In this work, we take the initiative in studying the information-theoretic tradeoff between communication and quickest change detection (QCD) under an integrated sensing and communication setting. We formally establish a joint communication and sensing problem for the quickest change detection. We assume a broadcast channel with a transmitter, a communication receiver, and a QCD detector in which only the detection channel is state dependent. For the problem setting, by utilizing constant subblock-composition codes and a modified CuSum detection rule, which we call subblock CuSum (SCS), we provide an inner bound on the information-theoretic tradeoff between communication rate and change point detection delay in the asymptotic regime of vanishing false alarm rate. We further provide a partial converse that matches our inner bound for a certain class of codes. This implies that the SCS detection strategy is asymptotically optimal for our codes as the false alarm rate constraint vanishes. We also present some canonical examples of the tradeoff region for a binary channel, a scalar Gaussian channel, and a MIMO Gaussian channel.

This work considers a problem of integrated sensing and communication (ISAC) in which the goal of sensing is to detect a binary state. Unlike most approaches that minimize the total detection error probability, in our work, we disaggregate the error probability into false alarm and missed detection probabilities and investigate their information-theoretic three-way tradeoff including communication data rate. We consider a broadcast channel that consists of a transmitter, a communication receiver, and a detector where the receiver’s and the detector’s channels are affected by an unknown binary state. We consider and present results on two different state-dependent models. In the first setting, the state is fixed throughout the entire transmission, for which we fully characterize the optimal three-way tradeoff between the coding rate for communication and the two possibly nonidentical error exponents for sensing in the asymptotic regime. The achievability and converse proofs rely on the analysis of the cumulant-generating function of the log-likelihood ratio. In the second setting, the state changes every symbol in an independently and identically distributed (i.i.d.) manner, for which we characterize the optimal tradeoff region based on the analysis of the receiver operating characteristic (ROC) curves.

In this study, we investigate the tradeoff between communication rate and quickest change detection (QCD) in integrated sensing and communication. We establish a joint problem for QCD, utilizing constant subblock-composition codes and a modified CuSum detection rule, named subblock CuSum (SCS). This approach provides an inner bound for the tradeoff between communication rate and change point detection delay in low false alarm scenarios. Our findings show that SCS is asymptotically optimal for certain code classes.

This paper considers the problem of distributed lossy compression where the goal is to recover one or more linear combinations of the sources at the decoder, subject to distortion constraints. For certain configurations, it is known that codes with algebraic structure can outperform i.i.d. codebooks. For the special case of finite-alphabet sources, recent work has demonstrated how to incorporate joint typicality decoding alongside linear encoding and binning. This work takes a discretization approach to extend this rate region to include both integer- and real-valued sources. As a case study, the rate region is evaluated for the Gaussian case. The resulting joint-typicality-based rate region recovers and generalizes the best-known rate region for this scenario, based on lattice encoding and sequential decoding.

In many sensor network applications, a fusion center often has additional valuable information, such as context data, which cannot be obtained directly from the sensors. Motivated by this, we study a generalized CEO problem where a CEO has access to context information. The main contribution of this work is twofold. Firstly, we characterize the asymptotically optimal error exponent per rate as the number of sensors and sum rate grow without bound. The proof extends the Berger-Tung coding scheme and the converse argument by Berger et al. (1996) taking into account context information. The resulting expression includes the minimum Chernoff divergence over context information. Secondly, assuming that the sizes of the source and context alphabets are respectively$|\mathcal {X}|$and$|\mathcal {S}|$, we prove that it is asymptotically optimal to partition all sensors into at most$\binom {|\mathcal {X}|}{2} |\mathcal {S}|$groups and have the sensors in each group adopt the same encoding scheme. Our problem subsumes the original CEO problem by Berger et al. (1996) as a special case if there is only one letter for context information; in this case, our result tightens its required number of groups from$\binom {|\mathcal {X}|}{2}+2$to$\binom {|\mathcal {X}|}{2}$. We also numerically demonstrate the effect of context information for a simple Gaussian scenario.

