We propose an enhanced precision channel state acquisition scheme for downlink Multi-User MIMO (MU-MIMO) transmission with a large number of transmit antennas at the base station. The proposed scheme is an attempt to harvest the Channel State Information (CSI) at the transmitter by incrementally improving the precision of the channel knowledge. We show how, in an LTE-Advanced system, CSI harvesting mechanism followed by the conventional Zero-Forcing Beam-Forming (ZFBF) results in improved MU-MIMO performance, relative to the current method for acquiring CSI at the transmitter. The proposed progressive CSI harvesting scheme is also shown to yield significant saving in terms of feedback overhead. The implications of the larger MIMO systems and higher MU-MIMO ranks, and sensitivity to higher mobility are also discussed.

Next-generation wireless communications systems are expected to exploit frequency bands above 6 GHz. An important transition towards such bands will be design of channel models capable of supporting the design of efficient air-interface and networks. In this paper, we describe a geometry-based statistical channel model and apply ray-tracing simulation for its validation. Focusing on path loss and root-mean-square (RMS) delay spread as metrics, we show that the proposed modeling approach is flexible and realistic.

We consider downlink multi-user MIMO transmission of a wireless system in which the base station is equipped with a large number of antennas. We propose two downlink schemes based on random beamforming and subspace tracking and show that while reducing signaling overhead, these schemes can improve the system performance compared to conventional systems with a smaller number of antennas at the base station.

Full Duplex or Simultaneous transmission and reception (STR) in the same frequency at the same time can potentially double the physical layer capacity. However, high power transmit signal will appear at receive chain as echoes with powers much higher than the desired received signal. Therefore, in order to achieve the potential gain, it is imperative to cancel these echoes. As these high power echoes can saturate low noise amplifier (LNA) and also digital domain echo cancellation requires unrealistically high resolution analog-to-digital converter (ADC), the echoes should be cancelled or suppressed sufficiently before LNA. In this paper we present a closed-loop echo cancellation technique which can be implemented purely in analogue domain. The advantages of our method are multiple-fold: it is robust to phase noise, does not require additional set of antennas, can be applied to wideband signals and the performance is irrelevant to radio frequency (RF) impairments in transmit chain. Next, we study a few protocols for STR systems in carrier sense multiple access (CSMA) network and investigate MAC level throughput with realistic assumptions in both single cell and multiple cells. We show that STR can reduce hidden node problem in CSMA network and produce gains of up to 279% in maximum throughput in such networks. Moreover, at high traffic load, the gain of STR system can be tremendously large since the throughput of non-STR system is close to zero at heavy traffic due to severe collisions. Finally, we investigate the application of STR in cellular systems and study two new unique interferences introduced to the system due to STR, namely BS-BS interference and UE-UE interference. We show that these two new interferences will hugely degrade system performance if not treated appropriately. We propose novel methods to reduce both interferences and investigate the performances in system level. We show that BS-BS interference can be suppressed sufficiently enough to be less than thermal noise power, and with favorable UE-UE channel model, capacities close to double are observed both in downlink (DL) and uplink (UL). When UE-UE interference is larger than DL co-channel interferences, we propose a simple and "non-cooperative" technique in order to reduce UE-UE interference.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes of the next-generation of cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users according to their channels vector directions and SINR levels. However, when scheduling is applied independently in each cell, the inter-cell interference (ICI) power at each user receiver is not known in advance since it changes at each new scheduling slot depending on the scheduling decisions of all interfering base stations. In order to cope with this uncertainty, we consider the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We develop a game-theoretic framework for this problem and build on stochastic optimization techniques in order to find optimal scheduling and ARQ schemes. Particularizing our framework to the case of "outage service rates", we obtain a scheme based on adaptive variable-rate coding at the physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then, we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ) that is able to achieve a throughput performance arbitrarily close to the "genie-aided service rates", with no need for a genie that provides non-causally the ICI power levels. The novel HARQ scheme is both easier to implement and superior in performance with respect to the conventional combination of adaptive variable-rate coding and ARQ-LLC.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes foreseen in the next-generation cellular systems (e.g., LTE-Advanced). Scheduling exploits the knowledge of the users' instantaneous achievable coding rates. However, when it is applied independently in each cell, the level of inter-cell interference (ICI) experienced by each user is not known a priori at the cell scheduler as it depends on the local scheduling decisions at all other base stations. As a result, the instantaneous achievable rates are random variables, even under the optimistic assumption of perfect transmitter channel state information. In order to cope with this problem, we consider the joint design and operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We present a systematic method for joint scheduler/ARQ design and show that properly designed incremental-redundancy hybrid ARQ schemes can achieve (with finite average decoding delay) any desired fraction of the "optimistic" throughputs, i.e., of the throughputs that would be achieved if the transmitter knew the instantaneous user ICI and transmitted to each scheduled user at the maximum possible instantaneous rate.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes of the next-generation of cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users according to their channels vector directions and channel quality indicator (CQI). However, when scheduling is applied independently in each cell, the inter-cell interference (ICI) power at each user receiver is random and not known in advance since it changes from slot to slot depending on the scheduling decisions of all interfering base stations. In order to cope with this uncertainty, we consider the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We develop a game-theoretic framework for this multi-cell decentralized scheduling problem for which we prove existence of Nash equilibria, and we build on stochastic optimization techniques in order to devise good joint scheduling and ARQ schemes. We also find “extremal” ICI power statistics that provide uniform lower and upper bounds to the values of the network utility function at all Nash equilibria of the decentralized scheduling game.

Downlink scheduling schemes are well-known and widely investigated under the assumption that the channel state is perfectly known to the scheduler. In the multiuser MIMO (broadcast) case, downlink scheduling in the presence of non-perfect channel state information (CSI) is only scantly treated. In this paper we provide a general framework that addresses the problem systematically. Also, we illuminate the key role played by the channel state prediction error: our scheme treats in a fundamentally different way users with small channel prediction error ("predictable" users) and users with large channel prediction error ("non-predictable" users), and can be interpreted as a near-optimal opportunistic time-sharing strategy between MIMO downlink beamforming to predictable users and space-time coding to non-predictable users. Our results, based on a realistic MIMO channel model used in 3GPP standardization, show that the proposed algorithms can significantly outperform a conventional "mismatched" scheduling scheme that treats the available CSI as if it was perfect.

Channel state feedback schemes for the MIMO broadcast downlink have been widely studied in the frequency flat case. This work focuses on the more relevant frequency selective case, where some important new aspects emerge. We consider a MIMO-OFDM broadcast channel and compare achievable ergodic rates under three channel state feedback schemes: analog feedback, direction quantized feedback and "time-domain" channel quantized feedback. The first two schemes are direct extensions of previously proposed schemes. The third scheme is novel, and it is directly inspired by rate-distortion theory of Gaussian correlated sources. For each scheme we derive the conditions under which the system achieves full multiplexing gain. The key difference with respect to the widely treated frequency-flat case is that in MIMO-OFDM the frequency domain channel transfer function is a Gaussian correlated source. The new time-domain quantization scheme takes advantage of the channel frequency correlation structure and outperforms the other schemes. Furthermore, it is by far simpler to implement than complicated spherical vector quantization. In particular, we observe that no structured codebook design and vector quantization is actually needed for efficient channel state information feedback.