A communication setup is considered where a single transmitter wishes to convey messages to one or two receivers and simultaneously estimate the states of the receivers through the backscattered signals of the emitted waveform. The scenario at hand is motivated by joint radar and communication, which aims to co-design radar sensing and communication over shared spectrum and hardware. In this paper, we model the communication channel as a simple memoryless channel with independent and identically distributed (i.i.d.) time-varying state sequences and we model the backscattered signals by (strictly causal) generalized feedback. For single-receiver systems of this form, we fully characterize the capacity-distortion tradeoff, defined as the largest rate at which a message can reliably be conveyed to the receiver while simultaneously allowing the transmitter to sense the state sequence with a given allowed distortion. Our results show a tradeoff between the achievable rates and distortions, and that this tradeoff only stems from a common choice of the input distribution (the waveform) but not from other properties of the utilized codes. To better illustrate the capacity-distortion tradeoff, we propose a numerical method to compute the optimal inputs (waveforms) that achieve the desired tradeoff. For two-receiver systems with two states, we characterize the capacity-distortion tradeoff region of physically degraded broadcast channels (BC) as a rather straightforward extension of the single receiver case. Here, a tradeoff not only arises between sensing and communication performances but also between the various rates and the distortions of the different states. Similarly to the single-receiver case, the optimal co-design scheme exploits the generalized feedback signals only for sensing but not for improving communication performance. This is different for general two-receiver BCs, where optimal co-design schemes exploit generalized feedback also to improve capacity. However, as we show, also for BCs the optimal sensing performance only depends on the chosen input distribution (waveform) but not on the code construction used to accomplish the communication task. For general BCs, we provide inner and outer bounds on the capacity-distortion region, as well as a sufficient condition when this capacity-distortion region is equal to the product of the capacity region and the set of achievable distortions, in which case no tradeoff between sensing and communication occurs. A number of illustrative examples demonstrate that the optimal co-design schemes outperform conventional schemes that split the resources between sensing and communication, both for single-receiver and BC systems.

Motivated by automotive applications, we consider joint radar sensing and data communication for a system operating at millimeter wave (mmWave) frequency bands, where a Base Station (BS) is equipped with a co-located radar receiver and sends data using the Orthogonal Time Frequency Space (OTFS) modulation format. We consider two distinct modes of operation. In Discovery mode, a single common data stream is broadcast over a wide angular sector. The radar receiver must detect the presence of not yet acquired targets and performs coarse estimation of their parameters (angle of arrival, range, and velocity). In Tracking mode, the BS transmits multiple individual data streams to already acquired users via beamforming, while the radar receiver performs accurate estimation of the aforementioned parameters. Due to hardware complexity and power consumption constraints, we consider a hybrid digital-analog architecture where the number of RF chains and A/D converters is significantly smaller than the number of antenna array elements. In this case, a direct application of the conventional MIMO radar approach is not possible. Consequently, we advocate a beam-space approach where the vector observation at the radar receiver is obtained through a RF-domain beamforming matrix operating the dimensionality reduction from antennas to RF chains. Under this setup, we propose a likelihood function-based scheme to perform joint target detection and parameter estimation in Discovery, and high-resolution parameter estimation in Tracking mode, respectively. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while closely approaching the Cramér-Rao Lower Bound (CRLB) of the corresponding parameter estimation problem.

A memoryless state-dependent multiple-access channel (MAC) is considered, where two transmitters wish to convey messages to a single receiver while simultaneously sensing (estimating) the respective states via generalized feedbacks. This scenario is motivated by joint radar and communication, which aims to co-design radar sensing and communication over shared spectrum and hardware. An improved inner bound is provided on the fundamental rate-distortions tradeoff which characterizes the communication rates the transmitters can achieve while simultaneously ensuring that their state-estimates satisfy desired distortion criteria. The new inner bound is based on a scheme where each transmitter codes over the generalized feedback so as to improve the state estimation at the other transmitter. We demonstrate the advantage of the proposed scheme other than previous ones through some examples. This is in contrast to the previously proposed schemes where coding is only used to convey data but not sensing information.

Caching at the wireless edge nodes is a promising way to boost the spatial and spectral efficiency, for the sake of alleviating networks from content-related traffic. Coded caching originally introduced by Maddah-Ali and Niesen significantly speeds up communication efficiency by transmitting multicast messages simultaneously useful to multiple users. Most prior works on coded caching are based on the assumption that each user may request all content in the library. However, in many applications the users are interested only in a limited set of content that depends on their location. For example, assisted self-driving vehicles may access super High-Definition maps of the area through which they are travelling. Motivated by these considerations, this paper formulates the coded caching problem for location-based content with edge cache nodes. The considered problem includes a content server with access to${\mathsf N}$location-based files (e.g., High-Definition maps),${\mathsf K}$edge cache nodes located at different regions, and${\mathsf K}$users (i.e., vehicles) each of which is in the serving region of one cache node and can retrieve the cached content of this cache node with negligible cost. Depending on the location, each user only requests a file from a location-dependent subset of the library. The objective is to minimize the worst-case load (i.e., the worst-case number of broadcasted bits from the content server among all possible demands). For this novel coded caching problem, we propose a highly non-trivial converse bound under uncoded cache placement (i.e., each cache node directly copies some library bits in its cache), which shows that a simple achievable scheme is optimal under uncoded cache placement. In addition, this achievable scheme is also proved to be generally order optimal within a factor of 3. Finally, we extend the coded caching problem for location-based content to the multiaccess coded caching topology originally proposed by Hachemet al., where each user is connected to${\mathsf L}$nearest cache nodes. When${\mathsf L}\geq 2$, we characterize the exact optimality on the worst-case load.

