This paper studies expurgated random coding bounds and exponents for source coding with side information with a given (possibly mismatched) decoding rule. We propose an expurgation technique that is an iterative version of Gallager’s expurgation method for channel coding and enables a direct dual domain derivation of non-asymptotic bounds for discrete sources with arbitrary side information alphabets and decoding metrics. Specializing the bounds to memoryless models a dual domain achievable error exponent for type-by-type random coding is derived and shown to coincide with the Csiszár-Körner exponent obtained via graph decomposition.

In lossless source coding, one-to-one codes refer to encodings without the prefix constraint. In this paper, we consider Campbell’s generalized expected length for such codes, i.e., the normalized cumulant generating function of codeword lengths. We show that the $\rho$-th order generalized expected length of any one-to-one code for a discrete random variable $X$ is at least \[H_{\frac{1}{1+\rho}}(X) - \log\big(H(X_{\frac{1}{1+\rho}}) + 1\big) - \log e, \] where $H_{\frac{1}{1+\rho}}(X)$ is the Rényi entropy of $X$, and $H(X_{\frac{1}{1+\rho}})$ is the Shannon entropy of the corresponding tilted (escort) distribution of order $1/(1+\rho)$. This result generalizes a bound by Alon and Orlitsky concerning the expected length, which is recovered by setting $\rho = 0$. Moreover, we show that the same bound also applies to the $\rho$-th guessing moment, yielding a lower bound that is valid for countably infinite supports.

We study the exponential strong converse for the Gelfand-Pinsker channel, i.e., the exponential speed at which the decoding error probability converges to 1 at rates above capacity. We establish the exact convergence speed, known as the strong converse exponent, by deriving matching upper and lower bounds. The upper bound is derived by applying and analyzing the likelihood decoder. The lower bound follows from single-letterizing a KL-divergence based on the weak converse.

We study joint communication and sensing in an OFDM system, involving a transmitter sending a message to a receiver while enabling radar sensing by generating back-scattered signals. The sensing task is ranging, modeled by on-grid recovery of target delays that remain fixed over the transmission block, and formulated as a multiple hypothesis testing problem. We establish the exact tradeoff between the achievable communication rate and the ranging error exponent, and show that the tradeoff relies only on the power allocation across subcarriers. We further identify scenarios where uniform power allocation is optimal and sub-optimal for the ranging task.

We introduce an expurgation method for source coding with side information that enables direct dual-domain derivations of expurgated error exponents. Dual-domain methods yield optimization problems over few parameters, with any sub-optimal choice resulting in an achievable exponent, as opposed to primal-domain optimization over distributions. In addition, dual-domain methods naturally allow for general alphabets and/or memory. We derive two such expurgated error exponents for different random-coding ensembles in the case where the decoder is possibly mismatched with respect to the source and side information joint distribution. We show the better of the exponents coincides with the Csiszár-Körner exponent obtained via a graph decomposition lemma. We show some numerical examples that illustrate the differences between the two exponents and show that in the case of source coding without side information, the expurgated exponent coincides with the error exponent of the source optimal code.

The information bottleneck channel, also known as oblivious relaying, is a two-hop channel where a transmitter sends messages to a remote receiver via an intermediate relay node. A codeword sent by the transmitter passes through a discrete memoryless channel to reach the relay, which then processes the noisy channel output and forwards it to the receiver through a noiseless rate-limited link. The relay is oblivious, in the sense that it has no knowledge of the channel codebook used in transmission. Previous works on oblivious relaying focus on characterizing achievable rates. In this work, we study error exponents and explore connections to lossless source coding with a helper, also known as the Wyner-Ahlswede-Körner (WAK) problem. We first establish an achievable error exponent for oblivious relaying under constant compositions codes. A key feature of our analysis is the use of the type covering lemma to design the relay’s compress-forward scheme. We then show that employing constant composition code ensembles does not improve the rates achieved with their IID counterparts. We also derive a sphere packing upper bound for the error exponent. In the second part of this paper, we establish a connection between the information bottleneck channel and the WAK problem. We show that good codes for the latter can be produced through permuting codes designed for the former. This is accomplished by revisiting Ahlswede’s covering lemma, and extending it to achieve simultaneous covering of a type class by several distinct sets using the same sequence of permutations. We then apply our approach to attain the best known achievable error exponent for the WAK problem, previously established by Kelly and Wagner. As a byproduct of our derivations, we also establish error exponents and achievable rates under mismatched decoding rules.

