In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas N and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than N, the maximal multiplexing gain can be achieved with many different transmission reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and or to enable interference-aware receive combining. In this paper, we try to answer the question if the N data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user-the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization.
The authors of @cite_21 @cite_0 arrive at a different conclusion when they compare BD (which selects @math users and sends @math streams user) and ZFC (which selects @math users and sends one stream user) under quantized CSI. Their simulations reveal a distinct advantage of BD (i.e., multi-stream multiplexing), but are limited to uncorrelated channels and neither include user selection nor interference rejection. We show that their results are misleading, because single-user transmission greatly outperforms both BD and ZFC in the scenario that they simulate.
@cite_0 @cite_21
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In this paper, we study resource allocation and relay selection in a decode-and-forward downlink OFDMA cooperative network assuming imperfect channel state information (CSI) at the base station (BS). We assume that due to feedback delay, the BS has only outdated CSI of BS to users and inter-user links. We propose a centralized optimization framework in which the BS takes decisions on the basis of available outdated CSI given the conditional probability distribution of the current CSI. We approach the problem by using dual optimization framework, decompose it into per-subcarrier optimization problem, and develop an optimal resource allocation and relay selection algorithm. The proposed algorithm minimizes the total transmit power of the system by jointly selecting the best relay node for each source-destination pair, and optimally allocating power and subcarriers to the source and relay nodes under constraints on users' conditional expected data rates. The duality gaps of the solution are virtually zero. Furthermore, we show that the per-subcarrier decomposition of the problem reduces the complexity of the solution from exponential to polynomial. Simulation results show that the system performance may be badly affected by feedback delay. Simulation results also show that users' cooperation significantly reduces the power consumption compared to non-cooperative communication. We consider the content delivery problem in a fading multi-input single-output channel with cache-aided users. We are interested in the scalability of the equivalent content delivery rate when the number of users, @math , is large. Analytical results show that, using coded caching and wireless multicasting, without channel state information at the transmitter, linear scaling of the content delivery rate with respect to @math can be achieved in some different ways. First, if the multicast transmission spans over @math independent sub-channels, e.g., in quasi-static fading if @math , and in block fading or multi-carrier systems if @math , linear scaling can be obtained, when the product of the number of transmit antennas and the number of sub-channels scales logarithmically with @math . Second, even with a fixed number of antennas, we can achieve the linear scaling with a threshold-based user selection requiring only one-bit feedbacks from the users. When CSIT is available, we propose a mixed strategy that combines spatial multiplexing and multicasting. Numerical results show that, by optimizing the power split between spatial multiplexing and multicasting, we can achieve a significant gain of the content delivery rate with moderate cache size. A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding. We completely remove this overhead by a deep-learning based channel extrapolation (or "prediction") approach and demonstrate that a neural network (NN) at the BS can infer the DL CSI centered around a frequency @math by solely observing uplink (UL) CSI on a different, yet adjacent frequency band around @math ; no more pilot reporting overhead is needed than with a genuine time division duplex (TDD)-based system. The rationale is that scatterers and the large-scale propagation environment are sufficiently similar to allow a NN to learn about the physical connections and constraints between two neighboring frequency bands, and thus provide a well-operating system even when classic extrapolation methods, like the Wiener filter (used as a baseline for comparison throughout) fails. We study its performance for various state-of-the-art Massive MIMO channel models, and, even more so, evaluate the scheme using actual Massive MIMO channel measurements, rendering it to be practically feasible at negligible loss in spectral efficiency when compared to a genuine TDD-based system. In this paper, we investigate secure transmission in untrusted amplify-and-forward half-duplex relaying networks with the help of cooperative jamming at the destination (CJD). Under the assumption of full channel state information (CSI), conventional CJD using self-interference cancelation at the destination is efficient when the untrusted relay has no capability to suppress the jamming signal. However, if the source and destination are equipped with a single antenna and the only untrusted relay is equipped with N multiple antennas, it can remove the jamming signal from the received signal by linear filters and the full multiplexing gain of relaying cannot be achievable with the conventional CJD due to the saturation of the secrecy rate at the high transmit power regime. We propose in this paper new CJD scheme where neither destination nor relay can acquire CSI of relay-destination link. Our proposed scheme utilizes zero-forcing cancelation based on known jamming signals instead of self-interference subtraction, while the untrusted relay cannot suppress the jamming signals due to the lack of CSI. We show that the secrecy rate of the proposed scheme can enjoy a half of multiplexing gain in half-duplex relaying while that of conventional CJD is saturated at high transmit power for N ≥2. The impact of channel estimation error at the destination is also investigated to show the robustness of the proposed scheme against strong estimation errors.