In this paper, we examine the benefits of multiple antenna communication in random wireless networks, the topology of which is modeled by stochastic geometry. The setting is that of the Poisson bipolar model introduced in [1], which is a natural model for ad-hoc and device-to-device (D2D) networks. The primary finding is that, with knowledge of channel state information between a receiver and its associated transmitter, by zero-forcing successive interference cancellation, and for appropriate antenna configurations, the ergodic spectral efficiency can be made to scale linearly with both 1) the minimum of the number of transmit and receive antennas, 2) the density of nodes and 3) the path-loss exponent. This linear gain is achieved by using the transmit antennas to send multiple data streams (e.g. through an open-loop transmission method) and by exploiting the receive antennas to cancel interference. Furthermore, when a receiver is able to learn channel state information from a certain number of near interferers, higher scaling gains can be achieved when using a successive interference cancellation method. A major implication of the derived scaling laws is that spatial multiplexing transmission methods are essential for obtaining better and eventually optimal scaling laws in multiple antenna random wireless networks. Simulation results support this analysis.
There has been extensive work on the capacity of MIMO-MANETs. MIMO-MANETs can be modeled as MIMO interference networks in which a finite number of transmit-and-receiver pairs communicate by sharing the same spectrum, without transmitter cooperation. @cite_29 studied the capacity of a MIMO-MANET by treating inter-node interference as additional noise at a receiver, and derived the optimal power allocation strategy for the MIMO transmission. For instance, in a certain range of interference-to-noise ratios, it turns out that allocating the whole power to one antenna (i.e., using a single stream transmission) is optimal. @cite_26 and @cite_16 extended the result of @cite_29 , and demonstrated that the asymptotic spectral efficiency is improved by sending multiple data streams. A common assumption of these studies is that the distances between any two nodes in the network are deterministic @cite_29 or identical @cite_26 , which is unrealistic to model MANETs in practice. This approach cannot be used to assess which MIMO transmission techniques provide the highest gains in large random MANETs.
@cite_29 @cite_26 @cite_16
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Large multiple-input multiple-output (MIMO) networks promise high energy efficiency, i.e., much less power is required to achieve the same capacity compared to the conventional MIMO networks if perfect channel state information (CSI) is available at the transmitter. However, in such networks, huge overhead is required to obtain full CSI especially for Frequency-Division Duplex (FDD) systems. To reduce overhead, we propose a downlink antenna selection scheme, which selects S antennas from M > S transmit antennas based on the large scale fading to serve K ≤ S users in large distributed MIMO networks employing regularized zero-forcing (RZF) precoding. In particular, we study the joint optimization of antenna selection, regularization factor, and power allocation to maximize the average weighted sum-rate. This is a mixed combinatorial and non-convex problem whose objective and constraints have no closed-form expressions. We apply random matrix theory to derive asymptotically accurate expressions for the objective and constraints. As such, the joint optimization problem is decomposed into subproblems, each of which is solved by an efficient algorithm. In addition, we derive structural solutions for some special cases and show that the capacity of very large distributed MIMO networks scales as O(KlogM) when M→∞ with K, S fixed. Simulations show that the proposed scheme achieves significant performance gain over various baselines. We consider n wireless ad hoc network nodes with one antenna each and equidistantly placed on a line. The transmission power of each node is just large enough to reach its next neighbor. For this setting we show that a message can be broadcasted to all nodes in time O(log n) without increasing each node's transmission power. Our algorithm needs O(log n) messages and consumes a total energy which is only a constant factor larger than the standard approach where nodes sequentially transmit the broadcast message to their next neighbors. We obtain this by synchronizing the nodes on the fly and using MIMO (multiple input multiple output) techniques. To achieve this goal we analyze the communication capacity of multiple antennas positioned on a line and use a communication model which is based on electromagnetic fields in free space. We extend existing communication models which either reflect only the sender power or neglect the locations by concentrating only on the channel matrix. Here, we compute the scalar channel matrix from the locations of the antennas and thereby only consider line-of-sight-communication without obstacles, reflections, diffractions or scattering. First, we show that this communication model reduces to the SINR power model if the antennas are uncoordinated. We show that n coordinated antennas can send a signal which is n times more powerful than the sum of their transmission powers. Alternatively, the power can be reduced to an arbitrarily small polynomial with respect to the distance. For coordinated antennas we show how the well-known power gain for MISO (multiple input single output) and SIMO (single input multiple output) can be described in this model. Furthermore, we analyze the channel matrix and prove that in the free space model no diversity gain can be expected for MIMO. Finally, we present the logarithmic time broadcast algorithm which takes advantage of the MISO power gain by self-coordinating wireless nodes. Multiple-input-multiple-output (MIMO) communication systems can provide large capacity gains over traditional single-input-single-output (SISO) systems and are expected to be a core technology of next generation wireless systems. Often, these capacity gains are achievable only with some form of adaptive transmission. In this paper, we study the capacity loss (defined as the rate loss in bits s Hz) of the MIMO wireless system when the covariance matrix of the transmitted signal vector is designed using a low rate feedback channel. For the MIMO channel, we find a bound on the ergodic capacity loss when random codebooks, generated from the uniform distribution on the complex unit sphere, are used to convey the second order statistics of the transmitted signal from the receiver to the transmitter. In this case, we find a closed-form expression for the ergodic capacity loss as a function of the number of bits fed back at each channel realization. These results show that the capacity loss decreases at least as O(2 sup -B (2MMt-2) ) where B is the number of feedback bits, M sub t is the number of transmit antennas, and M=min M sub r ,M sub t where M sub r is the number of receive antennas. In the high SNR regime, we present a new bound on the capacity loss that is tighter than the previously derived bound and show that the capacity loss decreases exponentially as a function of the number of feedback bits. We study in this paper the network spectral efficiency of a multiple-input multiple-output (MIMO) ad hoc network with K simultaneous communicating transmitter-receiver pairs. Assuming that each transmitter is equipped with t antennas and each receiver with r antennas and each receiver implements single-user detection, we show that in the absence of channel state information (CSI) at the transmitters, the asymptotic network spectral efficiency is limited by r nats s Hz as Krarrinfin and is independent of t and the transmit power. With CSI corresponding to the intended receiver available at the transmitter, we demonstrate that the asymptotic spectral efficiency is at least t+r+2radictr nats s Hz. Asymptotically optimum signaling is also derived under the same CSI assumption, i.e., each transmitter knows the channel corresponding to its desired receiver only. Further capacity improvement is possible with stronger CSI assumption; we demonstrate this using a heuristic interference suppression transmit beamforming approach. The conventional orthogonal transmission approach is also analyzed. In particular, we show that with idealized medium access control, the channelized transmission has unbounded asymptotic spectral efficiency under the constant per-user power constraint. The impact of different power constraints on the asymptotic spectral efficiency is also carefully examined. Finally, numerical examples are given that confirm our analysis