Compute–forward is a coding technique that enables receiver(s) in a network to directly decode one or more linear combinations of the transmitted codewords. Initial efforts focused on Gaussian channels and derived achievable rate regions via nested lattice codes and single-user (lattice) decoding as well as sequential (lattice) decoding. Recently, these results have been generalized to discrete memoryless channels via nested linear codes and joint typicality coding, culminating in a simultaneous-decoding rate region for recovering one or more linear combinations from$K$users. Using a discretization approach, this paper translates this result into a simultaneous-decoding rate region for a wide class of continuous memoryless channels, including the important special case of Gaussian channels. Additionally, this paper derives a single, unified expression for both discrete and continuous rate regions via an algebraic generalization of Rényi’s information dimension.

We present a novel unified framework of compute-forward achievable rate regions for simultaneous decoding of multiple linear codeword combinations. This framework covers a wide class of discrete and continuous-input channels, and computation over finite fields, integers, and reals. The resulting rate regions recover several well-known achievability results, and in some cases extend them. The framework is built upon a recently established achievable rate region based on linear codes and joint typicality decoding. The latter is extended from finite fields to computation over the integers and, via a discretization approach, to computation over the reals with integer coefficients and continuous inputs. Evaluating the latter with Gaussian distributions, we obtain a closed-form rate region which generalizes the classic compute-forward rates originally derived by means of lattice codes by Nazer and Gastpar.

In this paper, we consider the problem of handover decision making in the context of a dense heterogeneous network with a macro base station and multiple small base stations. We propose a distributed deep Q-learning based algorithm that minimizes the overall energy consumption by taking into account both the energy consumption from transmission and hand over overheads. The proposed algorithm is performed in a distributed and interactive manner in which a centralized training agent manages the replay buffer for training its deep Q-network, by gathering state, action, and reward information reported from distributed handover agents. We perform several numerical evaluations and demonstrate that the proposed algorithm provides 10% to 30% energy savings over other contemporary handover mechanisms depending on handover overhead costs.

In this paper, we consider a joint beam tracking and pattern optimization problem for massive multiple input multiple output (MIMO) systems in which the base station (BS) selects a beamforming codebook and performs adaptive beam tracking taking into account the user mobility. A joint adaptation scheme is developed in a two-phase reinforcement learning framework which utilizes practical signaling and feedback information. In particular, an inner agent adjusts the transmission beam index for a given beamforming codebook based on short-term instantaneous signal-to-noise ratio (SNR) rewards. In addition, an outer agent selects the beamforming codebook based on long-term SNR rewards. Simulation results demonstrate that the proposed online learning outperforms conventional codebook-based beamforming schemes using the same number of feedback information. It is further shown that joint beam tracking and beam pattern adaptation provides a significant SNR gain compared to the beam tracking only schemes, especially as the user mobility increases.

Algebraic network information theory is an emerging facet of network information theory, studying the achievable rates of random code ensembles that have algebraic structure, such as random linear codes. A distinguishing feature is that linear combinations of codewords can sometimes be decoded more efficiently than codewords themselves. The present work further develops this framework by studying the simultaneous decoding of multiple messages. Specifically, consider a receiver in a multi-user network that wishes to decode several messages. Simultaneous joint typicality decoding is one of the most powerful techniques for determining the fundamental limits at which reliable decoding is possible. This technique has historically been used in conjunction with random i.i.d. codebooks to establish achievable rate regions for networks. Recently, it has been shown that, in certain scenarios, nested linear codebooks in conjunction with “single-user” or sequential decoding can yield better achievable rates. For instance, the compute-forward problem examines the scenario of recovering L ≤ K linear combinations of transmitted codewords over a K-user multiple-access channel (MAC), and it is well established that linear codebooks can yield higher rates. This paper develops bounds for simultaneous joint typicality decoding used in conjunction with nested linear codebooks, and applies them to obtain a larger achievable region for compute-forward over a K-user discrete memoryless MAC. The key technical challenge is that competing codeword tuples that are linearly dependent on the true codeword tuple introduce statistical dependencies, which requires careful partitioning of the associated error events.

The problem of computing a linear combination of sources over a multiple access channel is studied. Inner and outer bounds on the optimal tradeoff between the communication rates are established when encoding is restricted to random ensembles of homologous codes, namely, structured nested coset codes from the same generator matrix and individual shaping functions, but when decoding is optimized with respect to the realization of the encoders. For the special case in which the desired linear combination is “matched” to the structure of the multiple access channel in a natural sense, these inner and outer bounds coincide. This result indicates that most, if not all, coding schemes for computation in the literature that rely on random construction of nested coset codes cannot be improved by using more powerful decoders such as the maximum likelihood decoder. The proof techniques are adapted to characterize the rate region for broadcast channels achieved by Marton's (random) coding scheme under maximum likelihood decoding. By generalizing some of the techniques, a single-letter outer bound for the capacity region of the computation problem is presented and compared with the inner bound achieved by homologous codes.