In the context of statistical learning, the Information Bottleneck (IB) method seeks a right balance between accuracy and generalization capability through a suitable tradeoff between compression complexity, measured by minimum description length, and distortion evaluated under logarithmic loss measure. In this paper, we study a variation of the problem, called scalable information bottleneck, in which the encoder outputs multiple descriptions of the observation with increasingly richer features. The model, which is of successive-refinement type with degraded side information streams at the decoders, is motivated by some application scenarios that require varying levels of accuracy depending on the allowed level of complexity. We establish an analytic characterization of the optimal relevance-complexity region for vector Gaussian sources. Then, we derive a variational inference type algorithm for general sources with unknown distribution; and show means of parametrizing it using neural networks. Finally, we provide experimental results on the MNIST dataset which illustrate that the proposed method generalizes better to unseen data compared to the standard IB with a single description.

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e., where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e., optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading.

The problem of optimally precoding over cooperative MIMO channels when the transmitters are endowed with different noisy channel state information is a long standing and challenging open problem. Recently an information theoretic result was obtained which characterized the common message capacity of a channel with two transmitters and a single receiver with such distributed channel state information (DCSIT) generated from different feedback links. While classical MIMO precoding with centralized CSIT implies the transmission of a number of spatial streams bounded by the number of transmit and receiver antennas, the above result suggests that, surprisingly, the transmission of additional streams may be beneficial. In this work, we explore the operational implications of the above intuition to optimally tackle the problem of ergodic rate optimization under distributed feedback. In particular, we propose a method for joint distributed precoding and feedback design under asymmetric feedback rate constraints. In doing so, we also optimize the number of spatial data streams under practical complexity constraints. Finally, we provide numerical simulations and illustrate the performance gains compared to conventional precoder design.

A memoryless state-dependent broadcast channel (BC) is considered, where the transmitter wishes to convey two private messages to two receivers while simultaneously estimating the respective states via generalized feedback. The model at hand is motivated by a joint radar and communication system where radar and data applications share the same frequency band. For physically degraded BCs with i.i.d. state sequences, we characterize the capacity-distortion region tradeoff. For general BCs, we provide inner and outer bounds on the capacity-distortion region, as well as a sufficient condition when it is equal to the product of the capacity region and the set of achievable distortion. Interestingly, the proposed synergetic design significantly outperforms a conventional approach that splits the resource either for sensing or communication.

We consider a Device-to-Device (D2D) aided multicast channel, where a base station (BS) wishes to convey a common message to many receivers and these receivers cooperate with each other. We analyze the performance of a two-phase cooperative multicasting scheme requiring only statistical channel knowledge at the BS. Our analysis reveals that, as the number of receivers K grows, the two-phase scheme guarantees an average multicast rate of 1/2 log2(1 + β ln K) with high probability for any β1.

The performance of existing coded caching schemes is sensitive to the worst channel quality, a problem which is exacerbated when communicating over fading channels. In this paper, we address this limitation in the following manner: in short-term, we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness among users. Our online scheme combines (i) the classical decentralized coded caching scheme with (ii) joint scheduling and power control for the fading broadcast channel, as well as (iii) congestion control for ensuring the optimal long-term average performance. We prove that our online delivery scheme maximizes the alpha-fair utility among all schemes restricted to decentralized placement. By tuning the value of alpha, the proposed scheme can achieve different operating points on the average delivery rate region and tune performance according to an operator's choice. We demonstrate via simulations that our scheme outperforms two baseline schemes: (a) standard coded caching with multicast transmission, limited by the worst channel user yet exploiting the global caching gain; (b) opportunistic scheduling with unicast transmissions exploiting the fading diversity but limited to local caching gain.

We consider a joint radar parameter estimation and communication system using orthogonal time frequency space (OTFS) modulation. The scenario is motivated by vehicular applications where a vehicle (or the infrastructure) equipped with a mono-static radar wishes to communicate data to its target receiver, while estimating parameters of interest related to this receiver. Provided that the radar-equipped transmitter is ready to send data to its target receiver, this setting naturally assumes that the receiver has been already detected. In a point-to-point communication setting over multipath time-frequency selective channels, we study the joint radar and communication system from two perspectives, i.e., the radar parameter estimation at the transmitter as well as the data detection at the receiver. For the radar parameter estimation part, we derive an efficient approximated Maximum Likelihood algorithm and the corresponding Cramér-Rao lower bound for range and velocity estimation. Numerical examples demonstrate that multi-carrier digital formats such as OTFS can achieve as accurate radar estimation as state-of-the-art radar waveforms such as frequency-modulated continuous wave (FMCW). For the data detection part, we focus on separate detection and decoding and consider a soft-output detector that exploits efficiently the channel sparsity in the Doppler-delay domain. We quantify the detector performance in terms of its pragmatic capacity, i.e., the achievable rate of the channel induced by the signal constellation and the detector soft-output. Simulations show that the proposed scheme outperforms concurrent state-of-the-art solutions. Overall, our work shows that a suitable digitally modulated waveform enables to efficiently operate joint radar parameter estimation and communication by achieving full information rate of the modulation and near-optimal radar estimation performance. Furthermore, OTFS appears to be particularly suited to the scope.