We study the information bottleneck (IB) source coding problem, also known as remote lossy source coding under logarithmic loss. Based on a rate-limited description of noisy observations, the receiver produces a soft estimate for the remote source, i.e., a probability distribution, evaluated under the logarithmic loss. We focus on the excess distortion probability of IB source coding and investigate how fast it converges to 0 or 1, depending on whether the rate is above or below the rate-distortion function. The latter case is also known as the exponential strong converse. We establish both the exact error exponent and the exact strong converse exponent for IB source coding by deriving matching upper and lower exponential bounds. The obtained exponents involve optimizations over auxiliary random variables. The matching converse bounds are derived through non-trivial extensions of existing sphere packing and single-letterization techniques, which we adapt to incorporate auxiliary random variables. In the second part of this paper, we establish a code-level connection between IB source coding and source coding with a helper, also known as the Wyner-Ahlswede-Körner (WAK) problem. We show that every code for the WAK problem is a code for IB source coding. This requires noticing that IB source coding, under the excess distortion criterion, is equivalent to source coding with a helper available atboththe transmitterandthe receiver; the latter in turn relates to the WAK problem. Through this connection, we re-derive the best known sphere packing exponent of the WAK problem, and provide it with an operational interpretation.

This paper considers guessing-based decoders with abandonment for discrete memoryless channels in which all codewords have the same composition. This class of decoders rank-orders all input sequences in the type class from “closest” to “farthest” from the channel output and then queries them sequentially in that order for codeword membership. Decoding stops when a codeword is encountered or when a predetermined number of guesses is reached and decoding is abandoned. Ensemble-tight first- and second-order asymptotics are derived for the code rate and abandonment rate. The optimal secondorder region is characterized in terms of the minimum of the second-order code and abandonment rates.

Past works on remote lossy source coding studied the rate under average distortion and the error exponent of excess distortion probability. In this work, we look into how fast the excess distortion probability converges to 1 at small rates, also known as exponential strong converse. We characterize its exponent by establishing matched upper and lower bounds. From the exponent, we also recover two previous results on lossy source coding and biometric authentication.

The capacity of a discrete-time channel with correlated phase noises is investigated. In particular, the electro-optic frequency comb system is considered, where the phase noise of each subchannel is a combination of two independent Wiener phase-noise sources. Capacity upper and lower bounds are derived for this channel and are compared with lower bounds obtained by numerically evaluating the achievable information rates using quadrature amplitude modulation constellations. Capacity upper and lower bounds are provided for the high signal-to-noise ratio (SNR) regime. The multiplexing gain (pre-log) is shown to beM− 1, whereMrepresents the number of subchannels. A constant gap between the asymptotic upper and lower bounds is observed, which depends on the number of subchannelsM. For the specific case ofM= 2, capacity is characterized up to a term that vanishes as the SNR grows large.

This paper considers guessing-based decoders with abandonment for discrete memoryless channels in which all codewords have the same composition. This class of decoders rank-orders all input sequences in the codebook’s composition class from “closest” to “farthest” from the channel output and then queries them sequentially in that order for codebook membership. Decoding terminates when a codeword is encountered or when a predetermined number of guesses is reached, and decoding is abandoned. We derive ensemble-tight first-order asymptotics for the code rate and abandonment rate, which shows that guessing-based decoding is more efficient than conventional testing-based decoding whenever the capacity of the channel exceeds half the entropy of the capacity-achieving input distribution. The main focus of this paper is on refined asymptotics, specifically, second-order asymptotics, error exponents, and strong converse exponents. The optimal second-order region is characterized in terms of the minimum of the second-order code and abandonment rates. The error (resp. strong converse) exponent is characterized in terms of the minimum (resp. maximum) of the usual channel coding exponent and an abandonment exponent, which turns out to be a special case of the exponent of conditional almost-lossless source coding.