Consider the important special case of the K-user distributed source coding problem where the decoder only wishes to recover one or more linear combinations of the sources. The work of Körner and Marton demonstrated that, in some cases, the optimal rate region is attained by random linear codes, and strictly improves upon the best-known achievable rate region established via random i.i.d. codes. Recent efforts have sought to develop a framework for characterizing the achievable rate region for nested linear codes via joint typicality encoding and decoding. Here, we make further progress along this direction by proposing an achievable rate region for simultaneous joint typicality decoding of nested linear codes. Our approach generalizes the results of Körner and Marton to computing an arbitrary number of linear combinations and to the lossy computation setting.

We present a practical strategy that aims to attain rate points on the dominant face of the multiple access channel capacity using a standard low complexity decoder. This technique is built upon recent theoretical developments of Zhu and Gastpar on compute-forward multiple access which achieves the capacity of the multiple access channel using a sequential decoder. We illustrate this strategy with off-the-shelf LDPC codes. In the first stage of decoding, the receiver first recovers a linear combination of the transmitted codewords using the sum-product algorithm (SPA). In the second stage, by using the recovered sum-of-codewords as side information, the receiver recovers one of the two codewords using a modified SPA, ultimately recovering both codewords. The main benefit of recovering the sum-of-codewords instead of the codeword itself is that it allows to attain points on the dominant face of the multiple access channel capacity without the need of rate-splitting or time sharing while maintaining a low complexity in the order of a standard point-to-point decoder. This property is also shown to be crucial for some applications, e.g., interference channels. For all the simulations with single-layer binary codes, our proposed practical strategy is shown to be within 1.7 dB of the theoretical limits, without explicit optimization on the off-the-self LDPC codes.

Computation codes in network information theory are designed for scenarios where the decoder is not interested in recovering the information sources themselves, but only a function thereof. Körner and Marton showed for distributed source coding (DSC) that such function decoding can be achieved more efficiently than decoding the full information sources. Compute-forward has shown that function decoding, in combination with network coding ideas, is a useful building block for end-to-end communication over a network. In both cases, good computation codes are the key component in the coding schemes. Could these same codes simultaneously also enable full message decoding over a sufficiently strong multiple-access channel (MAC)? This work establishes a partial negative answer and converse result. Specifically, for any code that is known to be a good computation code for some MAC, we characterize a class of MACs for which that code cannot enable full message decoding (and vice versa). Finally, an analogous duality result is established for a related DSC problem.

Recent studies by Padakandla and Pradhan, and by Lim, Feng, Pastore, Nazer, and Gastpar built the framework of nested coset codes for the computation problem, namely, computing a desired linear combination of sources over a multiple access channel. This paper presents an outer bound on the optimal rate region for the computation problem when the encoding strategy is restricted to random ensembles of homologous codes, namely, structured nested coset codes from the same generator matrix and individual shaping functions based on joint typicality encoding. The optimal rate region is characterized when the desired linear combination and the channel structure are matched. Under this condition, a suboptimal joint typicality decoding rule is shown to achieve the optimal rate region. This result implies that the performance of random homologous code ensembles cannot be improved by using the optimal maximum likelihood decoder for the aforementioned class of computation problems.