Multicast services, whereby a common valuable message needs to reach a whole population of user equipments (UEs), are gaining attention on account of new applications such as vehicular networks. As it proves challenging to guarantee decodability by every UE in a large population, service reliability is indeed the Achilles' heel of multicast transmissions. To circumvent this problem, a two-phase protocol capitalizing on device-to-device (D2D) links between UEs has been proposed, which overcomes the vanishing behavior of the multicast rate. In this paper, we revisit such a D2D-aided protocol in the new light of precoding capabilities at the base station (BS). We obtain an enhanced scheme that aims at selecting a subset of UEs who cooperate to spread the common message across the rest of the network via D2D retransmissions. With the objective of maximizing the multicast rate under some outage constraint, we propose an algorithm with provable convergence that jointly identifies the most pertinent relaying UEs and optimizes the precoding strategy at the BS.

A memoryless state-dependent multiple access channel (MAC) is considered where two transmitters wish to convey a respective message to a receiver while simultaneously estimating the respective channel state via generalized feedback. The scenario is motivated by a joint radar and communication system where the radar and data applications share the same bandwidth. An achievable capacity-distortion tradeoff region is derived that outperforms a resource-sharing scheme through a binary erasure MAC with binary states.

We consider coded caching over the fading broadcast channel, where the users, equipped with a memory of finite size, experience asymmetric fading statistics. It is known that a naive application of coded caching over the channel at hand performs poorly especially in the regime of a large number of users due to a vanishing multicast rate. We overcome this detrimental effect by a careful design of opportunistic scheduling policies such that some utility function of the long-term average rates should be maximized while balancing fairness among users. In particular, we propose a threshold-based scheduling that requires only statistical channel state information and one-bit feedback from each user. More specifically, each user indicates via feedback whenever its SNR is above a threshold determined solely by the fading statistics and the fairness requirement. Surprisingly, we prove that this simple scheme achieves the optimal utility in the regime of a large number of users.

A communication setup is considered where a transmitter wishes to simultaneously sense its channel state and convey a message to a receiver. The state is estimated at the transmitter by means of generalized feedback, i.e. a strictly causal channel output that is observed at the transmitter. The scenario is motivated by a joint radar and communication system where the radar and data applications share the same frequency band. For the case of a memoryless channel with i.i.d. state sequences, we characterize the capacity-distortion tradeoff, defined as the best achievable rate below which a message can be conveyed reliably while satisfying some distortion constraint on state sensing. An iterative algorithm is proposed to optimize the input probability distribution. Examples demonstrate the benefits of joint sensing and communication as compared to a separation-based approach.

We consider a device-to-device (D2D) aided multicast channel, where a transmitter wishes to convey a common message to many receivers and these receivers cooperate with each other. We propose a simple computationally efficient scheme requiring only statistical channel knowledge at transmitter. Our analysis in general topologies reveals that, when the number of receivers$K$grows to infinity, the proposed scheme guarantees a multicast rate of$\frac{1}{2}\log_{2}(1+\beta\ln K)$with high probability for any$\beta < \beta^{\star}$where$\beta^{\star}$depends on the network topology. This scheme undergoes a phase transition at threshold$\beta^{\star}\ln K$where transmissions are successful/unsuccessful with high probability when the SNR is above/below this threshold. We also analyze the outage rate of the proposed scheme in the same setting.

We consider coded caching over the fading broadcast channel, where the users, equipped with a memory of finite size, experience asymmetric fading statistics. It is known that a naive application of coded caching over the channel at hand performs poorly especially in the regime of a large number of users due to a vanishing multicast rate. We overcome this detrimental effect by a careful design of opportunistic scheduling policies such that some utility function of the long-term average rates should be maximized while balancing fairness among users. In particular, we propose a threshold-based scheduling that requires only statistical channel state information and one-bit feedback from each user. More specifically, each user indicates via feedback whenever its SNR is above a threshold determined solely by the fading statistics and the fairness requirement. Surprisingly, we prove that this simple scheme achieves the optimal utility in the regime of a large number of users. Numerical examples show that our proposed scheme performs closely to the scheduling with full channel state information, but at a significantly reduced complexity.

We consider the content delivery problem in a fading multi-input single-output channel with cache-aided users. We are interested in the scalability of the equivalent content delivery rate when the number of users, K, is large. Analytical results show that, using coded caching and wireless multicasting, without channel state information at the transmitter, linear scaling of the content delivery rate with respect to K can be achieved in some different ways. First, if the multicast transmission spans over L independent sub-channels, e.g., in quasi-static fading if L = 1, and in block fading or multi-carrier systems if L 1, linear scaling can be obtained, when the product of the number of transmit antennas and the number of sub-channels scales logarithmically with K. Second, even with a fixed number of antennas, we can achieve the linear scaling with a threshold-based user selection requiring only one-bit feedbacks from the users. When CSIT is available, we propose a mixed strategy that combines spatial multiplexing and multicasting. Numerical results show that, by optimizing the power split between spatial multiplexing and multicasting, we can achieve a significant gain of the content delivery rate with moderate cache size.

We consider content delivery over fading broadcast channels. A server wants to transmit K files to K users, each equipped with a cache of finite size. Using the coded caching scheme of Maddah-Ali and Niesen, we design an opportunistic delivery scheme where the long-term sum content delivery rate scales with the number of users in the system. The proposed delivery scheme combines superposition coding together with appropriate power allocation across sub-files intended to different subsets of users. We analyze the long- term average sum content delivery rate achieved by two special cases of our scheme: 1) a selection scheme that chooses the subset of users with the largest weighted rate, and 2) a baseline scheme that transmits to all K users using the scheme of Maddah-Ali and Niesen. We prove that coded caching with appropriate user selection is scalable since it yields a linear increase of the average sum content delivery rate.