We revisit guessing random additive noise decoding (GRAND) in discrete additive noise channels. We derive a non-asymptotic random coding bound using elementary tools, which is applicable to arbitrary noise guessing orders. We then use this bound to analyze a universal variant of GRAND, that does not require knowledge of the noise distribution, and show that it achieves the random coding error exponent. Finally, we apply GRAND to an instance of the Slepian-Wolf coding problem.

We derive an achievable error exponent for the information bottleneck channel. The exponent is expressed as a minimum of two terms: a compression error exponent due to the bottleneck, and a channel decoding error exponent. Achievability is established through treating the rate-limited noiseless link between the relay and the receiver asymptotically as a virtual discrete memoryless channel.

We introduce an adaptive, fully probabilistic pipeline for optimized signal subsampling in sampling-budget constrained systems. Our pipeline equips an agent with a deep generative model of its measurement-generating environment with which it infers posterior distributions over high-dimensional signals. This posterior distribution is subsequently used by the agent to adaptively select next samples that maximize the expected information gain. Experiments on the MNIST and fastMRI data sets show strong adaptability of selected sampling sequences to the signal modality, resulting in high-quality reconstructions for high acceleration factors. Performance is upper-bounded by the representation error of the used generative model, which is mainly evident at low acceleration factors.

We study a lossy variant of the Massy-Arikan guessing problem where instead of guessing the exact value of a discrete random variable, the goal is to guess a good soft reconstruction: a probability distribution under which the true realization has low uncertainty. The remaining uncertainty after guessing is measured through the logarithmic loss. We derive single-shot lower and upper bounds for the corresponding guessing moments. These bounds are exponentially tight in the asymptotic regime. Moreover, we establish a connection between our proposed soft guessing problem and the problem of variable-length lossy source coding under logarithmic loss.

We consider a basic communication and sensing setup comprising a transmitter, a receiver and a sensor. The transmitter sends an encoded sequence to the receiver through a discrete memoryless channel, and the receiver is interested in decoding the sequence. On the other hand, the sensor picks up a noisy version of the transmitted sequence through one of two possible discrete memoryless channels. The sensor knows the transmitted sequence and wishes to discriminate between the two possible channels, i.e. to identify the channel that has generated the output given the input. We study the trade-off between communication and sensing in the asymptotic regime, captured in terms of the coding rate to the receiver against the discrimination error exponent at the sensor. We characterize the optimal rate-exponent trade-off for general discrete memoryless channels with an input cost constraint.

The work focuses on optimizing coded caching under asynchronous demands. We consider a single-stream setting where users are allowed to request content at arbitrary time-slots. Aiming to minimize the total system delay required to serve all users, i.e. from the moment of the first request to the delivery of the last bit of requested information, we design a pair of placement and delivery algorithms and show that the achievable performance is within a multiplicative factor of 2 from the optimal, under the assumption of uncoded placement, and within a multiplicative factor of 4.02 in the general placement case. Interesting characteristics of our algorithms are that i) a placement phase agnostic to the users' arrival times is adequate to provide a near-optimal delay, and ii) the proposed delivery algorithm requires low complexity and, at the same time, requires no non-causal information. Further, we show that systems are able to withstand some degree of asynchronicity without an increase in the delay compared to an equivalent synchronous setting. Finally, we highlight an interesting connection between coded caching under asynchronous demands and coded caching in wireless environments under uneven channel strengths.

We consider a discrete memoryless cloud radio access network in which K users communicate with a remote destination through 2 relays in a cascade. The relays are oblivious in the sense that they operate without knowledge of the users’ codebooks. We focus on a scenario where the first and second relays are connected through a finite-capacity error-free link, while the second relay is connected to the remote destination via an infinite-capacity link. We establish the capacity region in this case, and show that it is achieved via a compress-and-forward scheme with successive decoding. Finally, the extension to Gaussian networks is discussed.