We study a simultaneous wireless information and power transfer (SWIPT) setup for the K-user multiple-input single-output (MISO) interference channel. Each transmitter can either send a private or a common message, and each receiver uses a power splitting method that divides the received signal into two parts for information decoding and energy harvesting. The private message is recovered by the destination receiver only while the common message is recovered by all the receivers. While satisfying individual rate and energy harvesting constraints, our goal is to minimize the total transmit power by properly selecting the message types, designing the beamforming vectors at transmitters, and adjusting the power splitting parameters at receivers. To optimize the parameters of our proposed scheme, we first formulate the problem in terms of a semidefinite programming (SDP) and further relax the formulation to propose low complexity algorithms for numerical optimization. In numerical results, it is shown that the required transmit power of the proposed scheme is much lower than that of the conventional scheme based on transmitting private messages only, especially when the required harvesting energy is high or the channel gains of the cross-links are strong.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) for Internet of Things (IoT) sensor networks. The transmitters (e.g., access point) employ hybrid beamforming and each IoT receiver adopts a power splitting (PS) method that divides the received signal into two parts for information recovery and energy harvesting. We propose a novel strategy for SWIPT in which the transmitters have the option to either send a private or a common message. The private message is recovered only by a designated IoT receiver while common messages are recovered by all the receivers. While requiring the receivers to recover a common message results in additional rate constraints, the overall system performance benefits by mitigating interference. We propose SWIPT schemes that minimize the total transmit power by properly selecting message configurations, designing hybrid beamforming vectors, and adjusting the PS ratio at the receivers to satisfy the individual rate and energy harvesting constraints. In particular, we develop tractable and efficient twostage algorithms that, in the first stage determine the message configurations and in the second stage find the beamforming vectors (for both analog and digital components). Numerical simulations demonstrate that the proposed schemes significantly outperform conventional schemes that transmit private messages only using digital beamforming.

A large-scale content-centric mobile ad hoc network employing subpacketization is studied in which each mobile node having finite-size cache moves according to the reshuffling mobility model and requests a content object from the library independently at random according to the Zipf popularity distribution. Instead of assuming that one content object is transferred in a single time slot, we consider a more challenging scenario where the size of each content object is considerably large and thus only a subpacket of a file can be delivered during one time slot, which is motivated by a fast mobility scenario. Under our mobility model, we consider a single-hop-based content delivery and characterize the fundamental tradeoffs between throughput and delay. The order-optimal throughput-delay tradeoff is analyzed by presenting the following two content reception strategies: the sequential reception for uncoded caching and the random reception for maximum distance separable (MDS)-coded caching. We also perform numerical evaluation to validate our analytical results. In particular, we conduct performance comparisons between the uncoded caching and the MDS-coded caching strategies by identifying the regimes in which the performance difference between the two caching strategies becomes prominent with respect to system parameters such as the Zipf exponent and the number of subpackets. In addition, we extend our study to the random walk mobility scenario and show that our main results are essentially the same as those in the reshuffling mobility model.

This paper presents a joint typicality framework for encoding and decoding nested linear codes in multi-user networks. This framework provides a new perspective on compute-forward within the context of discrete memoryless networks. In particular, it establishes an achievable rate region for computing a linear combination over a discrete memoryless multiple-access channel (MAC). When specialized to the Gaussian MAC, this rate region recovers and improves upon the lattice-based compute-forward rate region of Nazer and Gastpar, thus providing a unified approach for discrete memoryless and Gaussian networks. Furthermore, our framework provides some valuable insights on establishing the optimal decoding rate region for compute-forward by considering joint decoders, progressing beyond most previous works that consider successive cancellation decoding. Specifically, this paper establishes an achievable rate region for simultaneously decoding two linear combinations of nested linear codewords from K senders.

We study Please be advised that per instructions from the Communications Society this proof was formatted in Times Roman font and therefore some of the fonts will appear different from the fonts in your originally submitted manuscript. For instance, the math calligraphy font may appear different due to usage of the usepackage[mathcal]euscript. The Communications Society has decided not to use Computer Modern fonts in their publications. the degrees of freedom (DoF) of cellular networks in which a full duplex (FD) base station (BS) equipped with multiple transmit and receive antennas communicates with multiple mobile users. We consider two different scenarios. In the first scenario, we study the case when half duplex (HD) users, partitioned to either the uplink (UL) set or the downlink (DL) set, simultaneously communicate with the FD BS. In the second scenario, we study the case when FD users simultaneously communicate UL and DL data with the FD BS. Unlike conventional HD only systems, inter-user interference (within the cell) may severely limit the DoF, and must be carefully taken into account. With the goal of providing theoretical guidelines for designing such FD systems, we completely characterize the sum DoFs for both FD cellular networks. The key idea of the proposed scheme is to carefully allocate UL and DL streams using interference alignment and beam forming techniques. By comparing the DoFs of the FD systems with those of the conventional HD systems, we show that the DoF can approach the two-fold gain over the HD systems, when the number of users becomes large enough compared with the number of antennas at the BS.