The performance of existing coded caching schemes is sensitive to the worst channel quality, when applied to wireless channels. In this paper, we address this limitation in the following manner: in short-term, we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness across the different users. Our online delivery scheme combines (i) joint scheduling and power control for the fading broadcast channel, and (ii) congestion control for ensuring the optimal long-term average performance. By restricting the caching operations to decentralized coded caching proposed in the literature, we prove that our proposed scheme has near-optimal overall performance with respect to the long-term alpha fairness performance. By tuning the coefficient alpha, the operator can differentiate the user performance in terms of video delivery rates achievable by coded caching. We demonstrate via simulations that our scheme outperforms standard coded caching and unicast opportunistic scheduling, which are identified as special cases of our general framework.

We study a content delivery problem in the context of a K-user erasure broadcast channel such that a content providing server wishes to deliver requested files to users, each equipped with a cache of a finite memory. Assuming that the transmitter has state feedback and user caches can be filled during off-peak hours reliably by decentralized cache placement, we characterize the achievable rate region as a function of the memory sizes and the erasure probabilities. The proposed delivery scheme, based on the broadcasting scheme proposed by Wang and Gatzianas et al., exploits the receiver side information established during the placement phase. Our results can be extended to centralized cache placement as well as multi-antenna broadcast channels with state feedback.

We study a content delivery problem in a K-user erasure broadcast channel such that a content providing server wishes to deliver requested files to users, each equipped with a cache of a finite size. Assuming that the transmitter has state feedback and user caches can be filled during off-peak hours reliably by the decentralized content placement, we characterize the achievable rate region as a function of the memory sizes and the erasure probabilities for some special cases. The proposed delivery scheme, based on the broadcasting scheme by Wang and Gatzianas et al., exploits the receiver side information established during the placement phase. Our results can be extended to the centralized content placement as well as multi-antenna broadcast channels with state feedback.

In this paper, we consider the secure communication in a K-user multi-antenna broadcast channel (BC) with state feedback. We characterize the optimal secure degrees of freedom (SDoF) region of multiple-input single-output (MISO) channel. The SDoF region is achievable by a secret key based linear scheme, which generates analog secret keys by sending artificial noise and then performs space-time alignment scheme secured by these secret keys. The optimality is proved by deriving a new outer bound on the capacity region in a systematic way. Interestingly, the proposed outer bounding technique also applies to the erasure broadcast channel and provides a simpler proof as compared to the existing one. Finally, an explicit connection between the multi-antenna BC and the erasure BC is revealed. We show that, with a large number of users, secrecy cannot be guaranteed in the erasure BC, while it comes almost “for free” in the multi-antenna BC when the number of transmit antennas grows accordingly.

We consider the temporally correlated multiple-input multiple-output (MIMO) broadcast channels (BC) and interference channels (IC) where the transmitter(s) has/have 1) delayed channel state information (CSI) obtained from a latency-prone feedback channel as well as 2) imperfect current CSIT, obtained, e.g., from prediction on the basis of these past channel samples based on the temporal correlation. The degrees of freedom (DoF) regions for the two-user broadcast and interference MIMO networks with general antenna configuration under such conditions are fully characterized, as a function of the prediction quality indicator. Specifically, a simple unified framework is proposed, allowing us to attain optimal DoF region for the general antenna configurations and current CSIT qualities. Such a framework builds upon block-Markov encoding with interference quantization, optimally combining the use of both outdated and instantaneous CSIT. A striking feature of our work is that, by varying the power allocation, every point in the DoF region can be achieved with one single scheme. As a result, instead of checking the achievability of every corner point of the outer bound region, as typically done in the literature, we propose a new systematic way to prove the achievability.

The two-user Multiple-Input Multiple-Output (MIMO) broadcast channel (BC) with arbitrary antenna configuration is considered, in which the transmitter obtains (i) delayed channel state information (CSI) from a latency-prone feedback channel as well as (ii) imperfect current CSI, e.g., from prediction based on these past channel samples. The degrees of freedom (DoF) region under such a setting is fully characterized as a function of a prediction quality exponent. This work extends prior work, previously limited to MISO, to fully general antenna settings. An intriguing by-product of our results is to reveal the benefits of dealing with an asymmetric multi-user MIMO configuration (i.e., one in which terminals do not have the same number of antennas) in the case of non-perfect CSIT (e.g., caused by feedback delays or limited preciseness).

We define an “analog” index coding problem where a transmitter wishes to send a set of analog sources to be reconstructed with a desired distortion level at K receivers, each of which is characterized by a set of desired sources and by its own side information. The transmission channel is a multi-input single-output (MISO) broadcast channel with independent fading, perfect channel state information at the receiver and (strictly causal) feedback at the transmitter. An outer bound on the rate-distortion region is derived, revealing an interesting tradeoff between the communication rate (source samples per channel use) and the distortion exponential decay with snr in dB. We focus on the high-SNR and low-distortion regime, in which the proposed outer bound is shown to be tight for some cases of particular interest.

Vandermonde-subspace frequency division multiplexing (VFDM) is an overlay spectrum sharing technique for cognitive radio. VFDM makes use of a precoder based on a Vandermonde structure to transmit information over a secondary system, while keeping an orthogonal frequency division multiplexing (OFDM)-based primary system interference-free. To do so, VFDM exploits frequency selectivity and the use of cyclic prefixes by the primary system. Herein, a global view of VFDM is presented, including also practical aspects such as linear receivers and the impact of channel estimation. We show that VFDM provides a spectral efficiency increase of up to 1 bps/Hz over cognitive radio systems based on unused band detection. We also present some key design parameters for its future implementation and a feasible channel estimation protocol. Finally we show that, even when some of the theoretical assumptions are relaxed, VFDM provides non-negligible rates while protecting the primary system.

