We consider energy-efficient wireless resource management in cellular networks where BSs are equipped with energy harvesting devices, using statistical information for traffic intensity and harvested energy. The problem is formulated as adapting BSs
on-off states, active resource blocks (e.g. subcarriers) as well as power allocation to minimize the average grid power consumption in a given time period while satisfying the users quality of service (blocking probability) requirements. It is transformed into an unconstrained optimization problem to minimize a weighted sum of grid power consumption and blocking probability. A two-stage dynamic programming (DP) algorithm is then proposed to solve this optimization problem, by which the BSs on-off states are optimized in the first stage, and the active BSs resource blocks are allocated iteratively in the second stage. Compared with the optimal joint BSs on-off states and active resource blocks allocation algorithm, the proposed algorithm greatly reduces the computational complexity, while at the same time achieves close to the optimal energy saving performance.
A virtual multiple-input multiple-output (MIMO) wireless system using the receiver-side cooperation with the compress-and-forward (CF) protocol, is an alternative to a point-to-point MIMO system, when a single receiver is not equipped with multiple a
ntennas. It is evident that the practicality of CF cooperation will be greatly enhanced if an efficient source coding technique can be used at the relay. It is even more desirable that CF cooperation should not be unduly sensitive to carrier frequency offsets (CFOs). This paper presents a practical study of these two issues. Firstly, codebook designs of the Voronoi vector quantization (VQ) and the tree-structure vector quantization (TSVQ) to enable CF cooperation at the relay are described. A comparison in terms of the codebook design and encoding complexity is analyzed. It is shown that the TSVQ is much simpler to design and operate, and can achieve a favorable performance-complexity tradeoff. Furthermore, this paper demonstrates that CFO can lead to significant performance degradation for the virtual MIMO system. To overcome this, it is proposed to maintain clock synchronization and jointly estimate the CFO between the relay and the destination. This approach is shown to provide a significant performance improvement.
This paper presents a novel power spectral density estimation technique for band-limited, wide-sense stationary signals from sub-Nyquist sampled data. The technique employs multi-coset sampling and incorporates the advantages of compressed sensing (C
S) when the power spectrum is sparse, but applies to sparse and nonsparse power spectra alike. The estimates are consistent piecewise constant approximations whose resolutions (width of the piecewise constant segments) are controlled by the periodicity of the multi-coset sampling. We show that compressive estimates exhibit better tradeoffs among the estimators resolution, system complexity, and average sampling rate compared to their noncompressive counterparts. For suitable sampling patterns, noncompressive estimates are obtained as least squares solutions. Because of the non-negativity of power spectra, compressive estimates can be computed by seeking non-negative least squares solutions (provided appropriate sampling patterns exist) instead of using standard CS recovery algorithms. This flexibility suggests a reduction in computational overhead for systems estimating both sparse and nonsparse power spectra because one algorithm can be used to compute both compressive and noncompressive estimates.
In this letter the performance of multiple relay channels is analyzed for the situation in which multiple antennas are deployed only at the relays. The simple repetition-coded decodeand- forward protocol with two different antenna processing techniqu
es at the relays is investigated. The antenna combining techniques are maximum ratio combining (MRC) for reception and transmit beamforming (TB) for transmission. It is shown that these distributed antenna combining techniques can exploit the full spatial diversity of the relay channels regardless of the number of relays and antennas at each relay, and offer significant power gain over distributed space-time coding techniques.