Recent work has employed joint typicality encoding and decoding of nested linear code ensembles to generalize the compute-forward strategy to discrete memoryless multiple-access channels (MACs). An appealing feature of these nested linear code ensembles is that the coding strategies and error probability bounds are conceptually similar to classical techniques for random i.i.d. code ensembles. In this paper, we consider the problem of recovering K linearly independent combinations over a K-user MAC, i.e., recovering the messages in their entirety via nested linear codes. While the MAC rate region is well-understood for random i.i.d. code ensembles, new techniques are needed to handle the statistical dependencies between competing codeword K-tuples that occur in nested linear code ensembles.

Inspired by the compute-and-forward scheme from Nazer and Gastpar, a novel multiple-access scheme introduced by Zhu and Gastpar makes use of nested lattice codes and sequential decoding of linear combinations of codewords to recover the individual messages. This strategy, coined compute-forward multiple access (CFMA), provably achieves points on the dominant face of the multiple-access capacity region while circumventing the need of time sharing or rate splitting. For a two-user multiple-access channel (MAC), we propose a practical procedure to design suitable codes from off-the-shelf LDPC codes and present a sequential belief propagation decoder with complexity comparable with that of point-to-point decoders. We demonstrate the potential of our strategy by comparing several numerical evaluations with theoretical limits.

A new coding scheme for general N -node relay networks is presented for unicast, multicast, and broadcast. The proposed distributed decode-forward scheme combines and generalizes Marton coding for single-hop broadcast channels and the Cover-El Gamal partial decode-forward coding scheme for three-node relay channels. The key idea of the scheme is to precode all the codewords of the entire network at the source by multicoding over multiple blocks. This encoding step allows these codewords to carry partial information of the messages implicitly without complicated rate splitting and routing. This partial information is then recovered at the relay nodes and forwarded further. For N-node Gaussian unicast, multicast, and broadcast relay networks, the scheme achieves within 0.5N bits from the cutset bound, and thus from the capacity (region), regardless of the network topology, channel gains, or power constraints. Roughly speaking, distributed decode-forward is dual to noisy network coding, which generalized compress-forward to unicast, multicast, and multiple access relay networks.

In this paper, we consider a cache aided network in which each user is assumed to have individual caches, while upon users' requests, an update message is sent through a common link to all users. First, we formulate a general information theoretic setting that represents the database as a discrete memoryless source, and the users' requests as side information that is available everywhere except at the cache encoder. The decoders' objective is to recover a function of the source and the side information. By viewing cache aided networks in terms of a general distributed source coding problem and through information theoretic arguments, we present inner and outer bounds on the fundamental tradeoff of cache memory size and update rate. Then, we specialize our general inner and outer bounds to a specific model of content delivery networks: file selection networks, in which the database is a collection of independent equal-size files and each user requests one of the files independently. For file selection networks, we provide an outer bound and two inner bounds (for centralized and decentralized caching strategies). For the case when the user request information is uniformly distributed, we characterize the rate versus cache size tradeoff to within a multiplicative gap of 4. By further extending our arguments to the framework of Maddah-Ali and Niesen, we also establish a new outer bound and two new inner bounds in which it is shown to recover the centralized and decentralized strategies, previously established by Maddah-Ali and Niesen. Finally, in terms of rate versus cache size tradeoff, we improve the previous multiplicative gap of 72 to 4.7 for the average case with uniform requests.

In this paper, we present information theoretic inner and outer bounds on the fundamental tradeoff between cache memory size and update rate in a multi-user cache network. Each user is assumed to have individual caches, while upon users' requests, an update message is sent though a common link to all users. The database is represented as a discrete memoryless source and the user request information is represented as side information that is available at the decoders and the update encoder, but oblivious to the cache encoder. We establish two inner bounds, the first based on a centralized caching strategy and the second based on a decentralized caching strategy. For the case when the user requests are i.i.d. with the uniform distribution, we show that the performance of the decentralized inner bound is within a multiplicative gap of 4 from the optimal cache-rate tradeoff. For general request distributions, we numerically compare the bounds and the baseline uncoded strategy, caching the most popular files.