We consider the time correlated multiple-input single-output (MISO) broadcast channel where the transmitter has imperfect knowledge of the current channel state, in addition to delayed channel state information. By representing the quality of the current channel state information asP-αfor the signal-to-noise ratioPand some constant α ≥ 0, we characterize the optimal degrees of freedom region for this more general two-user MISO broadcast correlated channel. The essential ingredients of the proposed scheme lie in the quantization and multicast of the overheard interferences, while broadcasting new private messages. Our proposed scheme smoothly bridges between the scheme recently proposed by Maddah-Ali and Tse with no current state information and a simple zero-forcing beamforming with perfect current state information.

The degrees of freedom (DoF) of the two-user Gaussian multiple-input and multiple-output (MIMO) broadcast channel with confidential messages is studied under the assumption that delayed channel state information (CSI) is available at the transmitter. We characterize the optimal secrecy DoF (SDoF) region and show that it can be achieved by a simple artificial noise alignment scheme. The proposed scheme sends the confidential messages superposed with the artificial noise over several time slots. Exploiting delayed CSI, the transmitter aligns the transmit signal in such a way that the useful message can be extracted at the intended receiver but is completely drowned by the artificial noise at the unintended receiver. The proposed scheme can be regarded as a nontrivial extension of Maddah-Ali Tse scheme and enables us to quantify the resource overhead, or equivalently the DoF loss, to be paid for the secure communications.

We compare two classes of linear precoding strategies, zero-forcing (ZF) beamforming and the “MAT” scheme recently proposed by Maddah-Ali and Tse, over time-varying MISO broadcast channels. For a temporally correlated fading process with band-limited Doppler spectrum in [-F, F], with 0 ≤ F <; 1/2, it has been shown that ZF achieves a fraction 1-2F of the optimal degrees of freedom (DoF). On the other hand, MAT exploits delayed channel state information at transmitter (CSIT) and guarantees a constant DoF irrespectively of the fading Doppler bandwidth. In this work, we compare the net DoFs of both schemes by accounting for the correlation between fading blocks and for the resource required for downlink channel estimation. It is found that the downlink training for each receiver to learn the CSI of the others might be detrimental for the performance of MAT especially when the system size (number of users and transmit antennas) is not much smaller than the channel block length.

We consider the time correlated MISO broadcast channel where the transmitter has partial knowledge on the current channel state, in addition to delayed channel state information (CSI). Rather than exploiting only the current CSI, as the zero-forcing precoding, or only the delayed CSI, as the Maddah-Ali-Tse (MAT) scheme, we propose a seamless strategy that takes advantage of both. The achievable degrees of freedom of the proposed scheme is characterized in terms of the quality of the current channel knowledge.

We consider the two-user MISO broadcast channel where the transmitter has imperfect knowledge on the current channel state, in addition to delayed channel state information. The degree of freedom region is completely characterized. The optimal scheme smoothly bridges between the scheme recently proposed by Maddah-Ali and Tse with no current state information and a simple zero-forcing beamforming with perfect current state information. The essential ingredients of this scheme lie in the quantization and multicasting of the overheard interferences, while broadcasting new private messages.

We study a multi-cell frequency-selective fading uplink channel from K user terminals (UTs) to B base stations (BSs). The BSs, assumed to be oblivious of the applied encoding scheme, compress and forward their observations to a central station (CS) via capacity limited backhaul links. The CS jointly decodes the messages from all UTs. Since we assume no prior channel state information, the channel needs to be estimated during its coherence time. Based on a lower bound of the ergodic mutual information, we determine the optimal fraction of the coherence time used for channel training. We then study how the optimal training length is impacted by the backhaul capacity. Our analysis is based on large random matrix theory but shown by simulations to be tight for even small system dimensions.

In previous works, Vandermonde-subspace frequency division multiplexing (VFDM) has been shown to promote overlay networks by enabling a secondary transmitter to cancel its interference to a primary receiver, while simultaneously transmitting useful information to its own receiver at nonnegligible rates. Interference cancelation is achieved by exploiting the null-space of the channel from the secondary transmitter to the primary receiver. In the wake of a global deployment of the third generation partnership project's (3GPP) long term evolution (LTE), one of the open questions of VFDM concerns its applicability in a primary LTE-orthogonal frequency division multiple access (OFDMA) multi-user setting. In this work, we address this question by extending VFDM to the multi-user scenario where the primary system employs OFDMA, such as LTE. We show that by using at the secondary system a similar precoder structure to the ones previously studied, we are able to cancel the interference towards multiple primary receivers while still achieving acceptable rates for the secondary system.

We consider a certain class of large random matrices, composed of independent column vectors with zero mean and different covariance matrices, and derive asymptotically tight deterministic approximations of their moments. This random matrix model arises in several wireless communication systems of recent interest, such as distributed antenna systems or large antenna arrays. Computing the linear minimum mean square error (LMMSE) detector in such systems requires the inversion of a large covariance matrix which becomes prohibitively complex as the number of antennas and users grows. We apply the derived moment results to the design of a low-complexity polynomial expansion detector which approximates the matrix inverse by a matrix polynomial and study its asymptotic performance. Simulation results corroborate the analysis and evaluate the performance for finite system dimensions.

