless code structures the proposed scheme can substantially reduce the time required to recover higher priority layers while minimally impacting the time required to recover the lower priority layers.

The majority of speech and audio encoders today operate at rates of less than 1-2 bit/input-sample. Reducing the rates even further is a challenge in particular for coders using waveform-based coding paradigms. Specifically for transform-based coders it is often the case that transform coefficients are quantized at less than 1 bit/coefficient (on average) in many areas of the spectrum. Dealing with such cases is particularly challenging when some coefficients are assumed to be independent identically distributed random variables with little underlying predictable structure. This prompts a study on how to improve encoding in such situations beyond simply increasing the dimension of quantizers, an option that may not be practical or perceptually relevant in some coder designs. This paper looks at a general class of schemes motivated by observations on statistical variations at low dimensions. These schemes, termed partial ordering schemes, show advantages objectively (in mean square error) and perceptually by targeting randomly positioned peaks in the spectrum. At low rates, such schemes create sparse bit allocations that allow for further optimization using random noise fill. A listening test is presented demonstrating the advantages of such an approach

In this paper we focus on new options for the efficient quantization of statistically normalized target vectors at low bitrates. This problem is fundamental to many low-rate speech and audio coder designs. Here many such coders follow a general principle of taking a structured speech or audio signal, applying a process of redundancy removal and then quantizing each of the resulting statistically normalized targets to a relevant distortion level. We look at this latter problem when some of these targets are to be quantized at very low bitrates ( 1 bit/target-scalar). The approach we take is to efficiently communicate a target-adaptive pattern of unequal bit-assignments (noise allocations) across each target. This can increase performance over an approach that has a constant noise allocation even when target vectors consist of independent and identically distributed (i.i.d.) scalars. We extend these schemes to multi-option schemes allowing further options to adapt and improve performance

Wireless Local Area Networks (WLANs) based on IEEE 802.11 standards are becoming widespread and are now being considered as potential solutions for providing "last-mile" wireless IP-based services. One of the challenges to this end however comes from the grave inefficiencies in the CSMA-based 802.11 medium access control (MAC) when used with application payloads that are relatively small. This is the case seen in the important application of Voice over IP (VoIP). The inefficiencies becomes even more severe in multi-hop wireless mesh systems that use 802.11 radio interfaces. In this paper we present novel strategies that are in conformance with the 802.11 mechanism while at the same time providing significant performance improvements for such applications. Our main approach will be one of jointly combining routing and traffic shaping decisions together to improve the efficiency of DCF functions. Specifically we try to change the traffic characteristics at different hops in the network by combining multiple flows to produce new traffic and flow statistics. Correct routing enables one to do such changes and, therefore, by performing routing and traffic shaping decisions jointly we can improve the efficiency of 802.11 mesh networks. To demonstrate the effectiveness of the proposed scheme, we focus on the two-hop mesh architectures and consider the VoIP traffic as the application of interest. We specifically investigate the achievable throughput region with and without the traffic shaping mechanisms. Our experiments show that more than a 3-fold call-capacity improvements can be gained over the conventional transport methods.

Presented is a new coding paradigm, multimode transform predictive coding (MTPC), which combines speech and audio coding principles in a single coding structure. The paradigm is an adaptive coding paradigm which automatically adjusts how different coding modules are used based on the input signal. This allows MTPC coders to robustly handle a wider range of signals than single configuration (mode) transform predictive coding (TPC) designs. A wideband MTPC coder design targeting two-way communication applications and bitrates from 13 to 40 kbit/s is also presented. Subjective absolute category rating test results on speech, speech in noise and music demonstrate that the performance at 16, 24 and 32 kbit/s meets or exceeds that of ITU-T Rec. G.722 at 48, 56 and 64 kbit/s respectively for many coding conditions. Subjective Reference-ABx (R-ABx) tests are also included to show the potential advantages of the multimode coder over a single mode TPC coder. Finally, possible improvements in the MTPC coder design for applications such as broadcasting, which are less sensitive to delay and encoder complexity, are discussed.

In this paper we investigate the application of Unequal Error Protection (UEP) channel coding schemes to the Transform Predictive Coding (TPC) audio coding paradigm. The TPC paradigm has natural perceptual ordering of the bitstream that provides a good match with the UEP concept. Also introduced is the general idea of Mode Adaptive UEP schemes that adapt to the differing bit-error sensitivity profiles of signal adaptive multimode source-coders. By doing this the decoded signal quality is optimized jointly over the input signal, the channel conditions and the bit-error sensitivity profile of the source coder.

The move to digital is a natural progression taking place in all aspects of broadcast media applications from document processing in newspapers to video processing in television distribution. This is no less true for audio broadcasting which has taken a unique development path in the United States. This path has been heavily influenced by a combination of regulatory and migratory requirements specific to the U.S. market. In addition, competition between proposed terrestrial and satellite systems combined with increasing consumer expectations have set ambitious, and often changing, requirements for the systems. The result has been a unique set of evolving requirements on source coding, channel coding, and modulation technologies to make these systems a reality. This paper outlines the technical development of the terrestrial wireless and satellite audio broadcasting systems in the U.S., providing details on specific source and channel coding designs and adding perspective on why specific designs were selected in the final systems. These systems are also compared to other systems such as Eureka-147, DRM, and Worldspace, developed under different requirements.

Presented is an embedded coding strategy for wideband signals using the multimode transform predictive coding (MTPC) framework. The design uses many of the inherently layered features of the original non-embedded MTPC including iterative bit assignments and frequency domain coding. The design also uses the mode decision of the MTPC to adapt the layering strategy to the type input signal. The result is a high quality embedded wideband coder with multiple enhancement layers and little compromise in performance when compared to non-embedded MTPC designs.

A two stage hybrid embedded speech/audio coding structure is proposed. The structure uses a speech coder as a core to provide the minimal bitrate and an acceptable performance on speech inputs. The second stage is a transform coder using a modified discrete cosine transform (MDCT) and perceptual coding principles. This stage is itself embedded both in complexity and bitrate, and provides various levels of enhancement of the core output, particularly for general audio signals like music. Informal A-B comparison tests show that the performance of the structure at 16 kb/s is between that of the GSM enhanced full rate coder at 12.2 kb/s, and the G.728 LD-CELP coder at 16 kb/s.

Locally most powerful tests and invariant tests are combined in a new detector to test for the presence of a signal in additive white Gaussian noise. Such tests, locally most powerful invariant (LMPI) tests, are defined using a number of orthogonal linear subspaces, and the second-order signal statistics in each subspace.

The use of cone classes for signal detection and estimation is relatively new. Cone classes and the cone detector have shown potential as a viable alternative to subspace detection. The addition of new quadratic forms that model a wider range of signal characteristics, and the extension to using intersections of single cones, now makes it possible to apply cone classes to a wider range of practical problems. These extensions have resulted in a number of different types of intersection classes. Each type requires a different algorithm to implement the generalized likelihood ratio test (GLRT) used for signal detection. The authors classify these types and outline the possible solutions to implement the GLRT. New examples of signal classes modeled using cones are also given.