We study the generalized degrees-of-freedom (GDoF) of cellular networks under finite precision channel state information at the transmitters (CSIT). We consider downlink settings modeled by the interfering broadcast channel (IBC) under no multi-cell cooperation, and the overloaded multiple-input-single-output broadcast channel (MISO-BC) under full multi-cell cooperation. We focus on three regimes of interest: the mc-TIN regime, where a scheme based on treating inter-cell interference as noise (mc-TIN) was shown to be GDoF optimal for the IBC; the mc-CTIN regime, where the GDoF region achievable by mc-TIN is convex without the need for time-sharing; and the mc-SLS regime which extends a previously identified regime, where a simple layered superposition (SLS) scheme is optimal for the 3-transmitter-3-user MISO-BC, to overloaded cellular-type networks with more users than transmitters. We first show that the optimality of mc-TIN for the IBC extends to the entire mc-CTIN regime when CSIT is limited to finite precision. The converse proof of this result relies on a new application of aligned images bounds. We then extend the IBC converse proof to the counterpart overloaded MISO-BC, obtained by enabling full transmitter cooperation. This, in turn, is utilized to show that a multi-cell variant of the SLS scheme is optimal in the mc-SLS regime under full multi-cell cooperation, albeit only for 2-cell networks. The overwhelming combinatorial complexity of the GDoF region stands in the way of extending this result to larger networks. Alternatively, we appeal to extremal network analysis, recently introduced by Chan et al., and study the GDoF gain of multi-cell cooperation over mc-TIN in the three regimes of interest. We show that this extremal GDoF gain is bounded by small constants in the mc-TIN and mc-CTIN regimes, yet scales logarithmically with the number of cells in the mc-SLS regime.

We study the fundamental limits of multi-cell cooperation in downlink cellular networks under the assumption of finite precision channel state information at the transmitters (CSIT). By appealing to extremal network theory, recently introduced by Chan et al. [1], we characterize the extremal GDoF gain of multi-cell cooperation over non-cooperative multi-cell TIN in three weak inter-cell interference regimes: the mc-TIN regime, where multi-cell TIN is GDoF optimal under no cooperation; the mc-CTIN regime, where the GDoF region achieved through multi-cell TIN is convex without the need for time-sharing; and the mc-SLS regime, where a cooperative scheme based on simple layered superposition was shown to be optimal in small networks. We show that the extremal GDoF gain is bounded by a constant factor in the mc-TIN and mc-CTIN regimes, and scales logarithmically with the number of cells in the mc-SLS regime. The analysis is enabled by a new cooperative outer bound for cellular networks based on the aligned images approach.

In this work, we study the generalized degrees- of-freedom (GDoF) of downlink and uplink cellular networks, modeled as Gaussian interfering broadcast channels (IBC) and Gaussian interfering multiple access channels (IMAC), respectively. We focus on regimes of low inter-cell interference, where single-cell transmission with power control and treating inter-cell interference as noise (mc-TIN) is GDoF optimal. Recent works have identified two relevant regimes in this context: one in which the GDoF region achieved through mc-TIN for both the IBC and IMAC is a convex polyhedron without the need for time-sharing (mc-CTIN regime), and a smaller (sub)regime where mc-TIN is GDoF optimal for both the IBC and IMAC (mc-TIN regime). In this work, we extend the mc-TIN framework to cellular scenarios where channel state information at the transmitters (CSIT) is limited to finite precision. We show that in this case, the GDoF optimality of mc-TIN extends to the entire mc-CTIN regime, where GDoF benefits due to interference alignment (IA) are lost. Our result constitutes yet another successful application of robust outer bounds based on the aligned images (AI) approach.