The secrecy degrees of freedom (SDoF) of the Gaussian multiple-input and single-output (MISO) wiretap channel is studied under the assumption that delayed channel state information (CSI) is available at the transmitter and each receiver knows its own instantaneous channel. Such scenario is of practical interest since the legitimate receiver may send its channel states to the transmitter which is overheard by the eavesdropper. We first show that a strictly positive SDoF can be guaranteed whenever the transmitter has delayed CSI (either on the legitimate channel or/and the eavesdropper channel). In particular, in the case with delayed CSI on both channels, it is shown that the optimal SDoF is 2=3. We then generalize the result to the two-user Gaussian MISO broadcast channel with confidential messages and characterize the SDoF region when the transmitter has delayed CSI of both receivers. Interestingly, the artificial noise schemes are shown to provide the optimal SDoF region by masking the confidential message to the unintended receiver while aligning the interference at each receiver.

We consider a MIMO fading broadcast channel where the fading channel coefficients are constant over time-frequency blocks that span a coherent time x a coherence bandwidth. In closed-loop systems, channel state information at transmitter (CSIT) is acquired by the downlink training sent by the base station and an explicit feedback from each user terminal. In open-loop systems, CSIT is obtained by exploiting uplink training and channel reciprocity. We use closed-form lower bounds and tight approximations of the ergodic achievable rate in the presence of CSIT errors in order to optimize the overall system throughput, by taking explicitly into account the overhead due to channel estimation and channel state feedback. Based on three time-frequency block models inspired by actual systems, we provide useful guidelines for the overall system optimization. In particular, digital (quantized) feedback is found to offer a substantial advantage over analog (unquantized) feedback.

We consider a practical scenario of the Gaussian multiantenna wiretap channel where a transmitter with no channel state information wishes to send a confidential message to its legitimate receiver in the presence of an eavesdropper. It has been known that the secrecy capacity of such a channel does not scale with signal-to-noise ratio under general conditions. Taking into account the different temporal fading structures at the legitimate receiver and the eavesdropper, we characterize lower and upper bounds on the secrecy degrees of freedom (s.d.o.f.) of the channel at hand. Our results show that a positive s.d.o.f. can be ensured whenever two receivers experience the asynchronous variation. Remarkably, simple linear precoding schemes provide the optimal s.d.o.f. in most cases of interest by aligning either the confidential signal at the eavesdropper or the artificial noise at the legitimate receiver.

We consider the multi-antenna wiretap channel in which the transmitter wishes to send a confidential message to its receiver while keeping it secret to the eavesdropper. It has been known that the secrecy capacity of such a channel does not increase with signal-to-noise ratio when the transmitter has no channel state information (CSI) under mild conditions. Motivated by Jafar's robust interference alignment technique, we study the so-called staggered multi-antenna block-fading wiretap channel where the legitimate receiver and the eavesdropper have different temporal correlation structures. Assuming no CSI at transmitter, we characterize lower and upper bounds on the secrecy degrees of freedom (s.d.o.f.) of the channel at hand. Our results show that a positive s.d.o.f. can be ensured whenever two receivers experience different fading variation. Remarkably, very simple linear precoding schemes provide the optimal s.d.o.f. in some cases of interest.

Vandermonde-subspace frequency division multiplexing (VFDM) is a technique for interference cancellation in overlay networks that allows a secondary network to operate simultaneously with a primary network, on the same frequency band. VFDM can be applied to block transmission systems with a guard time (or cyclic prefix) over frequency selective channels. It achieves zero interference towards the primary system by employing a special precoder that aligns the data to the null space of the interfering channel from the secondary to the primary system. In this work, we extend the assessment of VFDM by analyzing the bit error rate and sum rate capacity of practical linear receiver structures for the VFDM-based secondary system. The study realized herein serves as a basis for the implementation of a VFDM prototype system on a real transmission testbed.

In this paper, we consider a multiple-input-multiple-output (MIMO) fading broadcast channel and compute achievable ergodic rates when channel state information (CSI) is acquired at the receivers via downlink training and it is provided to the transmitter by channel state feedback. Unquantized (analog) and quantized (digital) channel state feedback schemes are analyzed and compared under various assumptions. Digital feedback is shown to be potentially superior when the feedback channel uses per channel state coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a minor effect even if simple uncoded modulation is used on the feedback channel. We discuss first the case of an unfaded additive white Gaussian noise (AWGN) feedback channel with orthogonal access and then the case of fading MIMO multiple access (MIMO-MAC). We show that by exploiting the MIMO-MAC nature of the uplink channel, a much better scaling of the feedback channel resource with the number of base station (BS) antennas can be achieved. Finally, for the case of delayed feedback, we show that in the realistic case where the fading process has (normalized) maximum Doppler frequency shift 0 ¿ F < 1/2, a fraction 1 - 2F of the optimal multiplexing gain is achievable. The general conclusion of this work is that very significant downlink throughput is achievable with simple and efficient channel state feedback, provided that the feedback link is properly designed.

We consider a multi-cell MIMO downlink (network MIMO) where B base-stations (BS) with M antennas, connected to a central station (CS), transmit K messages to K single-antenna user terminals (UT) simultaneously. Although many works have shown the potential benefits of network MIMO, the conclusion critically depends on the underlying assumption of perfect channel state information at transmitters (CSIT). In this paper, we propose an outage-efficient strategy that requires only partial CSIT. Namely, with side information of all UT's messages and local CSIT, each BS applies zero-forcing (ZF) beamforming in a distributed manner, which creates K parallel MISO channels. Based on the statistical knowledge of these parallel channels, the CS performs a robust power allocation that jointly minimizes the outage probability of K UTs and achieves a diversity gain of B(M - K + 1) per UT. Numerical results show that even with partial CSIT network MIMO can be beneficial by providing high data rates with a sufficient reliability to individual users.