This paper studies the degrees of freedom (DoFs) of full-duplex (FD) multicell networks that share the spectrum among multiple cells. In the considered network, we assume that FD base stations (BSs) with multiple transmit and receive antennas communicate with multiple single-antenna uplink (UL) and downlink (DL) mobile users. By spectrum sharing among multiple cells, and with FD radio, the network can utilize the spectrum more efficiently. However, since spectrum sharing and FD induce additional inter-cell and intra-cell interferences, interference management is crucial to take advantage of these features. In this paper, we propose novel interference management strategies which take into account the new sources of interferences caused by spectrum sharing and FD to establish a general achievability result on the sum DoFs. The key ideas in our proposed scheme are to minimize the dimension of UL inter-cell and user-to-user interferences using interference alignment at the UL users. On the BS side, we propose an interference management strategy to align or null out DL intra-cell and inter-cell interferences, and BS-to-BS interferences. We further establish an upper bound on the sum DoFs and show that our lower and upper bounds match under certain conditions. Several numerical evaluations are shown which demonstrate that spectrum sharing and FD can significantly improve the throughput over conventional cellular networks, especially for a network with large number of users and/or cells.

Under the paradigm of caching, partial data are delivered before the actual requests of users are known. In this paper, this problem is modeled as a canonical distributed source coding problem with side information, where the side information represents the users' requests. For the single-user case, a singleletter characterization of the optimal rate region is established, and for several important special cases, closed-form solutions are given, including the scenario of uniformly distributed user requests. In this case, it is shown that the optimal caching strategy is closely related to total correlation and Wyner's common information. Using the insight gained from the single-user case, three two-user scenarios admitting single-letter characterization are considered, which draw connections to existing source coding problems in the literature: the Gray-Wyner system and distributed successive refinement. Finally, the model studied by Maddah-Ali and Niesen is rephrased to make a comparison with the considered information-theoretic model. Although the two caching models have a similar behavior for the single-user case, it is shown through a two-user example that the two caching models behave differently in general.

We study the degrees of freedom (DoF) of cellular networks in which a full duplex (FD) base station (BS) equipped with multiple transmit and receive antennas communicates with multiple mobile users. We consider two different scenarios. In the first scenario, we study the case when half duplex (HD) users, partitioned to either the uplink (UL) set or the downlink (DL) set, simultaneously communicate with the FD BS. In the second scenario, we study the case when FD users simultaneously communicate UL and DL data with the FD BS. For both network models, we completely characterize the sum DoFs by developing achievable schemes and obtaining matching upper bounds. The key idea of the proposed scheme is to carefully allocate UL and DL information streams using interference alignment and beamforming techniques. As a consequence of the result, we show that the DoF can approach the two-fold gain over the HD systems when the number of users becomes large enough as compared to the number of antennas at the BS.

Motivated by the caching problem introduced by Maddah-Ali and Niesen, a problem of distributed source coding with side information is formulated, which captures a distinct interesting aspect of caching. For the single-user case, a single-letter characterization of the optimal rate region is presented. For the cases where the source is composed of either independent or nested components, the exact optimal rate regions are found and some intuitive caching strategies are confirmed to be optimal. When the components are arbitrarily correlated with uniform requests, the optimal caching strategy is found to be closely related to total correlation and Wyner's common information. For the two-user case, some subproblems are solved which draw connections to the Gray-Wyner system and distributed successive refinement. Finally, inner and outer bounds are given for the case of two private caches with a common update.

Hybrid analog-digital coding has been used for several communication scenarios, such as joint source-channel coding of Gaussian sources over Gaussian channels and relay communication over Gaussian networks. In this paper, a generalized hybrid coding technique is proposed for communication over discrete memoryless and Gaussian systems, and its utility is demonstrated via three examples-lossy joint source-channel coding over multiple access channels, channel coding over two-way relay channels, and channel coding over diamond networks. The corresponding coding schemes recover and extend several existing results in the literature.

