In this paper, we study the sum rate performance of zero-forcing (ZF) and regularized ZF (RZF) precoding in large MISO broadcast systems under the assumptions of imperfect channel state information at the transmitter and per-user channel transmit correlation. Our analysis assumes that the number of transmit antennas M and the number of single-antenna users K are large while their ratio remains bounded. We derive deterministic approximations of the empirical signal-tointerference plus noise ratio (SINR) at the receivers, which are tight as M, K → ∞. In the course of this derivation, the per-user channel correlation model requires the development of a novel deterministic equivalent of the empirical Stieltjes transform of large dimensional random matrices with generalized variance profile. The deterministic SINR approximations enable us to solve various practical optimization problems. Under sum rate maximization, we derive (i) for RZF the optimal regularization parameter, (ii) for ZF the optimal number of users, (iii) for ZF and RZF the optimal power allocation scheme and (iv) the optimal amount of feedback in large FDD/TDD multi-user systems. Numerical simulations suggest that the deterministic approximations are accurate even for small M, K.

5G networks will be the first generation to benefit from location information that is sufficiently precise to be leveraged in wireless network design and optimization. We argue that location information can aid in addressing several of the key challenges in 5G, complementary to existing and planned technological developments. These challenges include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. This paper gives a broad overview of the growing research area of location-aware communications across different layers of the protocol stack. We highlight several promising trends, tradeoffs, and pitfalls. I. 5G: INTRODUCTION AND CHALLENGES 5 G will be characterized by a wide variety of use cases, as well as orders-of-magnitude increases in mobile data volume per area, number of connected devices, and typical user data rate, all compared to current mobile communication systems [1]. To cope with these demands, a number of challenges must be addressed before 5G can be successfully deployed. These include the demand for extremely high data rates and much lower latencies, potentially down to 1 ms end-to-end for certain applications [2]. Moreover, scalability and reduction of signaling overhead must be accounted for, as well as minimization of (total) energy consumption to enable affordable cost for network operation. To fulfill these requirements in 5G, network densification is key, calling for a variety of coordination and cooperation techniques between various kinds of network elements in an ultra-dense heterogeneous network. Moreover, by implementing sharing and coexistence approaches, along with new multi-GHz frequency bands, spectrum efficiency can be improved. An overview of a number of disruptive technologies for 5G is provided in [1]. It is our vision that context information in general, and location information in particular, can complement both traditional and disruptive technologies in addressing several of the challenges in 5G networks. While location information was available in previous generations of cellular mobile radio systems, e.g., cell-ID positioning in 2G, timing-based positioning using communication-relevant synchronization signals in 3G, and additionally dedicated positioning reference signals in 4G, accuracy ranged from hundreds to tens of meters, rendering position information insufficiently precise for some communications operations. In 5G, for the first time, a majority of

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We present a relative calibration method for a wireless TDD link, which, after a calibration phase involving feedback, lets the transmitter acquire knowledge of the downlink channel state from the uplink channel estimates, through proper modeling and estimation of the RF circuitry impulse responses. Contrarily to previous methods, relative calibration does not require specific calibration hardware. Experimental results confirm the validity of the proposed linear reciprocity model, and of the calibration approach.