The fundamental communication problem in the wireless Internet-of-Things (IoT) is to discover a massive number of devices and to provide them with reliable access to shared channels. Oftentimes these devices transmit short messages randomly and sporadically. This paper proposes a novel signaling scheme for grant-free massive access, where each device encodes its identity and/or information in a sparse set of tones. Such transmissions are implemented in the form of orthogonal frequency-division multiple access (OFDMA). Under some mild conditions and assuming device delays to be bounded unknown multiples of sampling intervals, sparse OFDMA is proved to enable arbitrarily reliable asynchronous device identification and message decoding with a codelength that is$O(K(\log K+\log S + \log N))$, where$N$denotes the device population,$K$denotes the actual number of active devices, and$\log S$is essentially equal to the number of information bits each device can send. The computational complexity for discovery and decoding can be made to be$O(K(\log K)(\log K+\log S+\log N)+K^{2}\log K)$. As a proof of concept, a specific design is proposed to identify up to 200 active devices out of$N=2^{96}$possible devices with up to 20 samples of delay, moderate signal-to-noise ratios, and fading. If the device population is$N=2^{48}$instead, each active device can also transmit 48 bits to the access point at the same time. The codelength compares much more favorably with those of standard slotted ALOHA and carrier-sensing multiple access (CSMA) schemes.

In massive multiple-input multiple-output (MIMO) systems, acquisition of the channel state information at the transmitter side (CSIT) is crucial. In this paper, a practical CSIT estimation scheme is proposed for frequency division duplexing (FDD) massive MIMO systems. Specifically, each received pilot symbol is first quantized to one bit per dimension at the receiver side and then the quantized bits are fed back to the transmitter. A joint one-bit compressed sensing algorithm is implemented at the transmitter to recover the channel matrices. The algorithm leverages the hidden joint sparsity structure in the user channel matrices to minimize the training and feedback overhead, which is considered to be a major challenge for FDD systems. Moreover, the one-bit compressed sensing algorithm accurately recovers the channel directions for beamforming. The one-bit feedback mechanism can be implemented in practical systems using the uplink control channel. Simulation results show that the proposed scheme nearly achieves the maximum output signal-to-noise-ratio for beamforming based on the estimated CSIT.

Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to infinity. This paper proposes a new paradigm, referred to as many-user information theory, where the number of users is allowed to grow with the blocklength. This paradigm is motivated by emerging systems with a massive number of users in an area, such as the Internet of Things. The focus of this paper is the many-access channel model, which consists of a single receiver and many transmitters, whose number increases unboundedly with the blocklength. Moreover, an unknown subset of transmitters may transmit in a given block and need to be identified as well as decoded by the receiver. A new notion of capacity is introduced and characterized for the Gaussian many-access channel with random user activities. The capacity can be achieved by first detecting the set of active users and then decoding their messages. The minimum cost of identifying the active users is also quantified.

Compressive sensing aims to recover a high-dimensional sparse signal from a relatively small number of measurements. In this paper, a novel design of the measurement matrix is proposed. The design is inspired by the construction of generalized low-density parity-check codes, where the capacity-achieving point-to-point codes serve as subcodes to robustly estimate the signal support. In the case that each entry of the n-dimensional ft-sparse signal lies in a known discrete alphabet, the proposed scheme requires only O(k log n) measurements and arithmetic operations. In the case of arbitrary, possibly continuous alphabet, an error propagation graph is proposed to characterize the residual estimation error. With O(k log2 n) measurements and computational complexity, the reconstruction error can be made arbitrarily small with high probability.

In this paper, we propose algorithms for computing Walsh-Hadamard transform with arbitrary K-sparse support. When K is sublinear in the dimension N of the time-domain signal, the algorithms achieve vanishing error probability as K increases without bound and involve sublinear computational complexity. Specifically, under the noiseless setting, an algorithm based on random hashing and successive cancellation is proposed, where O (K log K log N/K) operations on O(K log N/K) samples of the signal suffice. Under the noisy setting, a fast algorithm using the same framework is also proposed, which needs O (K log3K log N/K) operations and O (K log2K log N/K) samples. The latter algorithm reduces the complexity from superlinear in existing work to sublinear. The enabling idea is to relate the random hashing design to coding over a binary symmetric channel or a binary-input additive white Gaussian noise channel, whose quality depends on the noise level of the observations. Such inherent connection allows us to leverage well-established capacity-approaching codes to obtain the transform-domain signal with sublinear complexity.

Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to infinity. Motivated by emerging systems with a massive number of users, this paper studies the new many-user paradigm, where the number of users is allowed to grow with the blocklength. The focus of this paper is the degraded many-broadcast channel model, whose number of users may grow as fast as linearly with the blocklength. A notion of capacity in terms of message length is defined and an example of Gaussian degraded many-broadcast channel is studied. In addition, a numerical example for the Gaussian degraded many-broadcast channel with fixed transmit power constraint is solved, where every user achieves strictly positive message length asymptotically.

Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to infinity. This work proposes a new paradigm, referred to as many-user information theory, where the number of users is allowed to grow with the blocklength. This paradigm is motivated by emerging systems with a massive number of users in an area, such as machine-to-machine communication systems and sensor networks. The focus of the current paper is the many-access channel model, which consists of a single receiver and many transmitters, whose number increases unboundedly with the blocklength. Moreover, an unknown subset of transmitters may transmit in a given block and need to be identified. A new notion of capacity is introduced and characterized for the Gaussian many-access channel with random user activities. The capacity can be achieved by first detecting the set of active users and then decoding their messages.

A nationwide interoperable public safety broadband network is being planned by the United States government. The network will be based on long term evolution (LTE) standards and use recently designated spectrum in the 700 MHz band. The public safety network has different objectives and traffic patterns than commercial wireless networks. In particular, the public safety network puts more emphasis on coverage, reliability and latency in the worst case scenario. Moreover, the routine public safety traffic is relatively light, whereas when a major incident occurs, the traffic demand at the incident scene can be significantly heavier than that in a commercial network. Hence it is prohibitively costly to build the public safety network using conventional cellular network architecture consisting of an infrastructure of stationary base transceiver stations. A novel architecture is proposed in this paper for the public safety broadband network. The architecture deploys stationary base stations sparsely to serve light routine traffic and dispatches mobile base stations to incident scenes along with public safety personnel to support heavy traffic. The analysis shows that the proposed architecture can potentially offer more than 75% reduction in terms of the total number of base stations needed.

This paper studies communication networks with a very large number of users simultaneously communicating with an access point. A new notion of many-access channel (MnAC) is introduced, which is the same as a multiaccess channel except that the number of users increases unboundedly with the coding block length. Unlike the conventional multiaccess channel with a fixed number of users, the joint typicality decoding technique is not directly applicable to establish the achievability of the capacity. It is shown that, as long as the number of users grows sublinearly with the coding block length, random coding with Feinstein's threshold decoding is sufficient to achieve the symmetric capacity of the Gaussian MnAC.

In this study, the authors propose a systematic method of designing low-density parity-check codes with deterministic unequal-error-protection characteristics over an additive white Gaussian noise channel. The proposed method optimises the code rate or the noise threshold while at the same time guaranteeing that different classes of code bits can achieve their respective target error probabilities within a specified number of decoding iterations. The authors also derive the necessary and sufficient stability condition for the optimisation method.

In an opportunistic relaying mechanism, the "best" relay is selected to forward a received signal to the destination. In this paper, we propose a novel criterion to choose the "best" relay, namely the max-generalized-mean (MGM) selection criterion, which encompasses the max-min and the max-harmonic-mean (MHM) selection criteria. We then analyze the asymptotic outage probability of the selection decode-and-forward opportunistic relaying (SDF-OR) protocol under the proposed MGM selection criterion. Based on the asymptotic results, we show the conditions under which the MGM selection framework provides the full diversity. We further optimize the asymptotic outage probability by adjusting the parameters associated with the MGM selection criterion. We show that at high signal-to-noise-ratio (SNR), a lower outage probability can be accomplished by the MGM selection criterion compared with the max-min or the MHM selection criterion for the SDF-OR protocol.

In this letter, we consider a cooperative communication network over Rayleigh fading channels. We assume that the amplify-and-forward (AF) and the selection decode-and-forward (SDF) opportunistic relaying methods are used and that the relay with the largest harmonic mean of the source-relay channel gain and the relay-destination channel gain is selected for forwarding the message to the destination. We derive the high-SNR outage probabilities, which are then compared with the simulation results. We also compare our analytical results with the high-SNR outage probabilities found when the max–min criterion is used to select the opportunistic relay.