The parallel relay network is studied, in which a single source node sends a message to a single destination node with the help of N parallel relays. Channel coefficients are assumed to vary over time and channel state information (CSI) is causally available only at the receiver side (CSIR). Opportunistic noisy network coding is proposed for intelligently exploiting CSIR at each relay in a distributed manner by operating the noisy network coding scheme with adaptive compression. More specifically, each relay opportunistically vector-quantizes the collection of received symbols that is received with channel gains larger than a certain threshold. It then forwards the digital compression information to the destination node using independently generated Gaussian codes. For independent and identically distributed (i.i.d.) Rayleigh fading, the proposed scheme is shown to achieve the ergodic capacity in the large number of relays regime. Furthermore, the proposed scheme is extensively compared with several alternative schemes, the decode-forward scheme, the adaptive amplify-forward scheme, and the non-adaptive noisy network coding scheme over geometric models. We show that the new proposed scheme provides significant gain over these schemes in various cases.

In this paper, we study the degrees of freedom (DoF) of cellular networks in which a base station with full-duplex operation simultaneously communicates to multiple half-duplex mobile stations. The half-duplex mobile stations are assumed to either communicate with the base station in the uplink or downlink. For such networks, inter-terminal interference needs to be taken into account to achieve the potential gain that doubles the throughput of conventional half-duplex cellular networks. We provide an achievable total DoF for such networks and show that having full-duplex operation at the base station only can indeed double the DoF when the number of mobile stations is sufficiently greater than the number of antennas at the base station. The key idea is to utilize the full DoF for the uplink transmission, while minimizing the inter-terminal interference via interference alignment. The downlink communication rate is then carefully chosen to occupy the remaining DoF at the receiving mobile stations.

A new coding scheme for multicasting a message over a general relay network is presented that extends both network coding for graphical networks by Ahlswede, Cai, Li, and Yeung, and partial decode-forward for relay channels by Cover and El Gamal. For the N-node Gaussian multicast network, the scheme achieves within 0.5N bits from the capacity, improving upon the best known capacity gap results. The key idea is to use multicoding at the source as in Marton coding for broadcast channels. Instead of recovering a specific part of the message as in the original partial decode-forward scheme, a relay in the proposed distributed decode-forward scheme recovers an auxiliary index that implicitly carries some information about the message and forwards it in block Markov coding. This scheme can be adapted to broadcasting multiple messages over a general relay network, extending and refining a recent result by Kannan, Raja, and Viswanath.

A new coding scheme for broadcasting multiple messages over a general relay network is presented. The proposed distributed decode-forward scheme combines Marton coding for single-hop broadcast channels and partial decode-forward for relay channels by Cover and El Gamal. For the N-node Gaussian broadcast relay network, the scheme achieves within 0.5N bits from the capacity region, extending and refining a recent result by Kannan, Raja, and Viswanath. The main idea of the scheme is to precode all the codewords initially at the source and to decode and forward parts of them on the fly at the relays.

The recently developed noisy network coding naturally extends compress-forward coding for the relay channel by Cover and El Gamal to arbitrary relay networks. In particular, the noisy network coding scheme achieves the best known capacity lower bound for general Gaussian networks. Motivated by the recent development of noisy network coding, we propose a novel extension of noisy network coding specialized for the fading parallel relay network. In the new scheme, the relay observation is opportunistically compressed by adapting on the local channel state information of the source-relay link. More specifically, each relay node opportunistically compresses the collection of output symbols with channel gains above a certain threshold, and forwards the digital compression to the destination node using independent Gaussian codes. To present the potential of the new scheme, we focus on the symmetric setting in which the channel coefficients within each hop are identically and independently distributed. We show that in the large number of relays regime, our scheme achieves the capacity while outperforming other schemes such as amplify-forward and decode-forward. Our result demonstrates that adaptation using channel state information at the receiver side can be beneficial.

Motivated by the recently developed hybrid coding scheme for joint source-channel coding, this paper proposes a new coding scheme for noisy relay networks. The proposed coding scheme operates in a similar manner to the noisy network coding scheme, except that each relay node uses the hybrid coding interface to transmit a symbol-by-symbol function of the received sequence and its quantized version. This coding scheme unifies both amplify-forward and noisy network coding and can strictly outperform both. The potential of the hybrid coding interface for relaying is demonstrated through the diamond relay network and two-way relay channel examples.

A new analog-digital hybrid coding architecture for joint source-channel coding is proposed. The encoder generates a channel input by a symbol-by-symbol mapping of the observed (analog) source and its (digital) compression codeword, while the decoder reconstructs the source by a symbol-by-symbol mapping of the (analog) channel output and the decoded (digital) compression codeword from it. When applied to the problem of lossy communication of sources over the two-user discrete memoryless interference channel, this hybrid coding scheme achieves the best known performance and recovers as special cases several previous results on lossless and lossy communication over single hop networks.