We study the compound multi-input multi-output (MIMO) broadcast channel with confidential messages (BCC), where one transmitter sends a common message to two receivers and two confidential messages respectively to each receiver. The channel state may take one of a finite set of states, and the transmitter knows the state set but does not know the realization of the state. We study achievable rates with perfect secrecy in the high SNR regime by characterizing an achievable secrecy degree of freedom (s.d.o.f.) region for two models, the Gaussian MIMO-BCC and the ergodic fading multi-input single-output (MISO)-BCC without a common message. We show that by exploiting an additional temporal dimension due to state variation in the ergodic fading model, the achievable s.d.o.f. region can be significantly improved compared to the Gaussian model with a constant state, although at the price of a larger delay.

We consider a MIMO fading broadcast channel where channel state information is acquired at user terminals via downlink training and channel feedback is used to provide transmitter channel state information (CSIT) to the base station. The feedback channel (the corresponding uplink) is modeled as an AWGN channel, orthogonal across users. The total bandwidth consumed is the sum of the bandwidth/resources used for downlink training, channel feedback, and data transmission. Assuming that the channel follows a block fading model and that zero-forcing beamforming is used, we optimize the net achievable rate for unquantized (analog) and quantized (digital) channel feedback. The optimal number of downlink training pilots is seen to be essentially the same for both feedback techniques, but digital feedback is shown to provide a larger net rate than analog feedback.

We consider a two-hop wireless network where a transmitter communicates with a receiver via M relays with an amplify-and-forward (AF) protocol. Recent works have shown that the sophisticated linear processing such as beamforming and distributed space-time coding (DSTC) at relays enables to improve the AF performance. However, the relative utility of these strategies depends on the available channel state information at transmitter (CSIT), which in turn depends on the system parameters such as the speed of the underlying fading channel and that of training and feedback procedures. Moreover, it is of practical interest to have a single transmit scheme that handles different CSIT scenarios. This motivates us to consider a unified approach based on DSTC that potentially provides diversity gain with statistical CSIT and exploits some additional side information if available. Under individual power constraints at the relays, we optimize the amplifier power allocation such that pairwise error probability conditioned on the available CSIT is minimized. Under perfect CSIT, we propose an on-off gradient algorithm that efficiently finds a set of relays to switch on. Under partial and statistical CSIT, we propose a simple waterfilling algorithm that yields a non-trivial solution between maximum power allocation and a generalized STC that equalizes the averaged amplified noise for all relays. Moreover, we derive the closed-form solutions for M = 2 and in certain asymptotic regimes that enable an easy interpretation of the proposed algorithms. It is found that an appropriate amplifier power allocation is mandatory for DSTC to offer sufficient diversity and power gain in a general network topology.

We consider a MIMO fading broadcast channel where channel state information is acquired at user terminals via downlink training and explicit analog feedback is used to provide transmitter channel state information (CSIT) to the base station. The feedback channel (the corresponding uplink) is modeled as a MIMO multiple access channel. Under the assumption that data transmission, downlink training, and feedback are performed within the same channel coherence interval of length T symbols, the optimization of a lower bound on the achievable ergodic rate sum yields a non-trivial resource allocation tradeoff.We solve this tradeoff and provide the optimal training and feedback resource allocation for the case of zero-forcing beamforming. When the same power level is used during all stages, it is found that the optimal length of the training + feedback phases increases as O(radicT) for large T. On the other hand, when different power levels can be used for different stages, for sufficiently large T it is optimal to use the minimum number of symbols for training + feedback but to use power of order O(radicT).

We study the secrecy capacity of the frequency-selective wiretap channel. Assuming that a guard interval of L symbols is inserted to avoid the inter-block interference and these symbols are discarded at the receiver, the single-carrier frequency-selective channel is modeled as a multiple-input multiple-output Toeplitz matrix. For this special case of the MIMO wiretap channel and under the assumption of perfect channel knowledge at transmitter (CSIT), we propose a practical Vandermonde precoding scheme that transmits the confidential messages on the zeros of the eavesdropper channel. It is proved that this Vandermonde precoding achieves the full multiplexing gain offered by the frequency-selective wiretap channel in the high SNR regime. For a more realistic case where the transmitter only knows the legitime channel we consider the ldquomask beamformingrdquo scheme where the artificial noise is sent on the zeros of the legitime channel via the Vandermonde precoding. This mask beamforming is shown to achieve the same multiplexing gain as the perfect CSIT.

We study the maximization of the weighted sum rate in Gaussian multi-input multi-output OFDM broadcast channel under a total power constraint. This problem is motivated by adaptive resource allocation policies in a multi-carrier wireless system with multiple antennas at the base station. We propose a iterative waterfilling algorithm based on dual decomposition. Two decompositions are considered, one in subcarrier domain and another in both subcarrier and user domain. We show that both decompositions reduce to an identical problem that can be solved by multiuser waterfilling approach simultaneously for all subcarriers. A master problem is solved iteratively to achieve the total power constraint by a simple bisection method. Numerical examples show that our proposed algorithm converges much faster than steepest ascent algorithm and makes convergence almost independent of a number of subcarriers and antennas.

We consider a MIMO fading broadcast channel and compare the achievable ergodic rates when the channel state information at the transmitter is provided by "analog" noisy feedback or by quantized (digital) feedback. The superiority of digital feedback is shown, with perfect or imperfect CSIR, whenever the number of feedback channel uses per channel coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a minor effect even by using very simple uncoded modulation. Finally, we show that analog feedback achieves a fraction 1 - 2F of the optimal multiplexing gain even in the presence of a feedback delay, when the fading belongs to the class of "Doppler processes" with normalized maximum Doppler frequency shift 0 les F les 1/2.