We consider cache-aided wireless communication scenarios where each user requests both a file from an a-priori generated cacheable library (referred to as `content'), and an uncacheable `non-content' message generated at the start of the communication session. This scenario is easily found in real-world wireless networks, where the two types of traffic coexist and share limited radio resources. We focus our investigation on single-transmitter wireless networks with cache-aided receivers, where the wireless channel is modelled by a degraded Gaussian broadcast channel (GBC). For this setting, we study the (normalized) delay-rate trade-off, which characterizes the content delivery time and non-content communication rates that can be achieved simultaneously. We propose a scheme based on the separation principle, which isolates the coded caching problem from the physical layer transmission problem, and prove its information-theoretic order optimality up to a multiplicative factor of 2.01. A key insight emerging from our scheme is that substantial amounts of non-content traffic can be communicated while maintaining the minimum content delivery time, achieved in the absence of non-content messages; compliments of `topological holes' arising from asymmetries in wireless channel gains.

We study the optimality of power control and treating interference as noise (TIN) in the M × N X channel, from the generalized degrees-of-freedom (GDoF) and constant- gap capacity perspectives. A result by Geng, Sun and Jafar shows that if there exist K = min(M, N) transmitter-receiver pairs such that each direct link strength is no less than the sum of the strongest incoming and strongest outgoing cross link strengths (all in dB), then it is optimal to reduce the M × N X channel to a K-user interference channel and use TIN. The proof of this result relies on a deterministic approximation of the original Gaussian network, specifically for the case M <; N. Here we present a simpler proof by working directly with the original Gaussian network. Our proof relies on a new "less noisy under interference" order exhibited by TIN-optimal M × N X channels, akin to the "less noisy" order in broadcast channels.

We consider the problem of cache-aided interference management in a network consisting of KTsingle-antenna transmitters and KRsingle-antenna receivers, where each node is equipped with a cache memory. Transmitters communicate with receivers over two heterogenous parallel subchannels: the P-subchannel for which transmitters have perfect instantaneous knowledge of the channel state, and the N-subchannel for which the transmitters have no knowledge of the instantaneous channel state. Under the assumptions of uncoded placement and separable one-shot linear delivery over the two subchannels, we characterize the optimal degrees-of-freedom (DoF) to within a constant multiplicative factor of 2. We extend the result to a decentralized setting in which no coordination is required for content placement at the receivers. In this case, we characterize the optimal one-shot linear DoF to within a factor of 3.

We study the K-user, M-subchannel parallel multiple-input-single-output (MISO) broadcast channel (BC) under arbitrary levels of partial channel state information at the transmitter (CSIT). We show that the parallel subchannels constituting this setting are separable from a degrees-of-freedom (DoF) region perspective if and only if the partial CSIT pattern is totally ordered. This total order condition corresponds to users abiding by the same order, with respect to their CSIT quality levels, in each of the parallel subchannels. For instance, let αk[l]and αj[l]be the CSIT quality parameters for users k and j over subchannel l. Under total order, having αk[l]≥ αj[l]implies that αk[m]≥ αj[m]holds for every subchannel m. In this case, the entire DoF region is achievable using simple separate coding, where a single-subchannel-type transmission scheme is employed in each subchannel. To show this separability result, we first derive an outer bound for the DoF region by extending the aligned image sets approach of Davoodi and Jafar to the considered setting. We then show that this outer bound coincides with the inner bound achieved through separate coding, given by the Minkowski sum of M single-subchannel DoF regions, under the total order condition, hence settling the if part of the main theorem. To prove the only if part of the theorem, we identify a set of DoF tuples achievable through joint coding across subchannels, yet not achievable through separate coding whenever the total order condition is violated. Moreover, we also highlight the implications of our main result on the design of CSIT feedback schemes for multi-carrier multi-antenna wireless networks.

In[1], reference [26] was incorrect. Reference [26] should be as follows: E. Piovano and B. Clerckx, “Optimal DoF region of the K-user MISO BC with partial CSIT,”IEEE Commun. Lett., vol. 21, no. 11, pp. 2368–2371, Nov. 2017.