A noisy network coding scheme for communicating messages between multiple sources and destinations over a general noisy network is presented. For multi-message multicast networks, the scheme naturally generalizes network coding over noiseless networks by Ahlswede, Cai, Li, and Yeung, and compress-forward coding for the relay channel by Cover and El Gamal to discrete memoryless and Gaussian networks. The scheme also extends the results on coding for wireless relay networks and deterministic networks by Avestimehr, Diggavi, and Tse, and coding for wireless erasure networks by Dana, Gowaikar, Palanki, Hassibi, and Effros. The scheme involves lossy compression by the relay as in the compress-forward coding scheme for the relay channel. However, unlike previous compress-forward schemes in which independent messages are sent over multiple blocks, the same message is sent multiple times using independent codebooks as in the network coding scheme for cyclic networks. Furthermore, the relays do not use Wyner-Ziv binning as in previous compress-forward schemes, and each decoder performs simultaneous decoding of the received signals from all the blocks without uniquely decoding the compression indices. A consequence of this new scheme is that achievability is proved simply and more generally without resorting to time expansion to extend results for acyclic networks to networks with cycles. The noisy network coding scheme is then extended to general multi-message networks by combining it with decoding techniques for the interference channel. For the Gaussian multicast network, noisy network coding improves the previously established gap to the cutset bound. We also demonstrate through two popular Gaussian network examples that noisy network coding can outperform conventional compress-forward, amplify-forward, and hash-forward coding schemes.

Noisy network coding unifies network coding by Ahlswede, Cai, Li, and Yeung for noiseless networks and compress-forward by Cover and El Gamal for noisy relay channels. In particular, it achieves the best known capacity inner bounds for multi-source multicast networks including deterministic networks by Avestimehr, Diggavi, and Tse and erasure networks by Dana, Gowaikar, Palanki, Hassibi, and Effros. This paper extends noisy network coding for multicast networks to networks with general message demand by combining the underlying noisy network coding scheme with decoding techniques for interference channels. At one extreme, noisy network coding is combined with simultaneous decoding, while at the other extreme interference is treated as noise. The potential of noisy network coding as a canonical building block for wireless networks is demonstrated via three examples of Gaussian networks that have drawn recent attentions.

In this letter, we implement a simplified version of the Cover van der Meulen Hajek Pursley (CMHP) coding originally characterized by Wajcer, Wiesel, and Shamai. The vector Gaussian broadcast channel with imperfect channel state information at the transmitter (CSIT) is considered where the transmitter only knows the channel mean and variance. Our focus is on the implementation and performance analysis of CMHP under the imperfect CSIT model using practical codes. Turbo codes described in IEEE 802.20 draft specification and quadrature amplitude modulation are used to implement CMHP. In order to find the optimal power allocation and beamforming vectors which maximize the sum rate with practical codes, we introduce the SINR penalty factor. The SNRs that achieve various target spectral efficiency are presented and analyzed.

Motivated by fading channels and erasure channels, the problem of reliable communication over deterministic relay networks is studied, in which relay nodes receive a function of the incoming signals and a random network state. An achievable rate is characterized for the case in which destination nodes have full knowledge of the state information. If the relay nodes receive a linear function of the incoming signals and the state in a finite field, then the achievable rate is shown to be optimal, meeting the cut-set upper bound on the capacity. This result generalizes on a unified framework the work of Avestimehr, Diggavi, and Tse on the deterministic networks with state dependency, the work of Dana, Gowaikar, Palanki, Hassibi, and Effros on linear erasure networks with interference, and the work of Smith and Vishwanath on linear erasure networks with broadcast.

Presented in this paper is a study of the capacity evaluation of various multiuser MIMO schemes in cellular environments. The throughputs per user of the generalized zero-forcing with rank adaptation and vector perturbation schemes are compared with the capacity bound of the Gaussian MIMO broadcast channel, obtained by dirty paper coding under proportional fairness scheduling. The average cell throughputs of these schemes are also compared. From these comparisons, this study provides vital information for applying multiuser MIMO schemes in multicell environments