This paper considers sparse device activity detection for cellular machine-type communications with non-orthogonal signatures using the approximate message passing algorithm. This paper compares two network architectures, massive multiple-input-multiple-output (MIMO) and cooperative MIMO, in terms of their effectiveness in overcoming inter-cell interference. In the massive MIMO architecture, each base station (BS) detects only the users from its own cell while treating inter-cell interference as noise. In the cooperative MIMO architecture, each BS detects the users from neighboring cells as well; the detection results are then forwarded in the form of a log-likelihood ratio (LLR) to a central unit where final decisions are made. This paper analytically characterizes the probabilities of false alarm and missed detection for both architectures. The numerical results validate the analytic characterization and show that as the number of antennas increases, a massive MIMO system effectively drives the detection error to zero, while as the cooperation size increases, the cooperative MIMO architecture mainly improves the cell-edge user performance. Moreover, this paper studies the effect of LLR quantization to account for the finite-capacity fronthaul. The numerical simulations of a practical scenario suggest that in specific case cooperating three BSs in a cooperative MIMO system achieves about the same cell-edge detection reliability as a non-cooperative massive MIMO system with four times the number of antennas per BS.
Besides the aforementioned works on design and analysis for practical networks, there are also related works @cite_17 @cite_6 @cite_22 @cite_16 that address the massive random access problem with sparse user activity from information theoretical perspectives.
@cite_16 @cite_22 @cite_6 @cite_17
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We consider an uncoordinated Gaussian multiple access channel with a relatively large number of active users within each block. A low complexity coding scheme is proposed, which is based on a combination of compute-and-forward and coding for a binary adder channel. For a wide regime of parameters of practical interest, the energy-per-bit required by each user in the proposed scheme is significantly smaller than that required by popular solutions such as slotted-ALOHA and treating interference as noise. This paper discusses the contemporary problem of providing multiple-access (MAC) to a massive number of uncoordinated users. First, we define a random-access code for Ka-user Gaussian MAC to be a collection of norm-constrained vectors such that the noisy sum of any K a of them can be decoded with a given (suitably defined) probability of error. An achievability bound for such codes is proposed and compared against popular practical solutions: ALOHA, coded slotted ALOHA, CDMA, and treating interference as noise. It is found out that as the number of users increases existing solutions become vastly energy-inefficient. Second, we discuss the asymptotic (in blocklength) problem of coding for a K-user Gaussian MAC when K is proportional to blocklength and each user's payload is fixed. It is discovered that the energy-per-bit vs. spectral efficiency exhibits a rather curious tradeoff in this case. This paper aims to provide an information theoretical analysis of massive device connectivity scenario in which a large number of devices with sporadic traffic communicate in the uplink to a base-station (BS). In each coherence time interval, the BS needs to identify the active devices, to estimate their channels, and to decode the transmitted messages from the devices. This paper first derives an information theoretic upper bound on the overall transmission rate. We then provide a degree-of-freedom (DoF) analysis that illustrates the cost of device identification for massive connectivity. We show that the optimal number of active devices is strictly less than half of the coherence time slots, and the achievable DoF decreases linearly with the number of active devices when it exceeds the number of receive antennas. This paper further presents a two-phase practical framework in which device identification and channel estimation are performed jointly using compressed sensing techniques in the first phase, with data transmission taking place in the second phase. We outline the opportunities in utilizing compressed sensing results to analyze the performance of the overall framework and to optimize the system parameters. 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.