We consider the information-theoretic optimality of treating inter-cell interference as noise (multi-cell TIN) in downlink cellular networks. We focus on scenarios modeled by the Gaussian interfering broadcast channel (IBC), comprising K mutually interfering Gaussian broadcast channels (BCs), each formed by a base station communicating independent messages to an arbitrary number of users. We establish a new power allocation duality between the IBC and its dual interfering multiple access channel (IMAC), which entails that the corresponding generalized degrees-of-freedom regions achieved through multi-cell TIN and power control (TINA regions) for both networks are identical. As by-products of this duality, we obtain an explicit characterization of the IBC TINA region from a previously established characterization of the IMAC TINA region; and identify a multi-cell convex-TIN regime in which the IBC TINA region is a polyhedron (hence convex) without the need for time-sharing. We then identify a smaller multi-cell TIN regime in which the IBC TINA region is optimal and multi-cell TIN achieves the entire capacity region of the IBC, up to a constant gap. This is accomplished by deriving a new genie-aided outer bound for the IBC, that reveals a novel BC-type order that holds amongst users in each constituent BC (or cell) under inter-cell interference, which in turn is not implied by previously known BC-type orders (i.e. degraded, less noisy and more capable orders). The multi-cell TIN regime that we identify for the IBC coincides with a corresponding multi-cell TIN regime previously identified for the IMAC, hence establishing a comprehensive uplink-downlink duality of multi-cell TIN in the GDoF (and approximate capacity) sense.

We consider the information-theoretic optimality of treating inter-cell interference as noise in downlink cellular networks modeled as Gaussian interfering broadcast channels. Establishing a new uplink-downlink duality, we cast the problem in Gaussian interfering broadcast channels to that in Gaussian interfering multiple access channels, and characterize an achievable GDoF region under power control and treating inter-cell interference as (Gaussian) noise. We then identify conditions under which this achievable GDoF region is optimal.

In this paper, we explore the information-theoretic optimality of treating interference as noise (TIN) in cellular networks. We focus on uplink scenarios modeled by the Gaussian interfering multiple access channel (IMAC), comprising K mutually interfering multiple access channels (MACs), each formed by an arbitrary number of transmitters communicating independent messages to one receiver. We define TIN for this setting as a scheme in which each MAC (or cell) performs a power-controlled version of its capacity-achieving strategy, with Gaussian codebooks and successive decoding, while treating interference from all other MACs (i.e., inter-cell interference) as noise. We characterize the generalized degrees-of-freedom (GDoF) region achieved through the proposed TIN scheme, and then identify conditions under which this achievable region is convex without the need for time-sharing. We then tighten these convexity conditions and identify a regime in which the proposed TIN scheme achieves the entire GDoF region of the IMAC and is within a constant gap of the entire capacity region.

We consider the cache-aided MISO broadcast channel (BC) in which a multi-antenna transmitter serves K single-antenna receivers, each equipped with a cache memory. The transmitter has access to partial knowledge of the channel state information. For a symmetric setting, in terms of channel strength levels, partial channel knowledge levels and cache sizes, we characterize the generalized degrees of freedom (GDoF) up to a constant multiplicative factor. The achievability scheme exploits the interplay between spatial multiplexing gains and coded-multicasting gain. On the other hand, a cut-set-based argument in conjunction with a GDoF outer bound for a parallel MISO BC under channel uncertainty is used for the converse. We further show that the characterized order-optimal GDoF is also attained in a decentralized setting, where no coordination is required for content placement in the caches.

In this paper, we look at the problem of treating interference as noise (TIN) in the Gaussian interfering multiple access channel (IMAC). The considered network comprises K mutually interfering multiple access channels (MACs), each consisting of two transmitters communicating independent messages to one receiver. We define the TIN scheme for this channel as one in which each MAC performs a power controlled version of its capacity-achieving strategy while treating interference from all other MACs as noise. We characterize an achievable generalized degrees-of-freedom (GDoF) region under the TIN scheme and identify a regime of parameters (in terms of channel strength levels) where this region is optimal.

In this paper, we look at a wireless network consisting of K fully-cooperating transmitters serving K receivers, each equipped with a cache memory. Each node is equipped with a single antenna and transmitters have access to partial channel state information. For a symmetric setting, we characterize the generalized degrees of freedom (GDoF) up to a constant multiplicative factor. We further show that the characterized order-optimal GDoF is also attained in a decentralized setting, with no coordination during the cache content placement phase.