We consider a MIMO fading broadcast channel and study the achievable throughput of zero-forcing downlink beamforming when the channel state information (CSI) available to the transmitter and/or receivers is imperfect. Each receiver (mobile) acquires imperfect CSI via downlink training pilots, and the transmitter acquires CSI through explicit feedback from each mobile. We analyze both analog and digital (i.e., quantized) channel feedback techniques. Our analysis quantifies the throughput degradation due to limited training, limited feedback resources (measured in feedback channel symbols rather than bits), and errors on the feedback channel. Using these results we are able to quantify scenarios in which digital feedback outperforms analog and also provide guidelines for the optimal allocation of resources to training and channel feedback.

We consider the downlink of a wireless system where the base-station has M ges 1 antennas and K user terminals have one antenna each. We study the weighted rate sum maximization in the case of non-perfect Channel State Information at the Transmitter (CSIT). Some relevant downlink optimization problems, such as the stabilization of the transmission queues under random packet arrivals and the proportional fair scheduling for infinite backlogged systems, can be solved as special cases of the proposed problem. We restrict the transmitter strategy to be based on Gaussian coding and beamforming. Even under this simplifying condition, the problem at hand is non-convex and it does not appear to lend itself to a simple algorithmic solution. Therefore, we introduce some approximations that yield a definition of signal-to-interference plus noise ratio (SINR) commonly used in the classical array- processing/beamforming literature. For the simpler (but still non-convex) approximated problem, we propose a powerful heuristic solution based on greedy user selection and a gradient iteration that converges to a local maximum of the objective function. This method yields very competitive results with relatively low computational complexity. Extensive simulations show that, in the case of perfect CSIT, the proposed heuristic scheme performs very closely to the optimal (dirty-paper coding) strategy while, in the case of non-perfect CSIT, it significantly outperforms previously proposed suboptimal approaches, such as random beamforming and approximated zero-forcing with greedy user selection.

We compare space-time coding (transmit diversity) and random "opportunistic" beamforming in a space-division multiple access/time-division multiple access single-cell downlink system with random packet arrivals, correlated block-fading channels, and non-perfect channel state information at the transmitter due to a feedback delay. Our comparison is based on system stability. The ability of accurately predicting the channel signal-to-noise ratio dominates the performance of opportunistic beamforming, even under the optimistic assumption that the sequence of beamforming matrices is perfectly known a priori by the receivers. Our results show that the relative merit of opportunistic beamforming versus space-time coding strongly depends on the channel Doppler bandwidth. Therefore, previous naive conclusions on the fact that transmit diversity always hurts the system performance under multiuser-diversity scheduling should be taken with great care

We study a number of downlink signaling strategies under perfect and non-perfect channel state information at the transmitter (CSIT) for the case of random packet arrivals. Under this setting, the relevant fairness criterion is the stabilization of all buffer queues which guarantees a bounded average delay for all users. In the case of perfect CSIT, different linear beamforming schemes are compared with the stability optimal policy based on dirty-paper coding (DPC). It is found that simple linear beamforming schemes with greedy user selection achieve near DPC performance in terms of the average delay. In the case of non-perfect CSIT due to a delayed feedback, we propose a novel greedy beamforming (heuristic) scheme together with "analog feedback" of CSI, where each user sends back its estimated channel vector without quantizing and coding. The proposed scheme outperforms the previously proposed schemes such as opportunistic beamforming and requires a similar feedback overhead

We consider the maximization of weighted rate sum in Gaussian multiple-input-multiple-output broadcast channels. This problem is motivated by optimal adaptive resource allocation policies in wireless systems with multiple antenna at the base station. In fact, under random packet arrival and transmission queues, the system stability region is achieved by maximizing a weighted rate sum with suitable weights that depend on the queue buffer sizes. Our algorithm is a generalization of the well-known Iterative Multiuser Water-Filling that maximizes the rate sum under a total transmit power constraint and inherits from the latter its simplicity. We propose also a variation on the basic algorithm that makes convergence speed very fast and essentially independent of the number of users

We derive a low-complexity receiver scheme for joint multiuser decoding and parameter estimation of code division multiple access signals. The resulting receiver processes the users serially and iteratively and makes use of soft-in soft-out single-user decoders, of soft interference cancellation and of expectation-maximization parameter estimation as the main building blocks. Computer simulations show that the proposed receiver achieves near single-user performance at very high channel load (number of users per chip) and outperforms conventional schemes with similar complexity.

This paper proposes an anti-shadowing scheme employing site diversity combined with orthogonal frequency division multiplexing (OFDM) modulation for 60 GHz wireless LAN. In the proposed system which we name overlapped-spot diversity, multiple access points (APs) sufficiently separated on a ceiling cover a same terminal so that it can receive more than one direct LOS paths even under shadowing situation. By using OFDM modulation, multiple APs within a single cell can transmit identical information using an identical frequency band while a terminal can simply add up all the arrived signals from those APs without any specific combining technique. Our computer simulation results by means of ray-tracing showed the system with multiple APs achieves better robustness to shadowing compared to that with single AP in terms of average bit error rate and outage probability.

This paper investigates the optimal access point placement in a simultaneous broadcast system using orthogonal frequency division multiplexing (OFDM) for high data rate indoor wireless LAN. We aim to determine the best combination of multiple AP placements that minimizes the average bit error rate (BER) of terminals when input information including an indoor configuration, a number of APs, and total transmission power is given a priori. Since this AP placement problem is categorized as a hard combinatorial problem with many variables and constraints, we applied a nonlinear optimization scheme called very fast simulated annealing to search for an approximate optimal solution. The simulation results demonstrate that the algorithm has been successfully applied to the AP placement problem and its effectiveness was also confirmed in comparison with a local search algorithm.