Simultaneous Wireless Information and Power Transfer (SWIPT) is subject to nonlinearity at the energy harvester that leads to significant changes to transmit signal designs compared to conventional wireless communications. In this paper, the capacity of a discrete time, memoryless and complex Additive White Gaussian Noise (AWGN) channel in the presence of a nonlinear energy harvester at the receiver is studied. Considering the two common nonlinear energy harvester models introduced in the literature, two sets of constraints are considered. First the capacity is studied under average power (AP), peak amplitude (PA) and receiver delivery power (RDP) constraints. The RDP constraint is modelled as a linear combination of even-moment statistics of the channel input being larger than a threshold. It is shown that the capacity of an AWGN channel under AP and RDP constraints is the same as the capacity of an AWGN channel under an AP constraint, however, depending on the two constraints, it can be either achieved or arbitrarily approached. It is also shown that under AP, PA and RDP constraints, the amplitude of the optimal inputs is discrete with a finite number of mass points. Next, the capacity is studied under AP, PA and output outage probability (OOP) constraints. OOP is modelled as satisfying a certain probability inequality for the amplitude of the received signal being outside of a given interval. Similarly, it is shown that the amplitude of the optimal input is discrete with a finite number of mass points.

We study the degrees of freedom (DoF) of a K-user parallel MISO broadcast channel with arbitrary levels of partial CSIT over each subchannel. We derive a sum-DoF upperbound which depends on the average CSIT quality of each user. This upperbound is shown to be tight under total order, i.e. when the order of users with respect to their CSIT qualities is preserved over all subchannels. In this case, it is shown that separate coding over each subchannel is optimum in a sum-DoF sense.

This work investigates the interplay of coded caching and spatial multiplexing in an overloaded Multiple-Input-Single-Output (MISO) Broadcast Channel (BC), i.e. a system where the number of users is greater than the number of transmitting antennas. On one hand, coded caching uses the aggregate global cache memory of the users to create multicasting opportunities. On the other hand, multiple antennas at the transmitter leverage the available CSIT to transmit multiple streams simultaneously. In this paper, we introduce a novel scheme which combines both the gain derived from coded-caching and spatial multiplexing and outperforms existing schemes in terms of delivery time and CSIT requirement.

In this paper, we consider the problem of achieving max-min fairness amongst multiple co-channel multicast groups through transmit beamforming. We explicitly focus on overloaded scenarios in which the number of transmitting antennas is insufficient to neutralize all inter-group interference. Such scenarios are becoming increasingly relevant in the light of growing low-latency content delivery demands, and also commonly appear in multibeam satellite systems. We derive performance limits of classical beamforming strategies using degrees of freedom (DoF) analysis unveiling their limitations; for example, rates saturate in overloaded scenarios due to inter-group interference. To tackle interference, we propose a strategy based on degraded beamforming and successive interference cancellation. While the degraded strategy resolves the rate-saturation issue, this comes at a price of sacrificing all spatial multiplexing gains. This motivates the development of a unifying strategy that combines the benefits of the two previous strategies. We propose a beamforming strategy based on rate-splitting (RS), which divides the messages intended to each group into a degraded part and a designated part, and transmits a superposition of both degraded and designated beamformed streams. The superiority of the proposed strategy is demonstrated through DoF analysis. Finally, we solve the RS beamforming design problem and demonstrate significant performance gains through simulations.

For multiuser MISO systems with bounded uncertainties in the Channel State Information (CSI), we consider two classical robust design problems: maximizing the minimum rate subject to a transmit power constraint, and power minimization under a rate constraint. Contrary to conventional strategies, we propose a Rate-Splitting (RS) strategy where each message is divided into two parts, a common part and a private part. All common parts are packed into one super common message encoded using a shared codebook and decoded by all users, while private parts are independently encoded and retrieved by their corresponding users. We prove that RS-based designs achieve higher max-min Degrees of Freedom (DoF) compared to conventional designs (NoRS) for uncertainty regions that scale with SNR. For the special case of non-scaling uncertainty regions, RS contrasts with NoRS and achieves a non-saturating max-min rate. In the power minimization problem, RS is shown to combat the feasibility problem arising from multiuser interference in NoRS. A robust design of precoders for RS is proposed, and performance gains over NoRS are demonstrated through simulations.

This paper considers the sum-rate (SR) maximization problem in downlink multi-user multiple input simgle output (MU-MISO) systems under imperfect channel state information at the transmitter (CSIT). Contrary to existing works, we consider a rather unorthodox transmission scheme. In particular, the message intended to one of the users is split into two parts: a common part which can be recovered by all users, and a private part recovered by the corresponding user. On the other hand, the rest of users receive their information through private messages. This rate-splitting (RS) approach was shown to boost the achievable degrees of freedom when CSIT errors decay with increased SNR. In this paper, the RS strategy is married with linear precoder design and optimization techniques to achieve a maximized ergodic SR (ESR) performance over the entire range of SNRs. Precoders are designed based on partial CSIT knowledge by solving a stochastic rate optimization problem using means of sample average approximation coupled with the weighted minimum mean square error approach. Numerical results show that in addition to the ESR gains, the benefits of RS also include relaxed CSIT quality requirements and enhanced achievable rate regions compared with conventional transmission with no rate-splitting.

In this paper, we consider a MU-MISO system where users have highly accurate Channel State Information (CSI), while the Base Station (BS) has partial CSI consisting of an imperfect channel estimate and statistical knowledge of the CSI error. With the objective of maximizing the Average Sum Rate (ASR) subject to a power constraint, a special transmission scheme is considered where the BS transmits a common symbol in a multicast fashion, in addition to the conventional private symbols. This scheme is termed Joint Multicasting and Broadcasting (JMB). The ASR problem is transformed into an augmented Average Weighted Sum Mean Square Error (AWSMSE) problem which is solved using Alternating Optimization (AO). The enhanced rate performance accompanied with the incorporation of the multicast part is demonstrated through simulations.

We address the max-min fairness design problem for a MU-MISO system with partial Channel State Information (CSI) at the Base Station (BS), consisting of an imperfect channel estimate and statistical knowledge of the estimation error, and perfect CSI at the receivers. The objective is to maximize the minimum Average Rate (AR) among users subject to a transmit power constraint. An unconventional transmission scheme is adopted where the Base Station (BS) transmits a common message in addition to the conventional private messages. In situations where the CSIT is not accurate enough to perform interference nulling, individual rates are assisted by allocating parts of the common message to different users according to their needs. The AR problem is transformed into an augmented AverageWeighted Mean Square Error (AWMSE) problem, solved using Alternating Optimization (AO). The benefits of incorporating the common message are demonstrated through simulations.

In this paper, we consider the design of robust linear precoders for MU-MISO systems where users have perfect Channel State Information (CSI) while the BS has partial CSI. In particular, the BS has access to imperfect estimates of the channel vectors, in addition to the covariance matrices of the estimation error vectors. A closed-form expression for the Average Minimum Mean Square Error (AMMSE) is obtained using the second order Taylor Expansion. This approximation is used to formulate two fairness-based robust design problems: a maximum AMMSE-constrained problem and a power-constrained problem. We propose an algorithm based on convex optimization techniques to address the first problem, while the second problem is tackled by exploiting the close relationship between the two problems, in addition to their monotonic natures.

Recently, Multiflow transmission has been standardized as part of Multipoint High-Speed Downlink Packet Access (HSDPA). Multiflow schemes enable cell-edge HSDPA users to receive simultaneous data streams from multiple serving cells leading to improved user experience. The standard considers a single transmit antenna for each serving cell in a distributed Per Antenna Rate Control (PARC) manner. This paper investigates the potential gains of utilizing an extra antenna at each cell and hence enabling Beamforming. It is demonstrated that under the proposed transmission scheme, interference aware equalizers can be used without requiring knowledge about the Beamforming antenna weights of interfering cells. Furthermore, it is shown that combining transmit Beamforming with Successive Interference Cancellation (SIC) at the receiver could significantly improve the performance of Multiflow schemes.